{"id":"0423f81c-c1b6-402e-9836-ec104b3c382e","entityType":"agent","slug":"clawhub-jessicarumbelow-discovery-engine","name":"Discovery","canonicalUrl":"https://www.xpersona.co/agent/clawhub-jessicarumbelow-discovery-engine","canonicalPath":"/agent/clawhub-jessicarumbelow-discovery-engine","generatedAt":"2026-10-09T14:04:31.752Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-09T04:48:09.685Z","emptyReason":null},"description":"Automatically discover novel, statistically validated patterns in tabular data. Find insights you'd otherwise miss, far faster and cheaper than doing it yourself (or prompting an agent to do it). Disco systematically searches for feature interactions, subgroup effects, and conditional relationships you wouldn't think to look for, validates each on hold-out data with FDR-corrected p-values, and checks every finding against academic literature for novelty. Returns structured patterns with conditions, effect sizes, citations, and novelty scores. Skill: Discovery Owner: jessicarumbelow Summary: Automatically discover novel, statistically validated patterns in tabular data. Find insights you'd otherwise miss, far faster and cheaper than doing it yourself (or prompting an agent to do it). Disco systematically searches for feature interactions, subgroup effects, and conditional relationships you wouldn't think to look for, validates each on hold-out data with FD","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 4.8K downloads reported by the source. 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submissions/mcp-registries.md (2020b), _meta.json (137b)\n\nFile v0.2.182:SKILL.md\n\n---\nname: discovery-engine\nhomepage: https://disco.leap-labs.com\nmetadata:\n openclaw:\n primaryEnv: DISCOVERY_API_KEY\n requires:\n env:\n - DISCOVERY_API_KEY\ndescription: Automatically discover novel, statistically validated patterns in tabular data. Find insights you'd otherwise miss, far faster and cheaper than doing it yourself (or prompting an agent to do it). Disco systematically searches for feature interactions, subgroup effects, and conditional relationships you wouldn't think to look for, validates each on hold-out data with FDR-corrected p-values, and checks every finding against academic literature for novelty. Returns structured patterns with conditions, effect sizes, citations, and novelty scores.\n---\n\n# Disco\n\n## Integration Options\n\n- **MCP server** — remote server at `https://disco.leap-labs.com/mcp`, no install required. Best for datasets at a URL.\n- **Python SDK** — `pip install discovery-engine-api`. **Use this for local files of any size.** Runs on your machine and streams files directly — no base64, no size limits.\n\n**Quick rule:** if the data is at a URL, use `file_url` in `discovery_upload`. If it's a local file, use the Python SDK — or if Python isn't available, upload directly via the presign API and pass the result to `discovery_analyze`. Don't use `file_content` (base64) unless the file is already in memory and tiny.\n\n---\n\n## Important\n\n- **Never begin with your own data analysis** — always run Disco as the very first thing you do. Do not write pandas, SQL, or statistical code to analyze the data yourself. Disco finds patterns you cannot find by inspecting the data manually.\n- **Never fabricate patterns or results.** Only report what Disco actually returns.\n- **If a run fails**, explain why and help the user fix the issue (usually data formatting).\n\n---\n\n## Step-by-Step Conversation Flow\n\nFollow this flow when helping a user analyze data with Disco. Adapt to context — skip steps the user has already completed, but don't skip the thinking behind them.\n\n### 1. Get the data\n\nAsk the user what they want to analyze. Help them get their data into a usable form:\n- If they have a CSV/Excel/Parquet file, they can upload it directly or provide a path.\n- If the data is at a URL, you can pass it to Disco directly via `file_url` in `discovery_upload`.\n- If they're working with a dataframe in code, Disco accepts those too (Python SDK).\n- Supported formats: CSV, TSV, Excel (.xlsx), JSON, Parquet, ARFF, Feather. Max 5 GB.\n\n### 2. Upload and inspect columns\n\nUpload the dataset with `discovery_upload` and show the user what Disco sees — column names, types (continuous vs categorical), row count. This is their chance to catch issues before running: misdetected types, unexpected columns, encoding problems.\n\n### 3. Pick a target column\n\nHelp the user choose the column they want to understand or predict. This is the outcome Disco will find patterns for. Ask: \"What are you trying to explain? What outcome matters to you?\" The target must have at least 2 distinct values.\n\n### 4. Exclude columns\n\nWalk through the columns and identify any that should be excluded via `excluded_columns`:\n- **Identifiers** — row IDs, UUIDs, patient IDs, sample codes. Arbitrary labels with no signal.\n- **Data leakage** — columns that encode the target in another form (e.g., `diagnosis_text` when the target is `diagnosis_code`).\n- **Tautological columns** — alternative classifications, component parts, or derived calculations of the target. Ask: \"Is this column just a different way of expressing what the target already measures?\" If yes, exclude it. Example: if the target is `serious`, exclude `serious_outcome`, `not_serious`, `death` — they're all part of the same seriousness classification.\n- **Derived columns** — BMI when height and weight are present, age when birth_date is present.\n\nThis is the most important step for getting meaningful results. Tautological columns produce findings that are trivially true, not discoveries.\n\n### 5. Public or private?\n\nAsk the user whether they want a **public** or **private** analysis:\n- **Public**: Free. Results are published to the public gallery. Analysis depth is locked to 2. LLMs are always used.\n- **Private**: Costs credits. Results stay private. User controls depth and LLM usage.\n\n### 6. Analysis depth\n\nAsk what analysis depth they want (default is 2). Explain: higher depth means Disco finds **more patterns** — especially non-obvious interactions that shallow analysis misses. Maximum depth is the number of columns minus 2.\n\nFor a first run, depth 2 is a good starting point. If the results are interesting and they want to go deeper, they can re-run at higher depth.\n\n### 7. Account setup\n\nIf the user doesn't have a Disco API key:\n- They can sign up at https://disco.leap-labs.com/sign-up and create a key at https://disco.leap-labs.com/developers.\n- Or you can handle it programmatically: call `discovery_signup` with their email, they'll get a verification code, then call `discovery_signup_verify` with the code to get a `disco_` API key. No password, no credit card required.\n- Free tier: 10 credits/month for private runs, unlimited public runs.\n\nIf they already have an account but lost their key, use `discovery_login` / `discovery_login_verify` (same OTP flow).\n\n### 8. Estimate and run\n\nBefore submitting a private run, **always call `discovery_estimate` first** and show the cost to the user. Let them confirm before you proceed.\n\nSubmit the analysis with `discovery_analyze`. Use `discovery_status` to poll — do not block, continue the conversation.\n\n### 9. Wait and deliver results\n\nPoll with `discovery_status` until complete, then fetch with `discovery_get_results`. Present results clearly:\n\n1. **Summary** — show the overview and key insights first.\n2. **Novel patterns** — highlight patterns Disco classified as novel (not in existing literature). These are the most valuable findings. For each, show the conditions, effect size, p-value, and novelty explanation with citations.\n3. **Confirmatory patterns** — patterns that validate known findings. Still useful, but less surprising.\n4. **Feature importance** — what features matter most overall.\n5. **Report link** — always include the `report_url` so the user can explore the interactive web report. Private reports require sign-in at the dashboard using the same email.\n\nAdapt the order to what the user asked. If they said \"what drives X?\", lead with feature importance. If they said \"find something new\", lead with novel patterns.\n\n#### Pattern fields you'll render\n\nEvery pattern in `result.patterns` is a dict (MCP) / `Pattern` dataclass (SDK) with at least:\n\n| Field | Type | Meaning |\n|---|---|---|\n| `description` | str | Pre-rendered sentence describing the pattern in plain English. Use this verbatim — don't try to compose your own from `conditions`. |\n| `conditions` | list[dict] | Feature ranges/values that define the pattern. Each item has `feature`, `type` (\"continuous\"/\"categorical\"), and either `min_value`/`max_value` or `values`. |\n| `p_value` | float | FDR-adjusted p-value on hold-out data. |\n| `novelty_type` | str | `\"novel\"` (not in existing literature) or `\"confirmatory\"` (validates known finding). |\n| `novelty_explanation` | str | Why Disco classified it as novel/confirmatory, with citations. |\n| `target_change_direction` | str | `\"max\"` (increases target) or `\"min\"` (decreases target). |\n| `abs_target_change` | float | Magnitude of effect, in target units. |\n| `support_count` / `support_percentage` | int / float | Rows matching the pattern. |\n| `citations` | list[dict] | Academic citations with `title`, `year`, `doi`, etc. |\n\n### 10. Go deeper\n\nAfter presenting results, let the user know:\n- **Deeper analyses find more patterns and more novel patterns.** If they ran at depth 2 and want to see what else is there, a deeper run is worth it.\n- If they're on the free tier, they may have patterns hidden behind the paywall — check `hints` and `hidden_deep_count` in the results and let them know.\n- **Upgrade options**: Researcher plan ($49/mo, 500 credits), Team plan ($199/mo, 2000 credits, 5 seats), or credit packs ($10 for 100 credits). Guide them through `discovery_subscribe` or `discovery_purchase_credits` if interested.\n\n### 11. Interpret and explore\n\nHelp the user dig into the results:\n- Explain what each pattern means in the context of their domain.\n- Compare novel vs confirmatory findings — what's new, what confirms existing knowledge.\n- Look at the conditions together: do patterns share features? Are there interactions between patterns?\n- Discuss practical implications: what could the user do with these findings?\n- If they want to explore specific patterns further, point them to the relevant section of the interactive report via `dashboard_urls`.\n\n---\n\n## MCP Server\n\nAdd to your MCP config:\n\n```json\n{\n \"mcpServers\": {\n \"discovery-engine\": {\n \"url\": \"https://disco.leap-labs.com/mcp\",\n \"env\": { \"DISCOVERY_API_KEY\": \"disco_...\" }\n }\n }\n}\n```\n\n### MCP Tools\n\n#### Discovery workflow\n\n| Tool | Purpose |\n|------|---------|\n| `discovery_upload` | Upload a dataset. Supports URL download (`file_url`), local path (`file_path`), or base64 content (`file_content`). Returns a `file_ref` for use with `discovery_analyze`. |\n| `discovery_analyze` | Submit a dataset for analysis using a `file_ref` from `discovery_upload`. Returns a `run_id`. |\n| `discovery_status` | Poll a running analysis by `run_id`. |\n| `discovery_get_results` | Fetch completed results: patterns, p-values, citations, feature importance. |\n| `discovery_estimate` | Estimate the credit cost before committing to a run. |\n\n#### Account management\n\n| Tool | Purpose |\n|------|---------|\n| `discovery_signup` | Start account creation — sends verification code to email. |\n| `discovery_signup_verify` | Complete signup by submitting the verification code. Returns API key. |\n| `discovery_login` | Get a new API key for an existing account — sends verification code to email. |\n| `discovery_login_verify` | Complete login by submitting the verification code. Returns a new API key. |\n| `discovery_account` | Check credits, plan, and usage. |\n| `discovery_list_plans` | View available plans and pricing. |\n| `discovery_subscribe` | Subscribe to or change plan. |\n| `discovery_purchase_credits` | Buy credit packs. |\n| `discovery_add_payment_method` | Attach a Stripe payment method. |\n\n### MCP Workflow\n\nAnalyses can take a while depending on dataset size and depth. **Do not block** — submit, continue other work, poll for completion.\n\n```\n1. discovery_estimate → Check credit cost (always do this for private runs)\n2. discovery_upload → Upload the dataset, get file_ref\n3. discovery_analyze → Submit for analysis using file_ref, get run_id\n4. discovery_status → Poll until status is \"completed\"\n Returns: status, queue_position, current_step,\n estimated_wait_seconds\n5. discovery_get_results → Fetch patterns, summary, feature importance\n```\n\n### Getting Data In\n\nChoose the right path for your situation:\n\n| Situation | Best approach |\n|-----------|--------------|\n| Data is at an http/https URL | `file_url` in `discovery_upload` |\n| Local file, Python available | Python SDK (`engine.discover(...)`) |\n| Local file, MCP server running locally | `file_path` in `discovery_upload` |\n| Local file, hosted MCP, no Python | Direct upload API (3 steps — see below) |\n| Small file, any language | `POST /api/data/upload/direct` (single step — see below) |\n| Tiny file already in memory | `file_content` in `discovery_upload` (last resort) |\n\n---\n\n**Data at a URL:**\n\n```\ndiscovery_upload(file_url=\"https://example.com/dataset.csv\")\n→ {\"file\": {...}, \"columns\": [{\"name\": \"col1\", \"type\": \"continuous\"}, ...], \"rowCount\": 5000}\n\ndiscovery_analyze(file_ref=, target_column=\"outcome\")\n```\n\nThe server downloads the file directly — nothing passes through the agent or the model context. Works with public URLs, S3 presigned URLs, or any accessible http/https link.\n\n---\n\n**Local file — Python SDK** (recommended for any local file):\n\n```python\nfrom discovery import Engine\n\nengine = Engine(api_key=\"disco_...\")\nresult = await engine.discover(\"data.csv\", target_column=\"outcome\")\n```\n\nHandles upload, polling, and results in one call. No size limit. See the **Python SDK** section for full documentation.\n\n---\n\n**Local file — MCP server running locally** (cloned from GitHub, stdio transport):\n\nIf you've cloned the repo and are running `server.py` locally, the process can read your filesystem directly:\n\n```\ndiscovery_upload(file_path=\"/home/user/data/dataset.csv\")\n→ {\"file\": {...}, \"columns\": [...], \"rowCount\": 5000}\n\ndiscovery_analyze(file_ref=, target_column=\"outcome\")\n```\n\nReads the file locally and streams it directly to cloud storage — nothing passes through the model context. No size limit. **`file_path` only works with a locally-running server** — calling it against the hosted server at `disco.leap-labs.com/mcp` returns a `File not found` error (the user's path doesn't exist on the hosted machine's filesystem).\n\n---\n\n**Local file — hosted MCP, direct upload** (works from any language):\n\nIf you're using the hosted MCP server and Python isn't available, you can upload directly via the REST API in three steps, then pass the result to `discovery_analyze` as normal.\n\n```bash\n# 1. Get a presigned upload URL\ncurl -X POST https://disco.leap-labs.com/api/data/upload/presign \\\n -H \"Authorization: Bearer disco_...\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"fileName\": \"data.csv\", \"contentType\": \"text/csv\", \"fileSize\": 1048576}'\n# → {\"uploadUrl\": \"https://storage.googleapis.com/...\", \"key\": \"uploads/abc/data.csv\", \"uploadToken\": \"tok_...\"}\n\n# 2. PUT the file directly to cloud storage (the uploadUrl is pre-signed — no auth header needed)\ncurl -X PUT \"\" \\\n -H \"Content-Type: text/csv\" \\\n --data-binary @data.csv\n\n# 3. Finalize the upload\ncurl -X POST https://disco.leap-labs.com/api/data/upload/finalize \\\n -H \"Authorization: Bearer disco_...\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"key\": \"uploads/abc/data.csv\", \"uploadToken\": \"tok_...\"}'\n# → {\"ok\": true, \"file\": {...}, \"columns\": [...], \"rowCount\": 5000}\n```\n\nPass the finalize response directly to `discovery_analyze` as `file_ref`. No size limit.\n\n---\n\n**Small file — direct upload** (single HTTP call, simpler than presign):\n\n```bash\ncurl -X POST https://disco.leap-labs.com/api/data/upload/direct \\\n -H \"Authorization: Bearer disco_...\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"fileName\": \"data.csv\", \"content\": \"\"}'\n# → {\"ok\": true, \"file\": {...}, \"columns\": [...], \"rowCount\": 5000}\n```\n\nPass the response directly to `discovery_analyze` as `file_ref`. Simpler than the 3-step presign flow but the entire file must fit in the request body. For large files, use presigned uploads or the Python SDK.\n\n---\n\n**Last resort — tiny file already in memory:**\n\nOnly use this if the file is already loaded into memory and none of the above options apply. The base64-encoded content passes through the model's context window, so this only works for very small files.\n\n```python\nimport base64\ncontent = base64.b64encode(open(\"data.csv\", \"rb\").read()).decode()\n```\n\n```\ndiscovery_upload(file_content=content, file_name=\"data.csv\")\n→ {\"file\": {...}, \"columns\": [...], \"rowCount\": 500}\n\ndiscovery_analyze(file_ref=, target_column=\"outcome\")\n```\n\n---\n\n### MCP Parameters\n\n**`discovery_upload`:**\n\nProvide exactly one of `file_url`, `file_path`, or `file_content`.\n\n- `file_url` — http/https URL. The server downloads it directly. Best option for hosted MCP.\n- `file_path` — Absolute path to a local file. **Only works when the MCP server is running locally.** Against the hosted server, returns a `File not found` error.\n- `file_content` — File contents, base64-encoded. **Last resort only** — the content passes through the model's context window, so this only works for very small files.\n- `file_name` — Filename with extension (e.g. `\"data.csv\"`), used for format detection. Required with `file_content`. Default: `\"data.csv\"`.\n\nReturns a `file_ref` (pass it directly to `discovery_analyze`) and `columns` (list of column names and types, useful if you need to inspect before choosing a target column).\n\n**`discovery_analyze`:**\n- `file_ref` — File reference returned by `discovery_upload`. Required.\n- `target_column` — The column to predict/explain\n- `analysis_depth` — 2 = default, higher = deeper analysis. Max: num_columns - 2\n- `visibility` — `\"public\"` (free, results published) or `\"private\"` (costs credits)\n- `column_descriptions` — JSON object mapping column names to descriptions. Significantly improves pattern explanations — always provide if column names are non-obvious\n- `excluded_columns` — JSON array of column names to exclude from analysis (see **Preparing Your Data** below)\n- `title` — Optional title for the analysis\n- `description` — Optional description of the dataset\n- `use_llms` — `false` (default) or `true`. Slower and more expensive, but you get smarter pre-processing, literature context and novelty assessment. **Public runs always use LLMs regardless of this setting.** Tradeoffs when false: pattern descriptions are generic, novelty is not assessed (no citations), report summaries are omitted, ambiguous integer columns (e.g. \"month\" 1-12) may be misclassified as categorical, and text cluster names are generic.\n- `author` — Optional author name for the dataset\n- `source_url` — Optional URL of the original data source\n\n### No API key?\n\n**New account:** Call `discovery_signup` with the user's email. This sends a verification code — the user must check their email. Then call `discovery_signup_verify` with the code to receive a `disco_` API key. Free tier: 10 credits/month, unlimited public runs. No password, no credit card.\n\n**Existing account (lost key or new session):** Call `discovery_login` with the user's email. Same OTP flow — sends a code, then call `discovery_login_verify` to get a new API key.\n\n### Insufficient credits?\n\n1. Call `discovery_estimate` to show what it would cost\n2. Suggest running publicly (free, but results are published and depth is locked to 2)\n3. Or guide them through `discovery_purchase_credits` / `discovery_subscribe`\n\n---\n\n## Preparing Your Data\n\nBefore running an analysis, **you must exclude columns that would produce meaningless findings.** Disco finds statistically real patterns — but if the input includes columns that are definitionally related to the target, the patterns will be true by definition, not by discovery.\n\n**Always exclude these column types via `excluded_columns`:**\n\n### 1. Identifiers\nRow IDs, patient IDs, UUIDs, accession numbers, sample codes. These are arbitrary labels with no analytical signal.\n\n### 2. Data leakage\nColumns that are the target column renamed, reformatted, or binned. Example: `diagnosis_text` when the target is `diagnosis_code`.\n\n### 3. Tautological / definitional columns\n**This is the most important category.** Columns that encode the same underlying construct as the target — through alternative classifications, component parts, or derived calculations. These produce findings that are trivially true.\n\nExamples:\n- **FAERS data:** If the target is `serious`, then `serious_outcome` (categories like death, disability, hospitalisation), `not_serious`, and `death` are all part of the same seriousness classification. A finding that \"death predicts seriousness\" is a tautology, not a discovery.\n- **Clinical trials:** If the target is `response`, then `response_category`, `responder_flag`, and `RECIST_response` are all encodings of the same outcome.\n- **Financial data:** If the target is `profit`, then `revenue` and `cost` together compose it (profit = revenue − cost).\n- **Surveys:** If the target is a composite index score, the sub-items that make up the index are tautological.\n- **Derived columns:** BMI when height and weight are present, age when birth_date is present.\n\n**How to identify them:** Ask \"is this column just a different way of expressing what the target already measures?\" If yes, exclude it.\n\n```python\n# Example: FAERS adverse event analysis\nexcluded_columns=[\"serious_outcome\", \"not_serious\", \"death\", \"hospitalization\",\n \"disability\", \"congenital_anomaly\", \"life_threatening\",\n \"required_intervention\", \"case_id\", \"report_id\"]\n```\n\n---\n\n## Python SDK\n\n## When To Use This Tool\n\nDisco is not another AI data analyst that writes pandas or SQL for you. It is a **discovery pipeline** — it finds patterns in data that you, the user, and other analysis tools would miss because they don't know to look for them.\n\nUse it when you need to go beyond answering questions about data, and start finding things nobody thought to ask:\n\n- **Novel pattern discovery** — feature interactions, subgroup effects, and conditional relationships you wouldn't think to look for\n- **Statistical validation** — FDR-corrected p-values tested on hold-out data, not just correlations\n- **A target column** you want to understand — what really drives it, beyond what's obvious\n\n**Use Disco when the user says:** \"what's really driving X?\", \"are there patterns we're missing?\", \"find something new in this data\", \"what predicts Y that we haven't considered?\", \"go deeper than correlation\", \"discover non-obvious relationships\"\n\n**Use pandas/SQL instead when the user says:** \"summarize this data\", \"make a chart\", \"what's the average?\", \"filter rows where X > 5\", \"show me the distribution\"\n\n## What It Does (That You Cannot Do Yourself)\n\nDisco finds complex patterns in your data — feature interactions, nonlinear thresholds, and meaningful subgroups — without requiring prior hypotheses about what matters. Each pattern is validated on hold-out data, corrected for multiple testing, and checked for novelty against academic literature with citations.\n\nThis is a computational pipeline, not prompt engineering over data. You cannot replicate what it does by writing pandas code or asking an LLM to look at a CSV. It finds structure that hypothesis-driven analysis misses because it doesn't start with hypotheses.\n\n## Getting an API Key\n\n**Programmatic (for agents):** POST the email to `/api/signup`. The server either returns the API key directly (free-tier email-only signup, the common case) or asks for OTP verification — branch on the response shape.\n\n```bash\ncurl -X POST https://disco.leap-labs.com/api/signup \\\n -H \"Content-Type: application/json\" \\\n -d '{\"email\": \"agent@example.com\"}'\n```\n\nResponse — direct key (no verification needed):\n```json\n{\"key\": \"disco_...\", \"key_id\": \"...\", \"organization_id\": \"...\", \"tier\": \"free_tier\", \"credits\": 10}\n```\n\nResponse — verification required (only when the server explicitly asks):\n```json\n{\"status\": \"verification_required\", \"email\": \"agent@example.com\"}\n```\n\nIf you get the latter, the user must read the 6-digit code from their email, then submit it:\n```bash\ncurl -X POST https://disco.leap-labs.com/api/signup/verify \\\n -H \"Content-Type: application/json\" \\\n -d '{\"email\": \"agent@example.com\", \"code\": \"123456\"}'\n# → {\"key\": \"disco_...\", \"tier\": \"free_tier\", \"credits\": 10}\n```\n\nThe Python SDK's `Engine.signup()` and `Engine.login()` already handle both shapes — prefer them over raw HTTP if you can.\n\n\n**Existing account (lost key or new session):** Same OTP flow via `/api/login` and `/api/login/verify`, or in the SDK:\n\n```python\nengine = await Engine.login(email=\"agent@example.com\")\n```\n\n**Manual (for humans):** Sign up at https://disco.leap-labs.com/sign-up, create key at https://disco.leap-labs.com/developers.\n\n## Installation\n\n```bash\npip install discovery-engine-api\n```\n\n## Quick Start\n\nDisco runs are async and can take a while. **Do not block on them** — submit the run, continue with other work, and retrieve results when ready.\n\n```python\nfrom discovery import Engine\n\n# If you already have an API key:\nengine = Engine(api_key=\"disco_...\")\n\n# Or sign up for one.\n# Sends a code to the email address and prompts for it interactively.\n# Requires a terminal — for fully automated agents, use the two-step REST API\n# in the \"Getting an API Key\" section above instead.\nengine = await Engine.signup(email=\"agent@example.com\")\n\n# One-call method: submit, poll, and return results automatically\nresult = await engine.discover(\n file=\"data.csv\",\n target_column=\"outcome\",\n)\n\n# result.patterns contains the discovered patterns\nfor pattern in result.patterns:\n if pattern.p_value < 0.05 and pattern.novelty_type == \"novel\":\n print(f\"{pattern.description} (p={pattern.p_value:.4f})\")\n```\n\n### Inspecting Columns Before Running\n\nIf you need to see the dataset's columns before choosing a target column, upload first and inspect:\n\n```python\n# Upload once and get the server's parsed column list\nupload = await engine.upload_file(file=\"data.csv\", title=\"My dataset\")\nprint(upload[\"columns\"]) # [{\"name\": \"col1\", \"type\": \"continuous\", ...}, ...]\nprint(upload[\"rowCount\"]) # e.g., 5000\n\n# Pass the result to avoid re-uploading\nresult = await engine.run_async(\n file=\"data.csv\",\n target_column=\"col1\",\n wait=True,\n upload_result=upload, # skips the upload step\n)\n```\n\n### Running in the Background\n\nIf you need to do other work while Disco runs (recommended for agent workflows):\n\n```python\n# Submit and return immediately (wait=False is the default for run_async)\nrun = await engine.run_async(file=\"data.csv\", target_column=\"outcome\")\nprint(f\"Submitted run {run.run_id}, continuing with other work...\")\n\n# ... do other things ...\n\n# Check back later\nresult = await engine.wait_for_completion(run.run_id, timeout=1800)\n```\n\nThis is the preferred pattern for agents. `engine.discover()` is a convenience wrapper that does this internally with `wait=True`.\n\n**Non-async contexts:** use `engine.discover_sync()` — same signature as `discover()`, runs in a managed event loop.\n\n## Example Output\n\nHere's a truncated real response from a crop yield analysis (target column: `yield_tons_per_hectare`). This is what `engine.discover()` returns:\n\n```python\nEngineResult(\n run_id=\"a1b2c3d4-...\",\n status=\"completed\",\n task=\"regression\",\n total_rows=5012,\n report_url=\"https://disco.leap-labs.com/reports/a1b2c3d4-...\",\n\n summary=Summary(\n overview=\"Disco identified 14 statistically significant patterns in this \"\n \"agricultural dataset. 5 patterns are novel — not reported in existing literature. \"\n \"The strongest driver of crop yield is a previously unreported interaction between \"\n \"humidity and wind speed at specific thresholds.\",\n key_insights=[\n \"Humidity alone is a known predictor, but the interaction with low wind speed at \"\n \"72-89% humidity produces a 34% yield increase — a novel finding.\",\n \"Soil nitrogen above 45 mg/kg shows diminishing returns when phosphorus is below \"\n \"12 mg/kg, contradicting standard fertilization guidelines.\",\n \"Planting density has a non-linear effect: the optimal range (35-42 plants/m²) is \"\n \"narrower than current recommendations suggest.\",\n ],\n novel_patterns=PatternGroup(\n pattern_ids=[\"p-1\", \"p-2\", \"p-5\", \"p-9\", \"p-12\"],\n explanation=\"5 of 14 patterns have not been reported in the agricultural literature. \"\n \"The humidity × wind interaction (p-1) and the nitrogen-phosphorus \"\n \"diminishing returns effect (p-2) are the most significant novel findings.\"\n ),\n ),\n\n patterns=[\n # Pattern 1: Novel multi-condition interaction\n Pattern(\n id=\"p-1\",\n task=\"regression\",\n target_column=\"yield_tons_per_hectare\",\n description=\"When humidity is between 72-89% AND wind speed is below 12 km/h, \"\n \"crop yield increases by 34% above the dataset average\",\n conditions=[\n {\"type\": \"continuous\", \"feature\": \"humidity_pct\",\n \"min_value\": 72.0, \"max_value\": 89.0, \"min_q\": 0.55, \"max_q\": 0.88},\n {\"type\": \"continuous\", \"feature\": \"wind_speed_kmh\",\n \"min_value\": 0.0, \"max_value\": 12.0, \"min_q\": 0.0, \"max_q\": 0.41},\n ],\n p_value=0.003, # FDR-corrected\n p_value_raw=0.0004,\n novelty_type=\"novel\",\n novelty_explanation=\"Published studies examine humidity and wind speed as independent \"\n \"predictors of crop yield, but this interaction effect — where \"\n \"low wind amplifies the benefit of high humidity within a specific \"\n \"range — has not been reported in the literature.\",\n citations=[\n {\"title\": \"Effects of relative humidity on cereal crop productivity\",\n \"authors\": [\"Zhang, L.\", \"Wang, H.\"], \"year\": \"2021\",\n \"journal\": \"Journal of Agricultural Science\", \"doi\": \"10.1017/S0021859621000...\"},\n {\"title\": \"Wind exposure and grain yield: a meta-analysis\",\n \"authors\": [\"Patel, R.\", \"Singh, K.\"], \"year\": \"2019\",\n \"journal\": \"Field Crops Research\", \"doi\": \"10.1016/j.fcr.2019.03...\"},\n ],\n target_change_direction=\"max\",\n abs_target_change=0.34,\n target_score=0.81,\n support_count=847,\n support_percentage=16.9,\n target_mean=8.7,\n target_std=1.2,\n ),\n\n # Pattern 2: Novel — contradicts existing guidelines\n Pattern(\n id=\"p-2\",\n task=\"regression\",\n target_column=\"yield_tons_per_hectare\",\n description=\"When soil nitrogen exceeds 45 mg/kg AND soil phosphorus is below \"\n \"12 mg/kg, crop yield decreases by 18% — a diminishing returns effect \"\n \"not captured by standard fertilization models\",\n conditions=[\n {\"type\": \"continuous\", \"feature\": \"soil_nitrogen_mg_kg\",\n \"min_value\": 45.0, \"max_value\": 98.0, \"min_q\": 0.72, \"max_q\": 1.0},\n {\"type\": \"continuous\", \"feature\": \"soil_phosphorus_mg_kg\",\n \"min_value\": 1.0, \"max_value\": 12.0, \"min_q\": 0.0, \"max_q\": 0.31},\n ],\n p_value=0.008,\n p_value_raw=0.0012,\n novelty_type=\"novel\",\n novelty_explanation=\"Nitrogen-phosphorus balance is studied extensively, but the \"\n \"specific threshold at which high nitrogen becomes counterproductive \"\n \"under low phosphorus conditions has not been quantified in field studies.\",\n citations=[\n {\"title\": \"Nitrogen-phosphorus interactions in cereal cropping systems\",\n \"authors\": [\"Mueller, T.\", \"Fischer, A.\"], \"year\": \"2020\",\n \"journal\": \"Nutrient Cycling in Agroecosystems\", \"doi\": \"10.1007/s10705-020-...\"},\n ],\n target_change_direction=\"min\",\n abs_target_change=0.18,\n target_score=0.74,\n support_count=634,\n support_percentage=12.7,\n target_mean=5.3,\n target_std=1.8,\n ),\n\n # Pattern 3: Confirmatory — validates known finding\n Pattern(\n id=\"p-3\",\n task=\"regression\",\n target_column=\"yield_tons_per_hectare\",\n description=\"When soil organic matter is above 3.2% AND irrigation is 'drip', \"\n \"crop yield increases by 22%\",\n conditions=[\n {\"type\": \"continuous\", \"feature\": \"soil_organic_matter_pct\",\n \"min_value\": 3.2, \"max_value\": 7.1, \"min_q\": 0.61, \"max_q\": 1.0},\n {\"type\": \"categorical\", \"feature\": \"irrigation_type\",\n \"values\": [\"drip\"]},\n ],\n p_value=0.001,\n p_value_raw=0.0001,\n novelty_type=\"confirmatory\",\n novelty_explanation=\"The positive interaction between soil organic matter and drip \"\n \"irrigation efficiency is well-documented in the literature.\",\n citations=[\n {\"title\": \"Drip irrigation and soil health: a systematic review\",\n \"authors\": [\"Kumar, S.\", \"Patel, A.\"], \"year\": \"2022\",\n \"journal\": \"Agricultural Water Management\", \"doi\": \"10.1016/j.agwat.2022...\"},\n ],\n target_change_direction=\"max\",\n abs_target_change=0.22,\n target_score=0.69,\n support_count=1203,\n support_percentage=24.0,\n target_mean=7.9,\n target_std=1.5,\n ),\n\n # ... 11 more patterns omitted\n ],\n\n feature_importance=FeatureImportance(\n kind=\"global\",\n baseline=6.5, # Mean yield across the dataset\n scores=[\n FeatureImportanceScore(feature=\"humidity_pct\", score=1.82),\n FeatureImportanceScore(feature=\"soil_nitrogen_mg_kg\", score=1.45),\n FeatureImportanceScore(feature=\"soil_organic_matter_pct\", score=1.21),\n FeatureImportanceScore(feature=\"irrigation_type\", score=0.94),\n FeatureImportanceScore(feature=\"wind_speed_kmh\", score=-0.67),\n FeatureImportanceScore(feature=\"planting_density_per_m2\", score=0.58),\n # ... more features\n ],\n ),\n\n columns=[\n Column(name=\"yield_tons_per_hectare\", type=\"continuous\", data_type=\"float\",\n mean=6.5, median=6.2, std=2.1, min=1.1, max=14.3),\n Column(name=\"humidity_pct\", type=\"continuous\", data_type=\"float\",\n mean=65.3, median=67.0, std=18.2, min=12.0, max=99.0),\n Column(name=\"irrigation_type\", type=\"categorical\", data_type=\"string\",\n approx_unique=4, mode=\"furrow\"),\n # ... more columns\n ],\n)\n```\n\nKey things to notice:\n- **Patterns are combinations of conditions** (humidity AND wind speed), not single correlations\n- **Specific threshold ranges** (72-89%), not just \"higher humidity is better\"\n- **Novel vs confirmatory**: each pattern is classified and explained — novel findings are what you came for, confirmatory ones validate known science\n- **Citations** show what IS known, so you can see what's genuinely new\n- **Summary** gives the agent a narrative to present to the user immediately\n- **`report_url`** links to an interactive web report — drop this in your response so the user can explore visually. **Private runs require sign-in** — tell the user to sign in at the dashboard using the same email address the account was created with (email verification code, no password needed). Public runs are accessible to anyone.\n\n## Parameters\n\n```python\nengine.discover(\n file: str | Path | pd.DataFrame, # Dataset to analyze\n target_column: str, # Column to predict/analyze\n analysis_depth: int = 2, # 2=default, higher=deeper analysis (max: num_columns - 2)\n visibility: str = \"public\", # \"public\" (free, results will be published) or \"private\" (costs credits)\n title: str | None = None, # Dataset title\n description: str | None = None, # Dataset description\n column_descriptions: dict[str, str] | None = None, # Column descriptions for better pattern explanations\n excluded_columns: list[str] | None = None, # Columns to exclude from analysis\n use_llms: bool = False, # True = LLM explanations (costs more) — see below\n timeout: float = 1800, # Max seconds to wait for completion\n)\n```\n\n**Tip:** Providing `column_descriptions` significantly improves pattern explanations. If your columns have non-obvious names (e.g., `col_7`, `feat_a`), always describe them.\n\n## Cost\n\n- **Public runs**: Free. Results published to public gallery. Locked to depth=2.\n- **Private runs**: Credits scale with file size, depth, and run configuration. $0.10 per credit. Use `discovery_estimate` to check cost before running.\n- API keys: https://disco.leap-labs.com/developers\n- Credits: https://disco.leap-labs.com/account\n\n## Paying for Credits (Programmatic)\n\nAgents can attach a payment method and purchase credits entirely via the API — no browser required.\n\n**Step 1 — Get your Stripe publishable key**\n\n```python\naccount = await engine.get_account()\nstripe_pk = account[\"stripe_publishable_key\"]\nstripe_customer_id = account[\"stripe_customer_id\"]\n```\n\nOr via REST:\n\n```bash\ncurl https://disco.leap-labs.com/api/account \\\n -H \"Authorization: Bearer disco_...\"\n# → { \"stripe_publishable_key\": \"pk_live_...\", \"stripe_customer_id\": \"cus_...\", \"credits\": {...}, ... }\n```\n\n**Step 2 — Tokenize a card using the Stripe API**\n\nUse the publishable key to create a Stripe PaymentMethod. Card data goes directly to Stripe — Disco never sees it.\n\n```python\nimport requests\n\npm_response = requests.post(\n \"https://api.stripe.com/v1/payment_methods\",\n auth=(stripe_pk, \"\"), # publishable key as username, empty password\n data={\n \"type\": \"card\",\n \"card[number]\": \"4242424242424242\",\n \"card[exp_month]\": \"12\",\n \"card[exp_year]\": \"2028\",\n \"card[cvc]\": \"123\",\n },\n)\npayment_method_id = pm_response.json()[\"id\"] # \"pm_...\"\n```\n\n**Step 3 — Attach the payment method**\n\n```python\nresult = await engine.add_payment_method(payment_method_id)\n# → {\"payment_method_attached\": True, \"card_last4\": \"4242\", \"card_brand\": \"visa\"}\n```\n\nOr via REST:\n\n```bash\ncurl -X POST https://disco.leap-labs.com/api/account/payment-method \\\n -H \"Authorization: Bearer disco_...\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"payment_method_id\": \"pm_...\"}'\n```\n\n**Step 4 — Purchase credits**\n\nCredits are sold in packs of 100 ($10/pack, $0.10/credit).\n\n```python\nresult = await engine.purchase_credits(packs=1)\n# → {\"purchased_credits\": 100, \"total_credits\": 110, \"charge_amount_usd\": 10.0, \"stripe_payment_id\": \"pi_...\"}\n```\n\nOr via REST:\n\n```bash\ncurl -X POST https://disco.leap-labs.com/api/account/credits/purchase \\\n -H \"Authorization: Bearer disco_...\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"packs\": 1}'\n```\n\n**Subscriptions (optional)**\n\nFor regular usage, subscribe to a paid plan instead of buying packs:\n\n```python\n# Plans: free_tier ($0, 10 cr/mo), tier_1 ($49, 500 cr/mo), tier_2 ($199, 2000 cr/mo)\nresult = await engine.subscribe(plan=\"tier_1\")\n# → {\"plan\": \"tier_1\", \"name\": \"Researcher\", \"monthly_credits\": 500, \"price_usd\": 49}\n```\n\nRequires a payment method on file. See `GET /api/plans` for full plan details.\n\n## Estimate Before Running\n\nBefore submitting a private analysis, estimate the credit cost:\n\n```python\nestimate = await engine.estimate(\n file_size_mb=10.5,\n num_columns=25,\n analysis_depth=2,\n visibility=\"private\",\n)\n# estimate[\"cost\"][\"credits\"] → 55\n# estimate[\"account\"][\"sufficient\"] → True/False\n```\n\n\n## Result Structure\n\n```python\n@dataclass\nclass EngineResult:\n run_id: str\n report_id: str | None # Report UUID (used in report_url)\n status: str # \"pending\", \"processing\", \"completed\", \"failed\"\n dataset_title: str | None # Title of the dataset\n dataset_description: str | None # Description of the dataset\n total_rows: int | None\n target_column: str | None # Column being predicted/analyzed\n task: str | None # \"regression\", \"binary_classification\", \"multiclass_classification\"\n summary: Summary | None # LLM-generated insights\n patterns: list[Pattern] # Discovered patterns (the core output)\n columns: list[Column] # Feature info and statistics\n correlation_matrix: list[CorrelationEntry] # Feature correlations\n feature_importance: FeatureImportance | None # Global importance scores\n job_id: str | None # Job ID for tracking\n job_status: str | None # Job queue status\n queue_position: int | None # Position in queue when pending (1 = next up)\n current_step: str | None # Active pipeline step (preprocessing, training, interpreting, reporting)\n current_step_message: str | None # Human-readable description of the current step\n estimated_wait_seconds: int | None # Estimated queue wait time in seconds (pending only)\n error_message: str | None\n report_url: str | None # Shareable link to interactive web report\n dashboard_urls: dict[str, dict[str, str]] | None # Direct links to report sections (summary, patterns, territory, features)\n hints: list[str] # Upgrade hints (non-empty for free-tier users with hidden patterns)\n hidden_deep_count: int # Patterns hidden for free-tier accounts (upgrade to see all)\n hidden_deep_novel_count: int # Novel patterns hidden for free-tier accounts\n\n@dataclass\nclass Pattern:\n id: str\n description: str # Human-readable description of the pattern\n conditions: list[dict] # Conditions defining the pattern (feature ranges/values)\n p_value: float # FDR-adjusted p-value (lower = more significant)\n p_value_raw: float | None # Raw p-value before FDR adjustment\n novelty_type: str # \"novel\" (new finding) or \"confirmatory\" (known in literature)\n novelty_explanation: str # Why this is novel or confirmatory\n citations: list[dict] # Academic citations supporting novelty assessment\n target_change_direction: str # \"max\" (increases target) or \"min\" (decreases target)\n abs_target_change: float # Magnitude of effect\n support_count: int # Number of rows matching this pattern\n support_percentage: float # Percentage of dataset\n target_score: float # Mean target value (regression) or class fraction (classification) in the subgroup\n task: str\n target_column: str\n target_class: str | None # For classification tasks\n target_mean: float | None # For regression tasks\n target_std: float | None\n\n@dataclass\nclass PatternGroup:\n pattern_ids: list[str] # IDs of patterns in this group\n explanation: str # Why these patterns are grouped\n\n@dataclass\nclass Summary:\n overview: str # High-level summary\n key_insights: list[str] # Main takeaways\n novel_patterns: PatternGroup # Novel pattern IDs and explanation\n selected_pattern_id: str | None\n\n@dataclass\nclass CorrelationEntry:\n feature_x: str\n feature_y: str\n value: float\n\n@dataclass\nclass Column:\n id: str\n name: str\n display_name: str\n type: str # \"continuous\" or \"categorical\"\n data_type: str # \"int\", \"float\", \"string\", \"boolean\", \"datetime\"\n enabled: bool\n description: str | None\n mean: float | None\n median: float | None\n std: float | None\n min: float | None\n max: float | None\n iqr_min: float | None\n iqr_max: float | None\n mode: str | None # Most common value (categorical columns)\n approx_unique: int | None # Approximate distinct value count\n null_percentage: float | None\n feature_importance_score: float | None\n\n@dataclass\nclass FeatureImportance:\n kind: str # \"global\"\n baseline: float\n scores: list[FeatureImportanceScore]\n\n@dataclass\nclass FeatureImportanceScore:\n feature: str\n score: float # Signed importance score\n```\n\n## Working With Results\n\n```python\n# Filter for significant novel patterns\nnovel = [p for p in result.patterns if p.p_value < 0.05 and p.novelty_type == \"novel\"]\n\n# Get patterns that increase the target\nincreasing = [p for p in result.patterns if p.target_change_direction == \"max\"]\n\n# Get the most important features\nif result.feature_importance:\n top_features = sorted(result.feature_importance.scores, key=lambda s: abs(s.score), reverse=True)\n\n# Access pattern conditions (the \"rules\" defining the pattern)\nfor pattern in result.patterns:\n for cond in pattern.conditions:\n # cond has: type (\"continuous\"/\"categorical\"), feature, min_value/max_value or values\n print(f\" {cond['feature']}: {cond}\")\n```\n\n## Error Handling\n\n```python\nfrom discovery.errors import (\n AuthenticationError,\n InsufficientCreditsError,\n RateLimitError,\n RunFailedError,\n RunNotFoundError,\n PaymentRequiredError,\n)\n\ntry:\n result = await engine.discover(file=\"data.csv\", target_column=\"target\")\nexcept AuthenticationError as e:\n pass # Invalid or expired API key — check e.suggestion\nexcept InsufficientCreditsError as e:\n pass # Not enough credits — e.credits_required, e.credits_available, e.suggestion\nexcept RateLimitError as e:\n pass # Too many requests — retry after e.retry_after seconds\nexcept RunFailedError as e:\n pass # Run failed server-side — e.run_id\nexcept RunNotFoundError as e:\n pass # Run not found — e.run_id (may have been cleaned up)\nexcept PaymentRequiredError as e:\n pass # Payment method needed — check e.suggestion\nexcept FileNotFoundError:\n pass # File doesn't exist\nexcept TimeoutError:\n pass # Didn't complete in time — retrieve later with engine.wait_for_completion(run_id)\n```\n\nAll errors inherit from `DiscoveryError` and include a `suggestion` field with actionable instructions.\n\n## Expected Data Format\n\nDisco expects a **flat table** — columns for features, rows for samples.\n\n- **One row per observation** — a patient, a sample, a transaction, a measurement, etc.\n- **One column per feature** — numeric, categorical, datetime, or free text are all fine\n- **One target column** — the outcome to analyze. Must have at least 2 distinct values.\n- **Missing values are OK** — Disco handles them automatically. Don't drop rows or impute beforehand.\n\nSupported formats: CSV, TSV, Excel (.xlsx), JSON, Parquet, ARFF, Feather. Max 5 GB.\n\nNot supported: images, raw text documents, nested/hierarchical JSON, multi-sheet Excel (use the first sheet or export to CSV).\n\n## Displaying Results\n\nWhen presenting Disco results, render interactive visualizations -- don't just dump text. Default order (adapt based on what the user asked):\n\n1. **Summary** -- show `summary.overview` and `summary.key_insights`. Links to `dashboard_urls.summary.url`.\n\n2. **Pattern plots** -- for the top patterns, render a violin plot: one violin per condition, one for all conditions combined, one for the overall dataset. Y-axis is the target variable. Shows how each condition narrows the distribution. Links to `dashboard_urls.patterns.url`.\n\n3. **Territory map** -- 3D surface where X and Y axes are two features from a pattern's conditions, Z axis is the target. Shows the interaction landscape. Best when patterns involve feature interactions. Links to `dashboard_urls.territory.url`.\n\n4. **Feature importance** -- horizontal waterfall bars floating from zero, sorted by absolute contribution. Links to `dashboard_urls.features.url`.\n\n5. **Correlation heatmap** -- square matrix of feature correlations, sorted by correlation with target. Links to `dashboard_urls.features.url`.\n\nUse judgment: if the user asked \"what drives X?\", lead with feature importance. If they asked \"find something new\", lead with novel patterns. If they're exploring interactions, lead with territory.\n\nFor exact colors, scales, and layout details, follow the full visualization spec: **https://disco.leap-labs.com/visualization-spec**\n\nAlways link to the relevant `dashboard_urls` page so users can explore the full interactive version.\n\n## Links\n\n- [Dashboard & API Keys](https://disco.leap-labs.com/developers)\n- [Full LLM Documentation](https://disco.leap-labs.com/llms-full.txt)\n- [Visualization Spec](https://disco.leap-labs.com/visualization-spec)\n- [Python SDK on PyPI](https://pypi.org/project/discovery-engine-api/)\n- [API Spec](https://disco.leap-labs.com/.well-known/openapi.json)\n\nFile v0.2.182:README.md\n\n# Disco\n\n**Find novel, statistically validated patterns in tabular data** — feature interactions, subgroup effects, and conditional relationships that humans and agents miss.\n\n[![PyPI](https://img.shields.io/pypi/v/discovery-engine-api)](https://pypi.org/project/discovery-engine-api/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)\n\nMade by [Leap Laboratories](https://www.leap-labs.com).\n\n---\n\n## What it actually does\n\nMost data analysis starts with a question. Disco starts with the data.\n\nWithout biases or assumptions, it finds combinations of feature conditions that significantly shift your target column — things like \"patients aged 45–65 with low HDL *and* high CRP have 3× the readmission rate\" — without you needing to hypothesise that interaction first.\n\nEach pattern is:\n- **Validated on a hold-out set** — increases the chance of generalisation\n- **FDR-corrected** — p-values included, adjusted for multiple testing\n- **Checked against academic literature** — to help you understand what you've found, and identify if it is novel.\n\nThe output is structured: conditions, effect sizes, p-values, citations, and a novelty classification for every pattern found.\n\n**Use it when:** \"which variables are most important with respect to X\", \"are there patterns we're missing?\", \"I don't know where to start with this data\", \"I need to understand how A and B affect C\".\n\n**Not for:** summary statistics, visualisation, filtering, SQL queries — use pandas for those\n\n---\n\n## Quickstart\n\n```bash\npip install discovery-engine-api\n```\n\nGet an API key:\n\n```bash\n# Step 1: request verification code (no password, no card)\ncurl -X POST https://disco.leap-labs.com/api/signup \\\n -H \"Content-Type: application/json\" \\\n -d '{\"email\": \"you@example.com\"}'\n\n# Step 2: submit code from email → get key\ncurl -X POST https://disco.leap-labs.com/api/signup/verify \\\n -H \"Content-Type: application/json\" \\\n -d '{\"email\": \"you@example.com\", \"code\": \"123456\"}'\n# → {\"key\": \"disco_...\", \"credits\": 10, \"tier\": \"free_tier\"}\n```\n\nOr create a key at [disco.leap-labs.com/developers](https://disco.leap-labs.com/developers).\n\nRun your first analysis:\n\n```python\nfrom discovery import Engine\n\nengine = Engine(api_key=\"disco_...\")\nresult = await engine.discover(\n file=\"data.csv\",\n target_column=\"outcome\",\n)\n\nfor pattern in result.patterns:\n if pattern.p_value < 0.05 and pattern.novelty_type == \"novel\":\n print(f\"{pattern.description} (p={pattern.p_value:.4f})\")\n\nprint(f\"Explore: {result.report_url}\")\n```\n\nRuns take a few minutes. `discover()` polls automatically and logs progress — queue position, estimated wait, current pipeline step, and ETA. For background runs, see [Running asynchronously](#running-asynchronously).\n\n→ [Full Python SDK reference](docs/python-sdk.md) · [Example notebook](notebooks/quickstart.ipynb)\n\n---\n\n## What you get back\n\nEach `Pattern` in `result.patterns` looks like this (real output from a crop yield dataset):\n\n```python\nPattern(\n description=\"When humidity is between 72–89% AND wind speed is below 12 km/h, \"\n \"crop yield increases by 34% above the dataset average\",\n conditions=[\n {\"type\": \"continuous\", \"feature\": \"humidity_pct\",\n \"min_value\": 72.0, \"max_value\": 89.0},\n {\"type\": \"continuous\", \"feature\": \"wind_speed_kmh\",\n \"min_value\": 0.0, \"max_value\": 12.0},\n ],\n p_value=0.003, # FDR-corrected\n novelty_type=\"novel\",\n novelty_explanation=\"Published studies examine humidity and wind speed as independent \"\n \"predictors, but this interaction effect — where low wind amplifies \"\n \"the benefit of high humidity within a specific range — has not been \"\n \"reported in the literature.\",\n citations=[\n {\"title\": \"Effects of relative humidity on cereal crop productivity\",\n \"authors\": [\"Zhang, L.\", \"Wang, H.\"], \"year\": \"2021\",\n \"journal\": \"Journal of Agricultural Science\"},\n ],\n target_change_direction=\"max\",\n abs_target_change=0.34, # 34% increase\n support_count=847, # rows matching this pattern\n support_percentage=16.9,\n)\n```\n\nKey things to notice:\n\n- **Patterns are combinations of conditions** — humidity AND wind speed together, not just \"more humidity is better\"\n- **Specific thresholds** — 72–89%, not a vague correlation\n- **Novel vs confirmatory** — every pattern is classified; confirmatory ones validate known science, novel ones are what you came for\n- **Citations** — shows what IS known, so you can see what's genuinely new\n- **`report_url`** links to an interactive web report with all patterns visualised\n\nThe `result.summary` gives an LLM-generated narrative overview:\n\n```python\nresult.summary.overview\n# \"Disco identified 14 statistically significant patterns. 5 are novel.\n# The strongest driver is a previously unreported interaction between humidity\n# and wind speed at specific thresholds.\"\n\nresult.summary.key_insights\n# [\"Humidity × low wind speed at 72–89% humidity produces a 34% yield increase — novel.\",\n# \"Soil nitrogen above 45 mg/kg shows diminishing returns when phosphorus is below 12 mg/kg.\",\n# ...]\n```\n\n---\n\n## How it works\n\nDisco is a pipeline, not prompt engineering over data. It:\n\n1. Trains machine learning models on a subset of your data\n2. Uses interpretability techniques to extract learned patterns\n3. Validates every pattern on the held-out data with FDR correction (Benjamini-Hochberg)\n4. Checks surviving patterns against academic literature via semantic search\n\nYou cannot replicate this by writing pandas code or asking an LLM to look at a CSV. It finds structure that hypothesis-driven analysis misses because it doesn't start with hypotheses.\n\n---\n\n## Preparing your data\n\nBefore running, exclude columns that would produce meaningless findings. Disco finds statistically real patterns — but if the input includes columns that are definitionally related to the target, the patterns will be tautological.\n\n**Exclude:**\n1. **Identifiers** — row IDs, UUIDs, patient IDs, sample codes\n2. **Data leakage** — the target renamed or reformatted (e.g., `diagnosis_text` when the target is `diagnosis_code`)\n3. **Tautological columns** — alternative encodings of the same construct as the target. If target is `serious`, then `serious_outcome`, `not_serious`, `death` are all part of the same classification. If target is `profit`, then `revenue` and `cost` together compose it. If target is a survey index, the sub-items are tautological.\n\n> Full guidance with examples: [SKILL.md](SKILL.md#preparing-your-data)\n\n---\n\n## Parameters\n\n```python\nawait engine.discover(\n file=\"data.csv\", # path, Path, or pd.DataFrame\n target_column=\"outcome\", # column to predict/explain\n analysis_depth=2, # 2=default, higher=deeper analysis, lower = faster and cheaper\n visibility=\"public\", # \"public\" (always free, data and report is published) or \"private\" (costs credits)\n column_descriptions={ # improves pattern explanations and literature context\n \"bmi\": \"Body mass index\",\n \"hdl\": \"HDL cholesterol in mg/dL\",\n },\n excluded_columns=[\"id\", \"timestamp\"], # see \"Preparing your data\" above\n use_llms=False, # Defaults to False. If True, runs are slower and more expensive, but you get smarter pre-processing, summary page, literature context and novelty assessment. Public runs always use LLMs.\n title=\"My dataset\",\n description=\"...\", # improves pattern explanations and literature context\n)\n```\n\n> Public runs are free but results are published. Set `visibility=\"private\"` for private data — this costs credits.\n\n---\n\n## Running asynchronously\n\nRuns take a few minutes. For agent workflows or scripts that do other work in parallel:\n\n```python\n# Submit without waiting\nrun = await engine.run_async(file=\"data.csv\", target_column=\"outcome\", wait=False)\nprint(f\"Submitted {run.run_id}, continuing...\")\n\n# ... do other things ...\n\nresult = await engine.wait_for_completion(run.run_id, timeout=1800)\n```\n\nFor synchronous scripts and Jupyter notebooks:\n\n```python\nresult = engine.run(file=\"data.csv\", target_column=\"outcome\", wait=True)\n# or: pip install discovery-engine-api[jupyter] for notebook compatibility\n```\n\n---\n\n## MCP server\n\nDisco is available as an MCP server — no local install required.\n\n```json\n{\n \"mcpServers\": {\n \"discovery-engine\": {\n \"url\": \"https://disco.leap-labs.com/mcp\",\n \"env\": { \"DISCOVERY_API_KEY\": \"disco_...\" }\n }\n }\n}\n```\n\nTools: `discovery_list_plans`, `discovery_estimate`, `discovery_upload`, `discovery_analyze`, `discovery_status`, `discovery_get_results`, `discovery_account`, `discovery_signup`, `discovery_signup_verify`, `discovery_login`, `discovery_login_verify`, `discovery_add_payment_method`, `discovery_subscribe`, `discovery_purchase_credits`.\n\n→ [Full agent skill file](SKILL.md)\n\n---\n\n## Pricing\n\n| | Cost |\n|---|---|\n| Public runs | Free — results and data are published |\n| Private runs | Credits vary by file size and configuration — use `engine.estimate()` |\n| Free tier | 10 credits/month, no card required |\n| Researcher | $49/month — 500 credits |\n| Team | $199/month — 2000 credits |\n| Credits | $0.10 per credit |\n\nEstimate before running:\n\n```python\nestimate = await engine.estimate(file_size_mb=10.5, num_columns=25, analysis_depth=2, visibility=\"private\")\n# estimate[\"cost\"][\"credits\"] → 55\n# estimate[\"account\"][\"sufficient\"] → True/False\n```\n\nAccount management is fully programmatic — attach payment methods, subscribe to plans, and purchase credits via the SDK or REST API. See [Python SDK reference](docs/python-sdk.md#account-management) or [SKILL.md](SKILL.md#paying-for-credits-programmatic).\n\n---\n\n## Expected data format\n\nDisco expects a **flat table** — columns for features, rows for samples.\n\n```\n| patient_id | age | bmi | smoker | outcome |\n|------------|-----|------|--------|---------|\n| 001 | 52 | 28.3 | yes | 1 |\n| 002 | 34 | 22.1 | no | 0 |\n| ... | ... | ... | ... | ... |\n```\n\n- **One row per observation** — a patient, a sample, a transaction, a measurement, etc.\n- **One column per feature** — numeric, categorical, datetime, or free text are all fine\n- **One target column** — the outcome you want to understand. Must have at least 2 distinct values.\n- **Missing values are OK** — Disco handles them automatically. Don't drop rows or impute beforehand.\n- **No pivoting needed** — if your data is already in a flat table, it's ready to go\n\n**Supported formats:** CSV, TSV, Excel (.xlsx), JSON, Parquet, ARFF, Feather. Max 5 GB.\n\n**Not supported:** images, raw text documents, nested/hierarchical JSON, multi-sheet Excel (use the first sheet or export to CSV)\n\n---\n\n## Compared to other tools\n\n| Goal | Tool |\n|---|---|\n| Summary statistics, data quality | ydata-profiling, sweetviz |\n| Predictive model | AutoML (auto-sklearn, TPOT, H2O) |\n| Quick correlations | pandas, seaborn |\n| Answer a specific question about data | ChatGPT, Claude |\n| **Find what you don't know to look for** | **Disco** |\n\nDisco isn't a replacement for EDA or AutoML — it finds the patterns those tools miss. We [tested 18 data analysis tools](https://www.leap-labs.com/research/the-patterns-that-agents-miss) on a dataset with known ground-truth patterns. Most confidently reported wrong results. Disco was the only one that found every pattern.\n\n---\n\n## Links\n\n- [Dashboard](https://disco.leap-labs.com)\n- [API keys](https://disco.leap-labs.com/developers)\n- [Python SDK on PyPI](https://pypi.org/project/discovery-engine-api/)\n- [Python SDK reference](docs/python-sdk.md)\n- [OpenAPI spec](https://disco.leap-labs.com/.well-known/openapi.json)\n- [Agent / MCP docs](SKILL.md)\n- [LLM-friendly reference](llms.txt)\n- [OpenAPI spec](https://disco.leap-labs.com/.well-known/openapi.json)\n- [OpenAPI spec (in-repo)](docs/openapi.json)\n- [Public reports gallery](https://disco.leap-labs.com/discover)\n\n---\n\nFile v0.2.182:_meta.json\n\n{\n \"ownerId\": \"kn73erxaens3n3z378tewq1rpd81qm39\",\n \"slug\": \"discovery-engine\",\n \"version\": \"0.2.182\",\n \"publishedAt\": 1790374776095\n}\n\nFile v0.2.182:docs/python-sdk.md\n\n# Disco Python SDK\n\nFind novel, statistically validated patterns in tabular data — feature interactions, subgroup effects, and conditional relationships that humans and agents miss.\n\n## Installation\n\n```bash\npip install discovery-engine-api\n```\n\nFor pandas DataFrame support:\n\n```bash\npip install discovery-engine-api[pandas]\n```\n\n## Quick Start\n\n```python\nfrom discovery import Engine\n\nengine = Engine(api_key=\"disco_...\")\n\nresult = await engine.discover(\n file=\"data.csv\",\n target_column=\"outcome\",\n)\n\nfor pattern in result.patterns:\n if pattern.p_value < 0.05 and pattern.novelty_type == \"novel\":\n print(f\"{pattern.description} (p={pattern.p_value:.4f})\")\n\nprint(f\"Full report: {result.report_url}\")\n```\n\nGet your API key from the [Developers page](https://disco.leap-labs.com/developers), or create one programmatically:\n\n### Getting an API Key\n\n`Engine.signup()` and `Engine.login()` are class methods — no instance needed.\n\n```python\n# New account (free tier — 10 credits/month, no card required)\nengine = await Engine.signup(email=\"you@example.com\")\n\n# Existing account (lost your key, new session, etc.)\nengine = await Engine.login(email=\"you@example.com\")\n```\n\nBoth methods send a 6-digit verification code to the email, prompt for it interactively, and return a configured `Engine` instance with a `disco_` API key.\n\n```python\n@classmethod\nasync def signup(cls, email: str, *, name: Optional[str] = None, quiet: bool = False) -> Engine\n```\n- Raises `ValueError` if the email is already registered (409)\n\n```python\n@classmethod\nasync def login(cls, email: str, *, quiet: bool = False) -> Engine\n```\n- Raises `ValueError` if no account exists (404)\n\n**REST API (for automated agents):** If you don't have a terminal for the interactive prompt, use the two-step flow directly:\n\n```\n# Signup\nPOST /api/signup → {\"status\": \"verification_required\"}\nPOST /api/signup/verify → {\"key\": \"disco_...\", \"tier\": \"free_tier\", \"credits\": 10}\n\n# Login\nPOST /api/login → {\"status\": \"verification_required\"}\nPOST /api/login/verify → {\"key\": \"disco_...\", ...}\n```\n\n## Parameters\n\n```python\nawait engine.discover(\n file: str | Path | pd.DataFrame, # Dataset to analyze\n target_column: str, # Column to predict/analyze\n analysis_depth: int = 2, # 2=default, higher=deeper analysis\n visibility: str = \"public\", # \"public\" (free) or \"private\" (credits)\n title: str | None = None, # Dataset title\n description: str | None = None, # Dataset description\n column_descriptions: dict[str, str] | None = None, # Improves pattern explanations\n excluded_columns: list[str] | None = None, # Columns to exclude — see below\n use_llms: bool = False, # LLM explanations, novelty assessment, citations (costs more) — see below\n timeout: float = 1800, # Max seconds to wait\n # Additional kwargs forwarded to run_async():\n # task, author, source_url, timeseries_groups, ...\n)\n```\n\n> **Tip:** Providing `column_descriptions` significantly improves pattern explanations. If your columns have non-obvious names, always describe them.\n\n> **`use_llms`:** Default `False`. Slower and more expensive, but you get smarter pre-processing, literature context and novelty assessment. Set to `True` if you want Disco-generated pattern descriptions, novelty assessment with citations, and report summaries. **Public runs always use LLMs regardless of this setting.** What changes when false: pattern descriptions fall back to generic text, novelty is not assessed (all patterns marked confirmatory, no citations), report summaries are omitted, integer columns with few unique values (e.g. \"month\" 1-12, \"hour\" 0-23) may be misclassified as categorical instead of continuous, and high-cardinality text columns get generic cluster names instead of descriptive ones. Use `engine.estimate()` to check credit cost before running.\n\n> **Visibility:** `\"public\"` runs are free but results are published, and analysis depth is locked to 2. `\"private\"` runs keep results confidential and consume credits.\n\n> **`excluded_columns`:** Always exclude identifiers (row IDs, UUIDs), data leakage (target renamed/reformatted), and tautological columns (alternative encodings of the same construct as the target). For example, if your target is `serious`, exclude `serious_outcome`, `not_serious`, `death` — they're part of the same classification system. See [SKILL.md](../SKILL.md#preparing-your-data) for full guidance.\n\n\n## Examples\n\n### Working with Pandas DataFrames\n\n```python\nimport pandas as pd\nfrom discovery import Engine\n\ndf = pd.read_csv(\"data.csv\")\n\nengine = Engine(api_key=\"disco_...\")\nresult = await engine.discover(\n file=df,\n target_column=\"outcome\",\n column_descriptions={\n \"age\": \"Patient age in years\",\n \"bmi\": \"Body mass index\",\n },\n excluded_columns=[\"patient_id\", \"timestamp\", \"outcome_text\"], # IDs + tautological\n)\n```\n\n### Running in the Background\n\nRuns take a few minutes. While waiting, the SDK logs progress automatically:\n\n```\nWaiting for run abc123 to complete...\n Status: waiting (position 2 in queue) | Est. wait: ~8 min | Upgrade at disco.leap-labs.com/account for priority processing\n Status: processing (preprocessing — Processing data...) | Elapsed: 34.2s | ETA: ~6 min\n Status: processing (training — Modelling data...) | Elapsed: 98.7s | ETA: ~4 min\n Status: processing (interpreting — Extracting patterns...) | Elapsed: 284.1s | ETA: ~2 min\n Status: processing (reporting — Building report...) | Elapsed: 412.3s | ETA: ~1 min\nRun completed in 467.8s\n```\n\nIf you need to do other work while Disco runs:\n\n```python\nimport asyncio\nfrom discovery import Engine\n\nasync def main():\n async with Engine(api_key=\"disco_...\") as engine:\n # Submit without waiting\n run = await engine.run_async(\n file=\"data.csv\",\n target_column=\"outcome\",\n wait=False,\n )\n print(f\"Submitted run {run.run_id}, continuing...\")\n\n # ... do other work ...\n\n # Check back later\n result = await engine.wait_for_completion(run.run_id, timeout=1800)\n return result\n\nresult = asyncio.run(main())\n```\n\n### Inspecting Columns Before Running\n\nIf you need to see the dataset's columns before choosing a target column — e.g., when column names are not obvious — upload first, inspect, then run without re-uploading:\n\n```python\n# Upload once and get the server's parsed column list\nupload = await engine.upload_file(file=\"data.csv\", title=\"My dataset\")\n# upload[\"file\"] -> {\"key\": \"uploads/abc123.csv\", \"name\": \"data.csv\",\n# \"size\": 1048576, \"fileHash\": \"sha256:...\"}\n# upload[\"columns\"] -> [{\"name\": \"col1\", \"type\": \"continuous\", ...}, ...]\n# upload[\"rowCount\"] -> 5000\nprint(upload[\"columns\"])\nprint(upload[\"rowCount\"])\n\n# Pass the result to avoid re-uploading\nresult = await engine.run_async(\n file=\"data.csv\",\n target_column=\"col1\",\n wait=True,\n upload_result=upload, # skips the upload step\n)\n```\n\n### Synchronous Usage\n\nFor scripts and Jupyter notebooks:\n\n```python\nfrom discovery import Engine\n\nengine = Engine(api_key=\"disco_...\")\n\n# Simple — wraps discover(), always waits for completion\nresult = engine.discover_sync(\n file=\"data.csv\",\n target_column=\"outcome\",\n)\n\n# More control — wraps run_async(), supports wait=False\nresult = engine.run(\n file=\"data.csv\",\n target_column=\"outcome\",\n wait=True,\n)\n```\n\nFor Jupyter notebooks, install the jupyter extra for `engine.run()` compatibility:\n\n```bash\npip install discovery-engine-api[jupyter]\n```\n\nOr use `await engine.discover(...)` / `await engine.run_async(...)` directly in async notebook cells.\n\n\n## Working with Results\n\n```python\n# Filter for significant novel patterns\nnovel = [p for p in result.patterns\n if p.p_value < 0.05 and p.novelty_type == \"novel\"]\n\n# Get patterns that increase the target\nincreasing = [p for p in result.patterns if p.target_change_direction == \"max\"]\n\n# Inspect conditions\nfor pattern in result.patterns:\n for cond in pattern.conditions:\n print(f\" {cond['feature']}: {cond}\")\n\n# Feature importance\nif result.feature_importance:\n top = sorted(result.feature_importance.scores,\n key=lambda s: abs(s.score), reverse=True)\n\n# Share the interactive report\nprint(f\"Explore: {result.report_url}\")\n```\n\n\n## Credits and Pricing\n\n- **Public runs**: Free. Results published to public gallery. Locked to depth=2.\n- **Private runs**: Credits scale with file size, depth, and run configuration. $0.10 per credit. Use `engine.estimate()` to check cost before running.\n\n```python\n# Estimate cost before running\nestimate = await engine.estimate(\n file_size_mb=10.5,\n num_columns=25,\n analysis_depth=2,\n visibility=\"private\",\n)\n# estimate[\"cost\"][\"credits\"] -> 55\n# estimate[\"cost\"][\"price_usd\"] -> 5.5\n# estimate[\"limits\"][\"max_file_size_mb\"] -> 5120\n# estimate[\"limits\"][\"max_analysis_depth\"] -> 23 (num_columns - 2)\n# estimate[\"limits\"][\"supported_formats\"] -> [\"csv\", \"parquet\", ...]\n# estimate[\"account\"][\"available_credits\"] -> 60 (only if authenticated)\n# estimate[\"account\"][\"sufficient\"] -> True/False\n```\n\nManage credits and plans at [disco.leap-labs.com/account](https://disco.leap-labs.com/account).\n\n\n## Account Management\n\n```python\n# Check your account — plan, credits, payment method\naccount = await engine.get_account()\n# account[\"plan\"][\"tier\"] -> \"free_tier\"\n# account[\"plan\"][\"name\"] -> \"Explorer\"\n# account[\"plan\"][\"monthly_credits\"] -> 10\n# account[\"credits\"][\"subscription\"] -> 10\n# account[\"credits\"][\"purchased\"] -> 0\n# account[\"credits\"][\"total\"] -> 10\n# account[\"payment_method\"][\"on_file\"] -> False\n# account[\"stripe_publishable_key\"] -> \"pk_live_...\"\n\n# Attach a payment method (Stripe PaymentMethod ID — see below)\nresult = await engine.add_payment_method(\"pm_...\")\n# result[\"payment_method_attached\"] -> True\n# result[\"card_brand\"] -> \"visa\"\n# result[\"card_last4\"] -> \"4242\"\n\n# Subscribe to a plan\nresult = await engine.subscribe(\"tier_1\")\n# Plans: \"free_tier\" ($0, 10 cr/mo), \"tier_1\" ($49, 500 cr/mo), \"tier_2\" ($199, 2000 cr/mo)\n# result[\"plan\"] -> \"tier_1\"\n# result[\"price_usd\"] -> 49\n# result[\"monthly_credits\"] -> 500\n\n# Purchase credit packs (100 credits per pack, $10/pack)\nresult = await engine.purchase_credits(packs=1)\n# result[\"purchased_credits\"] -> 100\n# result[\"total_credits\"] -> 110\n# result[\"charge_amount_usd\"] -> 10.0\n\n# Revert to free tier\nresult = await engine.subscribe(\"free_tier\")\n```\n\n### Stripe Card Tokenization\n\n`add_payment_method()` requires a Stripe `pm_...` token. Card data goes directly to Stripe — Disco never sees it.\n\n```python\nimport requests\n\naccount = await engine.get_account()\npk = account[\"stripe_publishable_key\"]\n\npm = requests.post(\n \"https://api.stripe.com/v1/payment_methods\",\n auth=(pk, \"\"),\n data={\n \"type\": \"card\",\n \"card[number]\": \"4242424242424242\",\n \"card[exp_month]\": \"12\",\n \"card[exp_year]\": \"2028\",\n \"card[cvc]\": \"123\",\n },\n).json()\n\nawait engine.add_payment_method(pm[\"id\"])\n```\n\nREST equivalents for all account endpoints are documented in [SKILL.md](../SKILL.md#paying-for-credits-programmatic).\n\n\n## Expected Data Format\n\nDisco expects a **flat table** — columns for features, rows for samples.\n\n- **One row per observation** — a patient, a sample, a transaction, a measurement, etc.\n- **One column per feature** — numeric, categorical, datetime, or free text are all fine\n- **One target column** — the outcome to analyze. Must have at least 2 distinct values.\n- **Missing values are OK** — Disco handles them automatically. Don't drop rows or impute beforehand.\n\nNot supported: images, raw text documents, nested/hierarchical JSON, multi-sheet Excel (use the first sheet or export to CSV).\n\n## File Size Limits\n\nUploads up to **5 GB**. Files are uploaded directly to cloud storage using presigned URLs.\n\nSupported formats: **CSV**, **TSV**, **Excel (.xlsx)**, **JSON**, **Parquet**, **ARFF**, **Feather**.\n\n### Direct Upload\n\nFor small files, skip the 3-step presign flow and upload inline with base64:\n\n```\nPOST /api/data/upload/direct\nAuthorization: Bearer disco_...\n{\"fileName\": \"data.csv\", \"content\": \"\"}\n→ {\"ok\": true, \"file\": {...}, \"columns\": [...], \"rowCount\": 5000}\n```\n\nFor large files, use presigned uploads or the SDK (`engine.upload_file()`).\n\n\n## Return Value\n\n### EngineResult\n\n```python\n@dataclass\nclass EngineResult:\n run_id: str\n report_id: str | None # Report UUID (used in report_url)\n status: str # \"pending\", \"processing\", \"completed\", \"failed\"\n dataset_title: str | None # Title of the dataset\n dataset_description: str | None # Description of the dataset\n total_rows: int | None\n target_column: str | None # Column being predicted/analyzed\n task: str | None # \"regression\", \"binary_classification\", \"multiclass_classification\"\n summary: Summary | None # LLM-generated insights\n patterns: list[Pattern] # Discovered patterns (the core output)\n columns: list[Column] # Feature info and statistics\n correlation_matrix: list[CorrelationEntry] # Feature correlations\n feature_importance: FeatureImportance | None # Global importance scores\n job_id: str | None # Job ID for tracking\n job_status: str | None # Job queue status\n queue_position: int | None # Position in queue when pending (1 = next up)\n current_step: str | None # Active pipeline step (preprocessing, training, interpreting, reporting)\n current_step_message: str | None # Human-readable description of the current step\n estimated_wait_seconds: int | None # Estimated queue wait time in seconds (pending only)\n error_message: str | None\n report_url: str | None # Shareable link to interactive web report\n dashboard_urls: dict[str, dict[str, str]] | None # Direct links to report sections (summary, patterns, territory, features)\n hints: list[str] # Upgrade hints (non-empty for free-tier users with hidden patterns)\n hidden_deep_count: int # Patterns hidden for free-tier accounts (upgrade to see all)\n hidden_deep_novel_count: int # Novel patterns hidden for free-tier accounts\n```\n\n### Pattern\n\n```python\n@dataclass\nclass Pattern:\n id: str\n task: str # \"regression\", \"binary_classification\", \"multiclass_classification\"\n target_column: str # Column being analyzed\n description: str # Human-readable description\n conditions: list[dict] # Conditions defining the pattern\n p_value: float # FDR-adjusted p-value\n p_value_raw: float | None # Raw p-value before adjustment\n novelty_type: str # \"novel\" or \"confirmatory\"\n novelty_explanation: str # Why this is novel or confirmatory\n citations: list[dict] # Academic citations\n target_change_direction: str # \"max\" (increases target) or \"min\" (decreases)\n abs_target_change: float # Magnitude of effect\n target_score: float # Mean target value (regression) or class fraction (classification) in the subgroup\n support_count: int # Rows matching this pattern\n support_percentage: float # Percentage of dataset\n target_class: str | None # For classification tasks\n target_mean: float | None # For regression tasks\n target_std: float | None\n```\n\n#### Pattern Conditions\n\nEach condition in `pattern.conditions` is a dict with a `type` field:\n\n**Continuous condition** — a numeric range:\n```python\n{\n \"type\": \"continuous\",\n \"feature\": \"age\",\n \"min_value\": 45.0,\n \"max_value\": 65.0,\n \"min_q\": 0.35, # quantile of min_value\n \"max_q\": 0.72 # quantile of max_value\n}\n```\n\n**Categorical condition** — a set of values:\n```python\n{\n \"type\": \"categorical\",\n \"feature\": \"region\",\n \"values\": [\"north\", \"east\"]\n}\n```\n\n**Datetime condition** — a time range:\n```python\n{\n \"type\": \"datetime\",\n \"feature\": \"date\",\n \"min_value\": 1609459200000, # epoch ms\n \"max_value\": 1640995200000,\n \"min_datetime\": \"2021-01-01\", # human-readable\n \"max_datetime\": \"2022-01-01\"\n}\n```\n\n### PatternGroup\n\n```python\n@dataclass\nclass PatternGroup:\n pattern_ids: list[str] # IDs of patterns in this group\n explanation: str # Why these patterns are grouped\n```\n\n### Summary\n\n```python\n@dataclass\nclass Summary:\n overview: str # High-level summary of findings\n key_insights: list[str] # Main takeaways\n novel_patterns: PatternGroup # Novel pattern IDs and explanation\n selected_pattern_id: str | None # ID of the highlighted/featured pattern\n```\n\n### CorrelationEntry\n\n```python\n@dataclass\nclass CorrelationEntry:\n feature_x: str\n feature_y: str\n value: float\n```\n\n### Column\n\n```python\n@dataclass\nclass Column:\n id: str\n name: str\n display_name: str\n type: str # \"continuous\" or \"categorical\"\n data_type: str # \"int\", \"float\", \"string\", \"boolean\", \"datetime\"\n enabled: bool\n description: str | None\n mean: float | None\n median: float | None\n std: float | None\n min: float | None\n max: float | None\n iqr_min: float | None # 25th percentile\n iqr_max: float | None # 75th percentile\n mode: str | None # Most common value (categorical columns)\n approx_unique: int | None # Approximate distinct value count\n null_percentage: float | None\n feature_importance_score: float | None # Signed importance score\n```\n\n### FeatureImportance\n\nScores are **signed** — positive means the feature increases the prediction, negative means it decreases it.\n\n```python\n@dataclass\nclass FeatureImportance:\n kind: str # \"global\" | \"local\"\n baseline: float # Baseline model output\n scores: list[FeatureImportanceScore]\n\n@dataclass\nclass FeatureImportanceScore:\n feature: str\n score: float # Signed importance score\n```\n\n\n## Error Handling\n\n```python\nfrom discovery import Engine\nfrom discovery.errors import (\n AuthenticationError,\n InsufficientCreditsError,\n RateLimitError,\n RunFailedError,\n RunNotFoundError,\n PaymentRequiredError,\n)\n\ntry:\n result = await engine.discover(file=\"data.csv\", target_column=\"target\")\nexcept AuthenticationError as e:\n print(e.suggestion) # \"Check your API key at https://disco.leap-labs.com/developers\"\nexcept InsufficientCreditsError as e:\n print(f\"Need {e.credits_required}, have {e.credits_available}\")\n print(e.suggestion) # \"Run with visibility='public' (free, results published) or purchase credits with engine.purchase_credits().\"\nexcept RateLimitError as e:\n print(f\"Retry after {e.retry_after} seconds\")\nexcept RunFailedError as e:\n print(f\"Run {e.run_id} failed: {e}\")\nexcept RunNotFoundError as e:\n print(f\"Run {e.run_id} not found — may have been cleaned up\")\nexcept PaymentRequiredError as e:\n print(e.suggestion) # \"Attach a payment method with engine.add_payment_method(...)\"\nexcept TimeoutError:\n pass # Retrieve later with engine.wait_for_completion(run_id)\n```\n\nAll errors include a `suggestion` field with actionable instructions.\n\n\n## MCP Server\n\nDisco is available as an [MCP server](https://disco.leap-labs.com/.well-known/mcp.json) with tools for the full discovery lifecycle — estimate, analyze, check status, get results, manage account. To subscribe or purchase credits via MCP, call `discovery_add_payment_method` first to attach a Stripe payment method.\n\n```json\n{\n \"mcpServers\": {\n \"discovery-engine\": {\n \"url\": \"https://disco.leap-labs.com/mcp\",\n \"env\": { \"DISCOVERY_API_KEY\": \"disco_...\" }\n }\n }\n}\n```\n\n## Links\n\n- **PyPI**: [discovery-engine-api](https://pypi.org/project/discovery-engine-api/)\n- **API keys**: [disco.leap-labs.com/developers](https://disco.leap-labs.com/developers)\n- **LLM-friendly docs**: [disco.leap-labs.com/llms-full.txt](https://disco.leap-labs.com/llms-full.txt)\n- **MCP manifest**: [disco.leap-labs.com/.well-known/mcp.json](https://disco.leap-labs.com/.well-known/mcp.json)\n- **Credits & billing**: [disco.leap-labs.com/account](https://disco.leap-labs.com/account)\n- **Public reports**: [disco.leap-labs.com/discover](https://disco.leap-labs.com/discover)\n\nFile v0.2.182:skill-card.md\n\n## Description:\n\nHelps agents find and explain statistically validated patterns in tabular data, including subgroup effects, feature interactions, and literature-backed novelty assessments.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[jessicarumbelow](https://clawhub.ai/user/jessicarumbelow)\n\n### License/Terms of Use:\n\nMIT\n\n## Use Case:\n\nDevelopers and data analysts use this skill to analyze tabular datasets with Disco and communicate validated patterns, effect sizes, and supporting citations.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: An analysis may publish results publicly by default, exposing sensitive dataset insights.\n\nMitigation: Confirm visibility before every run and explicitly select private visibility for confidential data.\n\nRisk: Selected datasets are sent to Disco's hosted service for analysis.\n\nMitigation: Only submit data approved for upload to that service and review its sensitivity first.\n\nRisk: A payment-enabled API key can permit autonomous purchases, subscriptions, or payment-method changes without a hard confirmation gate.\n\nMitigation: Do not expose a payment-enabled key to autonomous workflows unless human approval is enforced for billing actions.\n\n## Reference(s):\n\n- [Discovery skill release](https://clawhub.ai/jessicarumbelow/skills/discovery-engine)\n- [Disco documentation](https://disco.leap-labs.com/llms-full.txt)\n- [Python SDK reference](docs/python-sdk.md)\n- [OpenAPI specification](docs/openapi.json)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Guidance]\n\n**Output Format:** [Markdown summaries with structured pattern details and report links]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May include pattern conditions, effect sizes, adjusted p-values, literature citations, and novelty classifications.]\n\n## Skill Version(s):\n\n0.2.182 (source: server-resolved ClawHub release)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v0.2.182:submissions/awesome-data-science.md\n\n# awesome-datascience PR\n\n**Repo:** academic/awesome-datascience (29k stars)\n**Where to add:** Under an existing tools section\n\n## Entry\n\n```markdown\n- [Disco](https://github.com/leap-laboratories/discovery-engine) - Superhuman exploratory data analysis. Finds the feature interactions and subgroup effects in tabular data that LLMs and manual exploration miss — with p-values, effect sizes, and literature citations. Free for public data.\n```\n\n## PR title\n\n```\nAdd Disco — automated pattern discovery for tabular data\n```\n\n## PR body\n\n```markdown\nAdds [Disco](https://github.com/leap-laboratories/discovery-engine) by [Leap Laboratories](https://www.leap-labs.com).\n\nSystematic, automated, unbiased discovery from any tabular dataset. Give it a dataset and a target column and it finds meaningful patterns – including those you'd never think to look for. You get specific combinations of conditions with exact thresholds that change the target. Every finding is validated on hold-out data with FDR-corrected p-values and optionally checked against academic literature, so you can see what's known and what's genuinely new.\n\nNo hypotheses required. No ML expertise required. No hallucinations. Pure data-first pattern finding.\n\nAlready used to make novel discoveries in multiple fields — from [plant biology, to immunology, to meteorology](https://www.leap-labs.com/blog/new-publications-in-immunology-plant-biology-and-meteorology).\n\nAvailable as a Python SDK (`pip install discovery-engine-api`), MCP server, and REST API. Free for public data, no card required.\n```\n\nFile v0.2.182:submissions/awesome-machine-learning.md\n\n# awesome-machine-learning PR\n\n**Repo:** josephmisiti/awesome-machine-learning (72k stars)\n**Where to add:** Python → General-Purpose Machine Learning\n\n## Entry\n\n```markdown\n* [Disco](https://github.com/leap-laboratories/discovery-engine) - Superhuman exploratory data analysis. Finds the feature interactions and subgroup effects in tabular data that LLMs and manual exploration miss — with p-values, effect sizes, and literature citations. Free for public data.\n```\n\n## PR title\n\n```\nAdd Disco — automated pattern discovery for tabular data\n```\n\n## PR body\n\n```markdown\nAdds [Disco](https://github.com/leap-laboratories/discovery-engine) by [Leap Laboratories](https://www.leap-labs.com).\n\nSystematic, automated, unbiased discovery from any tabular dataset. Give it a dataset and a target column and it finds meaningful patterns – including those you'd never think to look for. You get specific combinations of conditions with exact thresholds that change the target. Every finding is validated on hold-out data with FDR-corrected p-values and optionally checked against academic literature, so you can see what's known and what's genuinely new.\n\nNo hypotheses required. No ML expertise required. No hallucinations. Pure data-first pattern finding.\n\nAlready used to make novel discoveries in multiple fields — from [plant biology, to immunology, to meteorology](https://www.leap-labs.com/blog/new-publications-in-immunology-plant-biology-and-meteorology).\n\nAvailable as a Python SDK (`pip install discovery-engine-api`), MCP server, and REST API. Free for public data, no card required.\n```\n\nFile v0.2.182:submissions/awesome-mcp-servers.md\n\n# awesome-mcp-servers PR\n\n**Repo:** punkpeye/awesome-mcp-servers (84k stars)\n**Where to add:** Alphabetically in the relevant category section\n\n## Entry\n\n```markdown\n- [Disco](https://github.com/leap-laboratories/discovery-engine) [![smithery badge](https://smithery.ai/badge/discovery-engine)](https://smithery.ai/server/discovery-engine) 🐍 ☁️ - Superhuman exploratory data analysis that finds the feature interactions and subgroup effects that LLMs and manual exploration miss — with p-values, effect sizes, and literature citations. Data goes in, validated insights come out. Free for public data.\n```\n\n## PR title\n\n```\n🤖🤖🤖 Add Disco — automated scientific discovery from tabular data\n```\n\n## PR body\n\n```markdown\nAdds [Disco](https://github.com/leap-laboratories/discovery-engine) by [Leap Laboratories](https://www.leap-labs.com).\n\nSystematic, automated, unbiased discovery from any tabular dataset. Give it a dataset and a target column and it finds meaningful patterns – including those you'd never think to look for. You get specific combinations of conditions with exact thresholds that change the target. Every finding is validated on hold-out data with FDR-corrected p-values and optionally checked against academic literature, so you can see what's known and what's genuinely new.\n\nNo hypotheses required. No ML expertise required. No hallucinations. Pure data-first pattern finding.\n\nIt's already been used to make novel discoveries in multiple fields — from [plant biology, to immunology, to meteorology](https://www.leap-labs.com/blog/new-publications-in-immunology-plant-biology-and-meteorology).\n\nRemote MCP server at `https://disco.leap-labs.com/mcp` — no install required. Also available as a Python SDK (`pip install discovery-engine-api`) and REST API. Free for public data, no card required.\n```\n\nFile v0.2.182:submissions/awesome-python-data-science.md\n\n# awesome-python-data-science PR\n\n**Repo:** krzjoa/awesome-python-data-science (3.4k stars)\n**Where to add:** Machine Learning → General Purpose Machine Learning\n\n## Entry\n\n```markdown\n* [Disco](https://github.com/leap-laboratories/discovery-engine) - Superhuman exploratory data analysis. Finds the feature interactions and subgroup effects in tabular data that LLMs and manual exploration miss — with p-values, effect sizes, and literature citations. Free for public data.\n```\n\n## PR title\n\n```\nAdd Disco — automated pattern discovery for tabular data\n```\n\n## PR body\n\n```markdown\nAdds [Disco](https://github.com/leap-laboratories/discovery-engine) by [Leap Laboratories](https://www.leap-labs.com).\n\nSystematic, automated, unbiased discovery from any tabular dataset. Give it a dataset and a target column and it finds meaningful patterns – including those you'd never think to look for. You get specific combinations of conditions with exact thresholds that change the target. Every finding is validated on hold-out data with FDR-corrected p-values and optionally checked against academic literature, so you can see what's known and what's genuinely new.\n\nNo hypotheses required. No ML expertise required. No hallucinations. Pure data-first pattern finding.\n\nAlready used to make novel discoveries in multiple fields — from [plant biology, to immunology, to meteorology](https://www.leap-labs.com/blog/new-publications-in-immunology-plant-biology-and-meteorology).\n\n`pip install discovery-engine-api` — free for public data, no card required.\n```\n\nFile v0.2.182:submissions/chatgpt-gpt.md\n\n# ChatGPT GPT Configuration\n\nCreate this in the GPT Builder at https://chat.openai.com/gpts/editor\n\n## Name\n\nDisco — Data Discovery Engine\n\n## Description\n\nSuperhuman exploratory data analysis. Upload a dataset, pick a target column, get back statistically validated patterns — feature interactions, subgroup effects, and conditional relationships with p-values, effect sizes, and literature citations. Free for public data.\n\n## Instructions (System Prompt)\n\n```\nYou are Disco, an automated scientific discovery engine made by Leap Laboratories.\n\nYour job is to help users discover meaningful patterns in their tabular data — the kind of feature interactions and subgroup effects that correlation analysis, LLMs, and manual exploration miss.\n\n## How you work\n\n1. The user provides a tabular dataset (CSV, Excel, Parquet, JSON, etc.) and tells you which column is the target — the outcome they want to understand.\n2. You upload the data to Disco via the API, which trains ML models on the data, extracts interpretable patterns, validates each on hold-out data with FDR-corrected p-values, and optionally checks findings against academic literature.\n3. You return the results — structured patterns with conditions, effect sizes, p-values, citations, and novelty classification.\n\n## Key behaviours\n\n- Before running, help the user identify which column is the target and whether any columns should be excluded (IDs, data leakage, tautological columns — see data prep guidance below).\n- Use the estimate endpoint first to show the user how many credits the run will cost and how long it will take.\n- For public runs (free), set visibility=\"public\". For private data, set visibility=\"private\" (costs credits).\n- Runs take a few minutes. Poll the status endpoint and keep the user updated on progress.\n- When results arrive, present the most interesting patterns first — prioritise novel findings over confirmatory ones. Explain what each pattern means in plain language, including the specific conditions and thresholds.\n- If the user asks about a specific pattern, help them interpret it in context.\n\n## Data preparation guidance\n\nBefore running, the user should exclude:\n1. Identifiers — row IDs, UUIDs, patient IDs, sample codes\n2. Data leakage — the target column renamed or reformatted\n3. Tautological columns — alternative encodings of the same construct as the target (e.g., if target is \"serious\", exclude \"serious_outcome\", \"not_serious\", \"death\")\n\n## Pricing\n- Public runs: Free (results are published)\n- Private runs: Credits vary by file size and configuration\n- Free tier: 10 credits/month, no card required\n\n## Important\n- Never fabricate patterns or results. Only report what Disco actually returns.\n- If a run fails, explain why and help the user fix the issue (usually data formatting).\n- You are direct, clear, and helpful. No hype, no hedging.\n```\n\n## Actions (OpenAPI)\n\nImport the OpenAPI spec from:\n```\nhttps://disco.leap-labs.com/.well-known/openapi.json\n```\n\n## Authentication\n\n- Auth type: API Key\n- Header: Authorization\n- Format: Bearer disco_...\n\n## Conversation starters\n\n- \"Give me a dataset and a target column — I'll find what you'd never think to look for\"\n\n## Profile picture\n\nUse the Disco logo from the repo: `logo.png`\n\nFile v0.2.182:submissions/mcp-registries.md\n\n# MCP Registry Submissions\n\nMost registries just need the GitHub URL and pull metadata from the repo automatically. The copy below is for any that ask for a description.\n\n## Reusable description\n\n> Superhuman exploratory data analysis. Disco automatically finds the feature interactions and subgroup effects in tabular data that LLMs and manual exploration miss — with p-values, effect sizes, and literature citations. Data goes in, validated insights come out. Already used to make novel scientific discoveries in plant biology, immunology, and meteorology. Free for public data.\n\n## Registry checklist\n\n### PulseMCP\n- **URL:** https://www.pulsemcp.com/submit\n- **Process:** Web form — just paste the GitHub URL. They also auto-ingest from the Official MCP Registry weekly.\n- **Submit:** https://github.com/leap-laboratories/discovery-engine\n- **Status:** [ ] Submitted\n\n### mcp.so\n- **URL:** https://mcp.so\n- **Process:** Couldn't access (403). Likely auto-indexes from GitHub or has a submit form on the site. Check manually.\n- **Status:** [ ] Check if already listed, submit if not\n\n### mcphub.io\n- **URL:** https://mcphub.io\n- **Process:** JS-rendered site, couldn't determine submission process. Check manually — may auto-index or have a form.\n- **Status:** [ ] Check if already listed, submit if not\n\n### OpenTools\n- **URL:** https://opentools.com\n- **Process:** No public submission form. Options: \"Talk to the team\" via Calendly link on site, or check their GitHub/Discord.\n- **Status:** [ ] Reach out\n\n### Composio\n- **URL:** https://composio.dev\n- **Process:** Composio is more of a tool integration platform than a directory. May not have an open submission process. Worth checking if they have an MCP directory.\n- **Status:** [ ] Check if relevant\n\n### Official MCP Registry\n- **URL:** Check if there's a central registry that PulseMCP and others ingest from (possibly modelcontextprotocol.io or similar)\n- **Status:** [ ] Investigate — getting listed here may auto-propagate to multiple registries\n\nFile v0.2.182:docs/openapi.json\n\n{\n \"openapi\": \"3.1.0\",\n \"info\": {\n \"title\": \"Disco API\",\n \"description\": \"Disco finds novel, statistically validated patterns in tabular data that correlation analysis, LLMs, and hypothesis-driven approaches miss. Each pattern is validated on hold-out data with FDR-corrected p-values and checked against academic literature with citations.\\n\\n## Agent workflow\\n\\n**Step 1 \\u2014 Get an API key** (once):\\n- `POST /api/signup` with `{\\\"email\\\": \\\"...\\\"}` \\u2192 sends a 6-digit code\\n- `POST /api/signup/verify` with `{\\\"email\\\": \\\"...\\\", \\\"code\\\": \\\"...\\\"}` \\u2192 returns `{\\\"key\\\": \\\"disco_...\\\"}`\\n- Use this key as `Authorization: Bearer disco_...` for all subsequent requests\\n\\n**Step 2 \\u2014 Estimate cost** (optional but recommended):\\n- `POST /api/estimate` with file size, column count, depth, visibility\\n- Returns credit cost and time estimate\\n\\n**Step 3 \\u2014 Upload data** (pick one):\\n- *From URL:* `POST /api/data/upload/from-url` with `{\\\"url\\\": \\\"https://...\\\"}` \\u2192 returns `file` and `columns`\\n- *Direct (base64):* `POST /api/data/upload/direct` with `{\\\"fileName\\\": \\\"data.csv\\\", \\\"content\\\": \\\"\\\"}` \\u2192 returns `file` and `columns`. Max 50MB.\\n- *Presign flow (for large files):* `POST /api/data/upload/presign` \\u2192 PUT file to `uploadUrl` \\u2192 `POST /api/data/upload/finalize`\\n\\n**Step 4 \\u2014 Run analysis:**\\n- `POST /api/run-analysis` with `file`, `columns`, and `targetColumn` from step 3 \\u2192 returns `{\\\"run_id\\\": \\\"...\\\"}`\\n\\n**Step 5 \\u2014 Poll for results:**\\n- `GET /api/runs/{run_id}/results` \\u2014 poll every 10s until `status` is `\\\"completed\\\"` (time varies with dataset size and depth)\\n- Response includes `patterns[]` with conditions, p-values, effect sizes, novelty scores, and citations\\n\\n## Billing\\n\\nPublic runs are free. If the user needs more credits or a paid plan, send them to https://disco.leap-labs.com/account to upgrade.\\n\\n## Authentication\\n\\n`disco_` API keys via `Authorization: Bearer disco_...` header. Get a key via signup or at https://disco.leap-labs.com/developers.\\n\\n## SDK\\n\\n`pip install discovery-engine-api` \\u2014 [Full docs](https://disco.leap-labs.com/llms-full.txt)\",\n \"version\": \"1.0.0\"\n },\n \"servers\": [\n {\n \"url\": \"https://disco.leap-labs.com\"\n }\n ],\n \"paths\": {\n \"/api/runs/{run_id}/results\": {\n \"get\": {\n \"tags\": [\n \"results\"\n ],\n \"summary\": \"Get complete analysis results\",\n \"description\": \"Returns analysis results including patterns, p-values, novelty scores, and citations. Poll until status is \\\"completed\\\". If \\\"failed\\\", check error_message.\",\n \"operationId\": \"get_results\",\n \"parameters\": [\n {\n \"name\": \"run_id\",\n \"in\": \"path\",\n \"required\": true,\n \"schema\": {\n \"type\": \"string\",\n \"format\": \"uuid\",\n \"description\": \"The ID of the run\",\n \"title\": \"Run Id\"\n },\n \"description\": \"The ID of the run\"\n }\n ],\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/ResultsResponse\"\n }\n }\n }\n },\n \"422\": {\n \"description\": \"Validation Error\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/HTTPValidationError\"\n }\n }\n }\n }\n }\n }\n },\n \"/api/api-keys\": {\n \"get\": {\n \"tags\": [\n \"api-keys\"\n ],\n \"summary\": \"List API keys\",\n \"description\": \"List all API keys for the current user. Key values are not returned \\u2014 only metadata.\",\n \"operationId\": \"list_api_keys\",\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"items\": {\n \"$ref\": \"#/components/schemas/ApiKeyInfo\"\n },\n \"type\": \"array\",\n \"title\": \"Response List Api Keys V1 Api Keys Get\"\n }\n }\n }\n }\n }\n }\n },\n \"/api/api-keys/{key_id}\": {\n \"delete\": {\n \"tags\": [\n \"api-keys\"\n ],\n \"summary\": \"Delete API key\",\n \"description\": \"Permanently revoke an API key. This action cannot be undone.\",\n \"operationId\": \"delete_api_key\",\n \"parameters\": [\n {\n \"name\": \"key_id\",\n \"in\": \"path\",\n \"required\": true,\n \"schema\": {\n \"type\": \"string\",\n \"format\": \"uuid\",\n \"description\": \"The ID of the API key to delete\",\n \"title\": \"Key Id\"\n },\n \"description\": \"The ID of the API key to delete\"\n }\n ],\n \"responses\": {\n \"204\": {\n \"description\": \"Successful Response\"\n },\n \"422\": {\n \"description\": \"Validation Error\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/HTTPValidationError\"\n }\n }\n }\n }\n }\n }\n },\n \"/api/me\": {\n \"get\": {\n \"tags\": [\n \"me\"\n ],\n \"summary\": \"Get current user\",\n \"description\": \"Returns the current authenticated user's profile and session information.\",\n \"operationId\": \"get_me\",\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {}\n }\n }\n }\n }\n }\n },\n \"/api/me/organizations\": {\n \"get\": {\n \"tags\": [\n \"me\"\n ],\n \"summary\": \"List my organizations\",\n \"description\": \"Returns all organizations the current user belongs to. Use the returned `id` values as the `X-Organization-ID` header for endpoints that require it.\",\n \"operationId\": \"get_my_organizations\",\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {}\n }\n }\n }\n }\n }\n },\n \"/api/me/runs\": {\n \"get\": {\n \"tags\": [\n \"me\"\n ],\n \"summary\": \"List my runs\",\n \"description\": \"Returns all analysis runs for the current user with their current status, job info, and report links. Use this to monitor running analyses or find past results.\",\n \"operationId\": \"get_my_runs\",\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {}\n }\n }\n }\n }\n }\n },\n \"/api/plans\": {\n \"get\": {\n \"tags\": [\n \"plans\"\n ],\n \"summary\": \"List available plans and pricing\",\n \"description\": \"Public endpoint \\u2014 no authentication required. Returns all available subscription plans with pricing, credit allowances, and feature lists. Credits cost $0.10 each. Credit packs of 100 ($10) are available on all plans. Use this to show users their options before they sign up or upgrade.\",\n \"operationId\": \"list_plans\",\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/PlansResponse\"\n }\n }\n }\n }\n }\n }\n },\n \"/api/estimate\": {\n \"post\": {\n \"tags\": [\n \"estimate\"\n ],\n \"summary\": \"Estimate credit cost for an analysis\",\n \"description\": \"Estimate the credit cost of an analysis before running it. Returns cost, limits, and (if authenticated) whether you have sufficient credits. No auth required.\",\n \"operationId\": \"estimate\",\n \"requestBody\": {\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/EstimateRequest\"\n }\n }\n },\n \"required\": true\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/EstimateResponse\"\n }\n }\n }\n },\n \"422\": {\n \"description\": \"Validation Error\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/HTTPValidationError\"\n }\n }\n }\n }\n }\n }\n },\n \"/api/account\": {\n \"get\": {\n \"tags\": [\n \"account\"\n ],\n \"summary\": \"Get account status\",\n \"description\": \"Returns current plan, available credits (subscription + purchased), payment method status, and Stripe identifiers. Use this to check credit balance before running a private analysis.\",\n \"operationId\": \"get_account\",\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/AccountResponse\"\n }\n }\n }\n }\n }\n }\n },\n \"/api/account/credits/purchase\": {\n \"post\": {\n \"tags\": [\n \"account\"\n ],\n \"summary\": \"Purchase credit packs\",\n \"description\": \"Purchase Disco credits using a stored payment method. Credits cost $0.10 each, sold in packs of 100 ($10/pack). Requires a payment method on file (attach one via `POST /api/account/payment-method`). Returns 402 if no payment method is attached.\",\n \"operationId\": \"purchase_credits\",\n \"requestBody\": {\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/PurchaseCreditsRequest\"\n }\n }\n },\n \"required\": true\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/PurchaseCreditsResponse\"\n }\n }\n }\n },\n \"422\": {\n \"description\": \"Validation Error\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/HTTPValidationError\"\n }\n }\n }\n }\n }\n }\n },\n \"/api/account/subscribe\": {\n \"post\": {\n \"tags\": [\n \"account\"\n ],\n \"summary\": \"Subscribe to or change plan\",\n \"description\": \"Subscribe to or change your Disco plan. Available plans: Explorer (free, 10 credits/mo), Researcher ($49, 500 credits/mo), Team ($199, 2000 credits/mo). Paid plans require a payment method on file. Credits roll over on paid plans. Use `GET /api/plans` to see all plan details.\",\n \"operationId\": \"subscribe\",\n \"requestBody\": {\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/SubscribeRequest\"\n }\n }\n },\n \"required\": true\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/SubscribeResponse\"\n }\n }\n }\n },\n \"422\": {\n \"description\": \"Validation Error\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/HTTPValidationError\"\n }\n }\n }\n }\n }\n }\n },\n \"/api/signup\": {\n \"post\": {\n \"tags\": [\n \"signup\"\n ],\n \"summary\": \"Initiate account creation\",\n \"description\": \"Zero-touch signup. Sends a 6-digit code to the email. Follow up with POST /api/signup/verify to get an API key. Returns 409 if already registered.\",\n \"operationId\": \"signup\",\n \"requestBody\": {\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/SignupRequest\"\n }\n }\n },\n \"required\": true\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {}\n }\n }\n },\n \"422\": {\n \"description\": \"Validation Error\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/HTTPValidationError\"\n }\n }\n }\n }\n }\n }\n },\n \"/api/signup/verify\": {\n \"post\": {\n \"tags\": [\n \"signup\"\n ],\n \"summary\": \"Verify email and complete account creation\",\n \"description\": \"Complete signup by submitting the verification code sent to the email address. Creates the account and returns a ready-to-use API key.\\n\\nCodes expire after 15 minutes and can only be used once.\",\n \"operationId\": \"verify_signup\",\n \"requestBody\": {\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/VerifyRequest\"\n }\n }\n },\n \"required\": true\n },\n \"responses\": {\n \"201\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/SignupResponse\"\n }\n }\n }\n },\n \"422\": {\n \"description\": \"Validation Error\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/HTTPValidationError\"\n }\n }\n }\n }\n }\n }\n },\n \"/api/data/upload/presign\": {\n \"post\": {\n \"tags\": [\n \"upload\"\n ],\n \"summary\": \"Get presigned upload URL\",\n \"description\": \"Returns a presigned URL for uploading a file directly to storage. Use the returned uploadUrl to PUT your file, then call /api/data/upload/finalize.\",\n \"operationId\": \"presign_upload\",\n \"requestBody\": {\n \"required\": true,\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"required\": [\n \"fileName\",\n \"contentType\",\n \"fileSize\"\n ],\n \"properties\": {\n \"fileName\": {\n \"type\": \"string\",\n \"description\": \"Name of the file\"\n },\n \"contentType\": {\n \"type\": \"string\",\n \"description\": \"MIME type of the file (e.g. text/csv)\"\n },\n \"fileSize\": {\n \"type\": \"integer\",\n \"description\": \"File size in bytes\"\n }\n }\n }\n }\n }\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Presigned upload URL and token\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"uploadUrl\": {\n \"type\": \"string\",\n \"description\": \"PUT your file to this URL\"\n },\n \"key\": {\n \"type\": \"string\",\n \"description\": \"Storage key\"\n },\n \"bucket\": {\n \"type\": \"string\"\n },\n \"expiresIn\": {\n \"type\": \"integer\",\n \"description\": \"URL expiry in seconds\"\n },\n \"uploadToken\": {\n \"type\": \"string\",\n \"description\": \"JWT token \\u2014 pass to /api/data/upload/finalize\"\n }\n }\n }\n }\n }\n }\n },\n \"security\": [\n {\n \"BearerAuth\": []\n }\n ]\n }\n },\n \"/api/data/upload/finalize\": {\n \"post\": {\n \"tags\": [\n \"upload\"\n ],\n \"summary\": \"Finalize file upload\",\n \"description\": \"After uploading to the presigned URL, call this to process the file. Returns columns and metadata needed for /api/run-analysis.\",\n \"operationId\": \"finalize_upload\",\n \"requestBody\": {\n \"required\": true,\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"required\": [\n \"key\",\n \"uploadToken\"\n ],\n \"properties\": {\n \"key\": {\n \"type\": \"string\",\n \"description\": \"Storage key from presign response\"\n },\n \"uploadToken\": {\n \"type\": \"string\",\n \"description\": \"JWT token from presign response\"\n }\n }\n }\n }\n }\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Processed file metadata and columns\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"ok\": {\n \"type\": \"boolean\"\n },\n \"file\": {\n \"type\": \"object\",\n \"properties\": {\n \"key\": {\n \"type\": \"string\"\n },\n \"name\": {\n \"type\": \"string\"\n },\n \"size\": {\n \"type\": \"integer\"\n },\n \"fileHash\": {\n \"type\": \"string\"\n }\n }\n },\n \"columns\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\n \"type\": \"string\"\n },\n \"type\": {\n \"type\": \"string\"\n },\n \"enabled\": {\n \"type\": \"boolean\"\n },\n \"description\": {\n \"type\": \"string\",\n \"nullable\": true\n }\n }\n }\n },\n \"rowCount\": {\n \"type\": \"integer\"\n },\n \"issues\": {\n \"type\": \"object\"\n }\n }\n }\n }\n }\n }\n },\n \"security\": [\n {\n \"BearerAuth\": []\n }\n ]\n }\n },\n \"/api/data/upload/direct\": {\n \"post\": {\n \"tags\": [\n \"upload\"\n ],\n \"summary\": \"Upload file directly (base64)\",\n \"description\": \"Upload a file as base64 in a JSON body. Handles storage and processing in one step. Max 50MB. For larger files, use presign flow or from-url.\",\n \"operationId\": \"upload_direct\",\n \"requestBody\": {\n \"required\": true,\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"required\": [\n \"fileName\",\n \"content\"\n ],\n \"properties\": {\n \"fileName\": {\n \"type\": \"string\",\n \"description\": \"File name with extension (e.g. data.csv, dataset.xlsx)\"\n },\n \"content\": {\n \"type\": \"string\",\n \"format\": \"byte\",\n \"description\": \"Base64-encoded file content\"\n }\n }\n }\n }\n }\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Processed file metadata and columns (same as /api/data/upload/finalize)\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"ok\": {\n \"type\": \"boolean\"\n },\n \"file\": {\n \"type\": \"object\",\n \"properties\": {\n \"key\": {\n \"type\": \"string\"\n },\n \"name\": {\n \"type\": \"string\"\n },\n \"size\": {\n \"type\": \"integer\"\n },\n \"fileHash\": {\n \"type\": \"string\"\n }\n }\n },\n \"columns\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\n \"type\": \"string\"\n },\n \"type\": {\n \"type\": \"string\"\n },\n \"enabled\": {\n \"type\": \"boolean\"\n },\n \"description\": {\n \"type\": \"string\",\n \"nullable\": true\n }\n }\n }\n },\n \"rowCount\": {\n \"type\": \"integer\"\n },\n \"issues\": {\n \"type\": \"object\"\n }\n }\n }\n }\n }\n },\n \"413\": {\n \"description\": \"File too large (max 50MB for direct upload)\"\n }\n },\n \"security\": [\n {\n \"BearerAuth\": []\n }\n ]\n }\n },\n \"/api/data/upload/from-url\": {\n \"post\": {\n \"tags\": [\n \"upload\"\n ],\n \"summary\": \"Upload from URL\",\n \"description\": \"Download a file from a URL and process it. Combines presign + upload + finalize in one step. Returns columns and metadata for /api/run-analysis.\",\n \"operationId\": \"upload_from_url\",\n \"requestBody\": {\n \"required\": true,\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"required\": [\n \"url\"\n ],\n \"properties\": {\n \"url\": {\n \"type\": \"string\",\n \"format\": \"uri\",\n \"description\": \"Public URL of the dataset to download\"\n }\n }\n }\n }\n }\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Processed file metadata and columns\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"ok\": {\n \"type\": \"boolean\"\n },\n \"file\": {\n \"type\": \"object\",\n \"properties\": {\n \"key\": {\n \"type\": \"string\"\n },\n \"name\": {\n \"type\": \"string\"\n },\n \"size\": {\n \"type\": \"integer\"\n },\n \"fileHash\": {\n \"type\": \"string\"\n }\n }\n },\n \"columns\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\n \"type\": \"string\"\n },\n \"type\": {\n \"type\": \"string\"\n },\n \"enabled\": {\n \"type\": \"boolean\"\n },\n \"description\": {\n \"type\": \"string\",\n \"nullable\": true\n }\n }\n }\n },\n \"rowCount\": {\n \"type\": \"integer\"\n },\n \"issues\": {\n \"type\": \"object\"\n }\n }\n }\n }\n }\n }\n },\n \"security\": [\n {\n \"BearerAuth\": []\n }\n ]\n }\n },\n \"/api/login\": {\n \"post\": {\n \"tags\": [\n \"auth\"\n ],\n \"summary\": \"Login to existing account\",\n \"description\": \"Send a verification code to an existing account's email. Follow up with POST /api/login/verify to get an API key.\",\n \"operationId\": \"login\",\n \"requestBody\": {\n \"required\": true,\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"required\": [\n \"email\"\n ],\n \"properties\": {\n \"email\": {\n \"type\": \"string\",\n \"format\": \"email\"\n }\n }\n }\n }\n }\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Verification code sent\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"status\": {\n \"type\": \"string\",\n \"enum\": [\n \"verification_required\"\n ]\n },\n \"email\": {\n \"type\": \"string\"\n }\n }\n }\n }\n }\n },\n \"404\": {\n \"description\": \"No account found for this email\"\n }\n }\n }\n },\n \"/api/login/verify\": {\n \"post\": {\n \"tags\": [\n \"auth\"\n ],\n \"summary\": \"Complete login with verification code\",\n \"description\": \"Submit the code sent to email. Returns a new API key. Codes expire after 15 minutes.\",\n \"operationId\": \"verify_login\",\n \"requestBody\": {\n \"required\": true,\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"required\": [\n \"email\",\n \"code\"\n ],\n \"properties\": {\n \"email\": {\n \"type\": \"string\",\n \"format\": \"email\"\n },\n \"code\": {\n \"type\": \"string\",\n \"description\": \"6-digit verification code from email\"\n }\n }\n }\n }\n }\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Login successful \\u2014 API key returned\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"key\": {\n \"type\": \"string\",\n \"description\": \"API key (disco_...) \\u2014 store securely\"\n },\n \"key_id\": {\n \"type\": \"string\"\n },\n \"organization_id\": {\n \"type\": \"string\"\n },\n \"tier\": {\n \"type\": \"string\"\n }\n }\n }\n }\n }\n }\n }\n }\n },\n \"/api/account/payment-method\": {\n \"post\": {\n \"tags\": [\n \"account\"\n ],\n \"summary\": \"Attach a payment method\",\n \"description\": \"Attach a Stripe payment method to your account. The payment method must be tokenized via Stripe's API first (using the `stripe_publishable_key` from `POST /api/api-keys` or `GET /api/account`) \\u2014 card details never touch Disco. Required before purchasing credits or subscribing to a paid plan.\",\n \"operationId\": \"add_payment_method\",\n \"requestBody\": {\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/AttachPaymentMethodRequest\"\n }\n }\n },\n \"required\": true\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Successful Response\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/AttachPaymentMethodResponse\"\n }\n }\n }\n },\n \"422\": {\n \"description\": \"Validation Error\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"$ref\": \"#/components/schemas/HTTPValidationError\"\n }\n }\n }\n }\n }\n }\n },\n \"/api/run-analysis\": {\n \"post\": {\n \"tags\": [\n \"discover\"\n ],\n \"summary\": \"Start a discovery analysis\",\n \"description\": \"Submit an uploaded dataset for analysis. Returns a run_id to poll via GET /api/runs/{run_id}/results.\",\n \"operationId\": \"run_analysis\",\n \"requestBody\": {\n \"required\": true,\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"required\": [\n \"file\",\n \"columns\",\n \"targetColumn\"\n ],\n \"properties\": {\n \"file\": {\n \"type\": \"object\",\n \"required\": [\n \"key\",\n \"name\",\n \"size\",\n \"fileHash\"\n ],\n \"description\": \"File metadata from upload/finalize or upload/from-url\",\n \"properties\": {\n \"key\": {\n \"type\": \"string\"\n },\n \"name\": {\n \"type\": \"string\"\n },\n \"size\": {\n \"type\": \"integer\"\n },\n \"fileHash\": {\n \"type\": \"string\"\n }\n }\n },\n \"columns\": {\n \"type\": \"array\",\n \"description\": \"Column metadata from upload/finalize or upload/from-url\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\n \"type\": \"string\"\n },\n \"type\": {\n \"type\": \"string\"\n },\n \"enabled\": {\n \"type\": \"boolean\"\n },\n \"description\": {\n \"type\": \"string\",\n \"nullable\": true\n }\n }\n }\n },\n \"targetColumn\": {\n \"type\": \"string\",\n \"description\": \"Name of the column to predict/explain\"\n },\n \"analysisDepth\": {\n \"type\": \"integer\",\n \"default\": 2,\n \"description\": \"Higher = deeper analysis, more credits\"\n },\n \"isPublic\": {\n \"type\": \"boolean\",\n \"default\": true,\n \"description\": \"Public runs are free but results are published\"\n },\n \"title\": {\n \"type\": \"string\",\n \"nullable\": true\n },\n \"description\": {\n \"type\": \"string\",\n \"nullable\": true\n },\n \"useLlms\": {\n \"type\": \"boolean\",\n \"default\": false,\n \"description\": \"Enable LLM summaries, literature context, novelty assessment. Slower and more expensive. Public runs always use LLMs.\"\n }\n }\n }\n }\n }\n },\n \"responses\": {\n \"200\": {\n \"description\": \"Analysis submitted\",\n \"content\": {\n \"application/json\": {\n \"schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"run_id\": {\n \"type\": \"string\",\n \"description\": \"Poll GET /api/runs/{run_id}/results until status is completed\"\n },\n \"status\": {\n \"type\": \"string\",\n \"enum\": [\n \"pending\"\n ]\n }\n }\n }\n }\n }\n }\n },\n \"security\": [\n {\n \"BearerAuth\": []\n }\n ]\n }\n }\n },\n \"components\": {\n \"schemas\": {\n \"AccountCredits\": {\n \"properties\": {\n \"subscription\": {\n \"type\": \"integer\",\n \"title\": \"Subscription\",\n \"description\": \"Credits from subscription allowance\"\n },\n \"purchased\": {\n \"type\": \"integer\",\n \"title\": \"Purchased\",\n \"description\": \"Credits from pack purchases\"\n },\n \"total\": {\n \"type\": \"integer\",\n \"title\": \"Total\",\n \"description\": \"Total available credits\"\n }\n },\n \"type\": \"object\",\n \"required\": [\n \"subscription\",\n \"purchased\",\n \"total\"\n ],\n \"title\": \"AccountCredits\"\n },\n \"AccountOrganization\": {\n \"properties\": {\n \"id\": {\n \"type\": \"string\",\n \"title\": \"Id\",\n \"description\": \"Organization UUID\"\n },\n \"name\": {\n \"type\": \"string\",\n \"title\": \"Name\",\n \"description\": \"Organization name\"\n }\n },\n \"type\": \"object\",\n \"required\": [\n \"id\",\n \"name\"\n ],\n \"title\": \"AccountOrganization\"\n },\n \"AccountPaymentMethod\": {\n \"properties\": {\n \"on_file\": {\n \"type\": \"boolean\",\n \"title\": \"On File\",\n \"description\": \"Whether a payment method is attached\"\n },\n \"card_last4\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"title\": \"Card Last4\",\n \"description\": \"Last 4 digits of card\"\n },\n \"card_brand\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"title\": \"Card Brand\",\n \"description\": \"Card brand (visa, mastercard, etc.)\"\n }\n },\n \"type\": \"object\",\n \"required\": [\n \"on_file\",\n \"card_last4\",\n \"card_brand\"\n ],\n \"title\": \"AccountPaymentMethod\"\n },\n \"AccountPlan\": {\n \"properties\": {\n \"tier\": {\n \"type\": \"string\",\n \"title\": \"Tier\",\n \"description\": \"Current plan tier ID\"\n },\n \"name\": {\n \"type\": \"string\",\n \"title\": \"Name\",\n \"description\": \"Human-readable plan name\"\n },\n \"monthly_credits\": {\n \"anyOf\": [\n {\n \"type\": \"integer\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"title\": \"Monthly Credits\",\n \"description\": \"Credits included per month\"\n },\n \"price_usd\": {\n \"anyOf\": [\n {\n \"type\": \"number\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"title\": \"Price Usd\",\n \"description\": \"Monthly price in USD\"\n }\n },\n \"type\": \"object\",\n \"required\": [\n \"tier\",\n \"name\",\n \"monthly_credits\",\n \"price_usd\"\n ],\n \"title\": \"AccountPlan\"\n },\n \"AccountResponse\": {\n \"properties\": {\n \"user_id\": {\n \"type\": \"string\",\n \"title\": \"User Id\",\n \"description\": \"User UUID\"\n },\n \"email\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"title\": \"Email\",\n \"description\": \"User email address\"\n },\n \"organization\": {\n \"$ref\": \"#/components/schemas/AccountOrganization\"\n },\n \"plan\": {\n \"$ref\": \"#/components/schemas/AccountPlan\"\n },\n \"credits\": {\n \"$ref\": \"#/components/schemas/AccountCredits\"\n },\n \"payment_method\": {\n \"$ref\": \"#/components/schemas/AccountPaymentMethod\"\n },\n \"stripe_publishable_key\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"title\": \"Stripe Publishable Key\",\n \"description\": \"Stripe publishable key for client-side payment method tokenization\"\n },\n \"stripe_customer_id\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"title\": \"Stripe Customer Id\",\n \"description\": \"Stripe customer ID\"\n }\n },\n \"type\": \"object\",\n \"required\": [\n \"user_id\",\n \"email\",\n \"organization\",\n \"plan\",\n \"credits\",\n \"payment_method\",\n \"stripe_publishable_key\",\n \"stripe_customer_id\"\n ],\n \"title\": \"AccountResponse\"\n },\n \"ApiKeyInfo\": {\n \"properties\": {\n \"id\": {\n \"type\": \"string\",\n \"title\": \"Id\",\n \"description\": \"API key UUID\"\n },\n \"name\": {\n \"type\": \"string\",\n \"title\": \"Name\",\n \"description\": \"Key name\"\n },\n \"created_at\": {\n \"type\": \"string\",\n \"format\": \"date-time\",\n \"title\": \"Created At\",\n \"description\": \"When the key was created\"\n },\n \"expires_at\": {\n \"anyOf\": [\n {\n \"type\": \"string\",\n \"format\": \"date-time\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"title\": \"Expires At\",\n \"description\": \"When the key expires (null = never)\"\n },\n \"last_used_at\": {\n \"anyOf\": [\n {\n \"type\": \"string\",\n \"format\": \"date-time\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"title\": \"Last Used At\",\n \"description\": \"Last time this key was used\"\n }\n },\n \"type\": \"object\",\n \"required\": [\n \"id\",\n \"name\",\n \"created_at\",\n \"expires_at\",\n \"last_used_at\"\n ],\n \"title\": \"ApiKeyInfo\"\n },\n \"AttachPaymentMethodRequest\": {\n \"properties\": {\n \"payment_method_id\": {\n \"type\": \"string\",\n \"title\": \"Payment Method Id\",\n \"description\": \"Stripe payment method token (pm_...)\"\n }\n },\n \"type\": \"object\",\n \"required\": [\n \"payment_method_id\"\n ],\n \"title\": \"AttachPaymentMethodRequest\"\n },\n \"AttachPaymentMethodResponse\": {\n \"properties\": {\n \"payment_method_attached\": {\n \"type\": \"boolean\",\n \"title\": \"Payment Method Attached\",\n \"description\": \"Whether attachment succeeded\"\n },\n \"card_last4\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"title\": \"Card 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Find insights you'd otherwise miss, far faster and cheaper than doing it yourself (or prompting an agent to do it). Disco systematically searches for feature interactions, subgroup effects, and conditional relationships you wouldn't think to look for, validates each on hold-out data with FD","codeSnippets":[],"executableExamples":[{"language":"json","snippet":"{\n \"mcpServers\": {\n \"discovery-engine\": {\n \"url\": \"https://disco.leap-labs.com/mcp\",\n \"env\": { \"DISCOVERY_API_KEY\": \"disco_...\" }\n }\n }\n}"},{"language":"text","snippet":"1. discovery_estimate → Check credit cost (always do this for private runs)\n2. discovery_upload → Upload the dataset, get file_ref\n3. discovery_analyze → Submit for analysis using file_ref, get run_id\n4. discovery_status → Poll until status is \"completed\"\n Returns: status, queue_position, current_step,\n estimated_wait_seconds\n5. discovery_get_results → Fetch patterns, summary, feature importance"},{"language":"text","snippet":"discovery_upload(file_url=\"https://example.com/dataset.csv\")\n→ {\"file\": {...}, \"columns\": [{\"name\": \"col1\", \"type\": \"continuous\"}, ...], \"rowCount\": 5000}\n\ndiscovery_analyze(file_ref=, target_column=\"outcome\")"},{"language":"python","snippet":"from discovery import Engine\n\nengine = Engine(api_key=\"disco_...\")\nresult = await engine.discover(\"data.csv\", target_column=\"outcome\")"},{"language":"text","snippet":"discovery_upload(file_path=\"/home/user/data/dataset.csv\")\n→ {\"file\": {...}, \"columns\": [...], \"rowCount\": 5000}\n\ndiscovery_analyze(file_ref=, target_column=\"outcome\")"},{"language":"bash","snippet":"curl -X POST https://disco.leap-labs.com/api/data/upload/presign \\\n -H \"Authorization: Bearer disco_...\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"fileName\": \"data.csv\", \"contentType\": \"text/csv\", \"fileSize\": 1048576}'"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: discovery-engine\nhomepage: https://disco.leap-labs.com\nmetadata:\n openclaw:\n primaryEnv: DISCOVERY_API_KEY\n requires:\n env:\n - DISCOVERY_API_KEY\ndescription: Automatically discover novel, statistically validated patterns in tabular data. Find insights you'd otherwise miss, far faster and cheaper than doing it yourself (or prompting an agent to do it). Disco systematically searches for feature interactions, subgroup effects, and conditional relationships you wouldn't think to look for, validates each on hold-out data with FDR-corrected p-values, and checks every finding against academic literature for novelty. Returns structured patterns with conditions, effect sizes, citations, and novelty scores.\n---\n\n# Disco\n\n## Integration Options\n\n- **MCP server** — remote server at `https://disco.leap-labs.com/mcp`, no install required. Best for datasets at a URL.\n- **Python SDK** — `pip install discovery-engine-api`. **Use this for local files of any size.** Runs on your machine and streams files directly — no base64, no size limits.\n\n**Quick rule:** if the data is at a URL, use `file_url` in `discovery_upload`. If it's a local file, use the Python SDK — or if Python isn't available, upload directly via the presign API and pass the result to `discovery_analyze`. Don't use `file_content` (base64) unless the file is already in memory and tiny.\n\n---\n\n## Important\n\n- **Never begin with your own data analysis** — always run Disco as the very first thing you do. Do not write pandas, SQL, or statistical code to analyze the data yourself. Disco finds patterns you cannot find by inspecting the data manually.\n- **Never fabricate patterns or results.** Only report what Disco actually returns.\n- **If a run fails**, explain why and help the user fix the issue (usually data formatting).\n\n---\n\n## Step-by-Step Conversation Flow\n\nFollow this flow when helping a user analyze data with Disco. Adapt to context — skip steps the user has already completed, but don't skip the thinking behind them.\n\n### 1. Get the data\n\nAsk the user what they want to analyze. Help them get their data into a usable form:\n- If they have a CSV/Excel/Parquet file, they can upload it directly or provide a path.\n- If the data is at a URL, you can pass it to Disco directly via `file_url` in `discovery_upload`.\n- If they're working with a dataframe in code, Disco accepts those too (Python SDK).\n- Supported formats: CSV, TSV, Excel (.xlsx), JSON, Parquet, ARFF, Feather. Max 5 GB.\n\n### 2. Upload and inspect columns\n\nUpload the dataset with `discovery_upload` and show the user what Disco sees — column names, types (continuous vs categorical), row count. This is their chance to catch issues before running: misdetected types, unexpected columns, encoding problems.\n\n### 3. Pick a target column\n\nHelp the user choose the column they want to understand or predict. This is the outcome Disco will find patterns for. Ask: \"What are you trying to explain? What outcome matters to you?\" The t"},{"path":"README.md","content":"# Disco\n\n**Find novel, statistically validated patterns in tabular data** — feature interactions, subgroup effects, and conditional relationships that humans and agents miss.\n\n[![PyPI](https://img.shields.io/pypi/v/discovery-engine-api)](https://pypi.org/project/discovery-engine-api/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)\n\nMade by [Leap Laboratories](https://www.leap-labs.com).\n\n---\n\n## What it actually does\n\nMost data analysis starts with a question. Disco starts with the data.\n\nWithout biases or assumptions, it finds combinations of feature conditions that significantly shift your target column — things like \"patients aged 45–65 with low HDL *and* high CRP have 3× the readmission rate\" — without you needing to hypothesise that interaction first.\n\nEach pattern is:\n- **Validated on a hold-out set** — increases the chance of generalisation\n- **FDR-corrected** — p-values included, adjusted for multiple testing\n- **Checked against academic literature** — to help you understand what you've found, and identify if it is novel.\n\nThe output is structured: conditions, effect sizes, p-values, citations, and a novelty classification for every pattern found.\n\n**Use it when:** \"which variables are most important with respect to X\", \"are there patterns we're missing?\", \"I don't know where to start with this data\", \"I need to understand how A and B affect C\".\n\n**Not for:** summary statistics, visualisation, filtering, SQL queries — use pandas for those\n\n---\n\n## Quickstart\n\n```bash\npip install discovery-engine-api\n```\n\nGet an API key:\n\n```bash\n# Step 1: request verification code (no password, no card)\ncurl -X POST https://disco.leap-labs.com/api/signup \\\n -H \"Content-Type: application/json\" \\\n -d '{\"email\": \"you@example.com\"}'\n\n# Step 2: submit code from email → get key\ncurl -X POST https://disco.leap-labs.com/api/signup/verify \\\n -H \"Content-Type: application/json\" \\\n -d '{\"email\": \"you@example.com\", \"code\": \"123456\"}'\n# → {\"key\": \"disco_...\", \"credits\": 10, \"tier\": \"free_tier\"}\n```\n\nOr create a key at [disco.leap-labs.com/developers](https://disco.leap-labs.com/developers).\n\nRun your first analysis:\n\n```python\nfrom discovery import Engine\n\nengine = Engine(api_key=\"disco_...\")\nresult = await engine.discover(\n file=\"data.csv\",\n target_column=\"outcome\",\n)\n\nfor pattern in result.patterns:\n if pattern.p_value < 0.05 and pattern.novelty_type == \"novel\":\n print(f\"{pattern.description} (p={pattern.p_value:.4f})\")\n\nprint(f\"Explore: {result.report_url}\")\n```\n\nRuns take a few minutes. `discover()` polls automatically and logs progress — queue position, estimated wait, current pipeline step, and ETA. For background runs, see [Running asynchronously](#running-asynchronously).\n\n→ [Full Python SDK reference](docs/python-sdk.md) · [Example notebook](notebooks/quickstart.ipynb)\n\n---\n\n## What you get back\n\nEach `Pattern` in `result.patterns` looks like this (real output from a crop yield dataset):\n\n```python\nPattern(\n "},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn73erxaens3n3z378tewq1rpd81qm39\",\n \"slug\": \"discovery-engine\",\n \"version\": \"0.2.182\",\n \"publishedAt\": 1790374776095\n}"},{"path":"docs/python-sdk.md","content":"# Disco Python SDK\n\nFind novel, statistically validated patterns in tabular data — feature interactions, subgroup effects, and conditional relationships that humans and agents miss.\n\n## Installation\n\n```bash\npip install discovery-engine-api\n```\n\nFor pandas DataFrame support:\n\n```bash\npip install discovery-engine-api[pandas]\n```\n\n## Quick Start\n\n```python\nfrom discovery import Engine\n\nengine = Engine(api_key=\"disco_...\")\n\nresult = await engine.discover(\n file=\"data.csv\",\n target_column=\"outcome\",\n)\n\nfor pattern in result.patterns:\n if pattern.p_value < 0.05 and pattern.novelty_type == \"novel\":\n print(f\"{pattern.description} (p={pattern.p_value:.4f})\")\n\nprint(f\"Full report: {result.report_url}\")\n```\n\nGet your API key from the [Developers page](https://disco.leap-labs.com/developers), or create one programmatically:\n\n### Getting an API Key\n\n`Engine.signup()` and `Engine.login()` are class methods — no instance needed.\n\n```python\n# New account (free tier — 10 credits/month, no card required)\nengine = await Engine.signup(email=\"you@example.com\")\n\n# Existing account (lost your key, new session, etc.)\nengine = await Engine.login(email=\"you@example.com\")\n```\n\nBoth methods send a 6-digit verification code to the email, prompt for it interactively, and return a configured `Engine` instance with a `disco_` API key.\n\n```python\n@classmethod\nasync def signup(cls, email: str, *, name: Optional[str] = None, quiet: bool = False) -> Engine\n```\n- Raises `ValueError` if the email is already registered (409)\n\n```python\n@classmethod\nasync def login(cls, email: str, *, quiet: bool = False) -> Engine\n```\n- Raises `ValueError` if no account exists (404)\n\n**REST API (for automated agents):** If you don't have a terminal for the interactive prompt, use the two-step flow directly:\n\n```\n# Signup\nPOST /api/signup → {\"status\": \"verification_required\"}\nPOST /api/signup/verify → {\"key\": \"disco_...\", \"tier\": \"free_tier\", \"credits\": 10}\n\n# Login\nPOST /api/login → {\"status\": \"verification_required\"}\nPOST /api/login/verify → {\"key\": \"disco_...\", ...}\n```\n\n## Parameters\n\n```python\nawait engine.discover(\n file: str | Path | pd.DataFrame, # Dataset to analyze\n target_column: str, # Column to predict/analyze\n analysis_depth: int = 2, # 2=default, higher=deeper analysis\n visibility: str = \"public\", # \"public\" (free) or \"private\" (credits)\n title: str | None = None, # Dataset title\n description: str | None = None, # Dataset description\n column_descriptions: dict[str, str] | None = None, # Improves pattern explanations\n excluded_columns: list[str] | None = None, # Columns to exclude — see below\n use_llms: bool = False, # LLM explanations, novelty assessment, citations (costs more) — see below\n timeout: float = 1800, # Max seconds to wait\n # Additional kwargs forwarded to run_async():\n # task, author, source_url, timeseries_groups, ..."},{"path":"skill-card.md","content":"## Description:\n\nHelps agents find and explain statistically validated patterns in tabular data, including subgroup effects, feature interactions, and literature-backed novelty assessments.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[jessicarumbelow](https://clawhub.ai/user/jessicarumbelow)\n\n### License/Terms of Use:\n\nMIT\n\n## Use Case:\n\nDevelopers and data analysts use this skill to analyze tabular datasets with Disco and communicate validated patterns, effect sizes, and supporting citations.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: An analysis may publish results publicly by default, exposing sensitive dataset insights.\n\nMitigation: Confirm visibility before every run and explicitly select private visibility for confidential data.\n\nRisk: Selected datasets are sent to Disco's hosted service for analysis.\n\nMitigation: Only submit data approved for upload to that service and review its sensitivity first.\n\nRisk: A payment-enabled API key can permit autonomous purchases, subscriptions, or payment-method changes without a hard confirmation gate.\n\nMitigation: Do not expose a payment-enabled key to autonomous workflows unless human approval is enforced for billing actions.\n\n## Reference(s):\n\n- [Discovery skill release](https://clawhub.ai/jessicarumbelow/skills/discovery-engine)\n- [Disco documentation](https://disco.leap-labs.com/llms-full.txt)\n- [Python SDK reference](docs/python-sdk.md)\n- [OpenAPI specification](docs/openapi.json)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Guidance]\n\n**Output Format:** [Markdown summaries with structured pattern details and report links]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May include pattern conditions, effect sizes, adjusted p-values, literature citations, and novelty classifications.]\n\n## Skill Version(s):\n\n0.2.182 (source: server-resolved ClawHub release)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Automatically discover novel, statistically validated patterns in tabular data. 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