What makes research notable?

1.9K downloads. Trust evidence available

How should research be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

research

Runs multi-source research across GitHub, HN, Reddit, arXiv, and Semantic Scholar Skill: research Owner: athola Summary: Runs multi-source research across GitHub, HN, Reddit, arXiv, and Semantic Scholar Tags: latest:1.9.19 Version history: v1.9.19 | 2026-08-26T13:23:46.783Z | user Release v1.9.19 v1.9.17 | 2026-07-30T05:43:28.548Z | user Release v1.9.17 v1.9.16 | 2026-07-14T20:00:17.405Z | user Release v1.9.16 v1.9.14 | 2026-06-30T18:07:32.839Z | user Release v1.9.14 v1.9.13 | 2026-06-27T16:25:21.

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.9k

Updated

Oct 9, 2026

Version

1.9.19

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.9K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 1.9K downloadsadoption · observed Oct 9, 2026
Latest release: 1.9.19release · observed Aug 26, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17emme0e2m3cpf7k2jvp3a84984b8z9:nm-tome-research

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-tome-research/snapshot"

Documentation

CLAWHUB

52,634 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: research
description: |
  Runs multi-source research across GitHub, HN, Reddit, arXiv, and Semantic Scholar
version: 1.9.8
triggers:
  - research
  - synthesis
  - multi-source
  - surveying a technical topic across multiple channels
metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/tome", "emoji": "\ud83e\udd9e"}}
source: claude-night-market
source_plugin: tome
---

> **Night Market Skill** — ported from [claude-night-market/tome](https://github.com/athola/claude-night-market/tree/master/plugins/tome). For the full experience with agents, hooks, and commands, install the Claude Code plugin.


# Research Session Orchestrator

Run a full multi-source research session: classify the
domain, dispatch parallel agents, synthesize findings,
and output a formatted report.

## Workflow

### Step 1: Classify Domain

Run the domain classifier on the topic:

```python
from tome.scripts.domain_classifier import classify
result = classify(topic)
# result.domain, result.triz_depth, result.channel_weights
```

If confidence < 0.6, ask the user to confirm or override
the domain classification before proceeding.

### Step 2: Plan Research

```python
from tome.scripts.research_planner import plan
research_plan = plan(result)
# research_plan.channels, research_plan.weights, research_plan.triz_depth
```

### Step 3: Create Session

```python
from tome.session import SessionManager
mgr = SessionManager(Path.cwd())
session = mgr.create(topic, result.domain, result.triz_depth, research_plan.channels)
```

### Step 4: Dispatch Agents

Launch research agents in parallel using the Agent tool.
Use this mapping:

| Channel | Agent Type | Prompt Includes |
|---------|-----------|-----------------|
| code | `tome:code-searcher` | topic |
| discourse | `tome:discourse-scanner` | topic, domain, subreddits |
| academic | `tome:literature-reviewer` | topic, domain |
| triz | `tome:triz-analyst` | topic, domain, triz_depth |

**Rules:**
- Always dispatch code and discourse agents
- Dispatch academic agent only if "academic" is in
  research_plan.channels
- Dispatch triz agent only if "triz" is in
  research_plan.channels AND triz_depth != "light"
- Dispatch all eligible agents in a SINGLE message
  (parallel, not sequential)

Each agent prompt must include:
1. The topic string
2. The domain classification
3. Any channel-specific context (subreddits for discourse,
   triz_depth for triz)
4. Instruction to return findings as JSON

### Step 5: Collect and Synthesize

After all agents return:

1. Parse each agent's findings into Finding objects
2. Merge using `tome.synthesis.merger.merge_findings()`
3. Rank using `tome.synthesis.ranker.rank_findings()`

### Step 6: Generate Output

```python
from tome.output.report import format_report, format_brief, format_transcript

# Default to report format
output = format_report(session)

# Save to docs/research/
output_path = f"docs/research/{session.id}-{slug}.md"
```

Save the sess

_meta.json

{
  "ownerId": "kn7d107jg9jv602h9ytsegydq184a42s",
  "slug": "nm-tome-research",
  "version": "1.9.19",
  "publishedAt": 1787750626783
}

skill-card.md

## Description:

Runs multi-source research across GitHub, Hacker News, Reddit, arXiv, and Semantic Scholar.

This skill is ready for commercial/non-commercial use.

## Publisher:

[athola](https://clawhub.ai/user/athola)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and technical researchers use this skill to orchestrate multi-source research sessions, synthesize findings from code, discourse, academic, and TRIZ channels, and save a formatted report for later review.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The workflow may query external sources through research agents, which can expose sensitive research topics.

Mitigation: Avoid including secrets or confidential details in research prompts, and review queries before dispatching agents.

Risk: The workflow can save reports and session state locally in the workspace.

Mitigation: Review saved docs/research outputs and delete or redact sensitive material when needed.

Risk: Parallel research agents can return incomplete, failed, or inconsistent findings.

Mitigation: Review synthesized findings before relying on them, and rerun or manually supplement failed channels.

## Reference(s):

- [Claude Night Market tome plugin](https://github.com/athola/claude-night-market/tree/master/plugins/tome)
- [ClawHub skill page](https://clawhub.ai/athola/skills/nm-tome-research)

## Skill Output:

**Output Type(s):** [text, markdown, files, guidance]

**Output Format:** [Markdown report, brief, or transcript with summarized findings and workspace file paths]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May save reports under docs/research and session state in the workspace.]

## Skill Version(s):

1.9.19 (source: server release metadata; artifact frontmatter reports 1.9.8)

## Ethical Considerations:

Users 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.

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