How should as be evaluated before use?

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

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.

Crawler Summary

as answer-first brief

Agent Swarm demo using CrewAI, requires an LLM API key Sprint Planner Swarm A small, provider-agnostic agent swarm that reads an Azure DevOps backlog, finds blockers, ranks work, drafts a justified sprint plan, and — only on explicit approval — moves the chosen items into the next sprint. Two implementations of the same thing: | File | What it shows | |---|---| | run_demo.py + swarm/crew.py | The practical version on **CrewAI** (the one you demo) | | swarm_from_scratch.p Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

as is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB REPOS, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Agent DossierGITHUB REPOSSafety: 66/100

as

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Sfgannon

Artifacts

Benchmarks

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

View Source

Setup snapshot

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
2
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.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content

Vendor (1)

Vendor

Sfgannon

profilemedium

Observed Oct 9, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed Oct 9, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB REPOS

Extracted files

Examples

Snippets

Languages

python

Executable Examples

bash

python -m venv .venv && source .venv/bin/activate    # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env                                  # fill in endpoint, deployment, org, project, team
az login                                              # Entra ID for both Azure OpenAI and Azure DevOps

bash

python run_demo.py --offline          # smoke test with the bundled 12-item fixture
python run_demo.py                    # real backlog, dry run (nothing written)
python run_demo.py --apply            # dry run, then confirm to move items into the sprint
python run_demo.py --quiet            # progress only, no agent chatter
python swarm_from_scratch.py --offline

text

LLM_MODEL_OVERRIDE=anthropic/claude-sonnet-4-5   # + ANTHROPIC_API_KEY
LLM_MODEL_OVERRIDE=openai/gpt-4o                 # + OPENAI_API_KEY
LLM_MODEL_OVERRIDE=ollama/llama3.1               # local, free

text

ADO REST (deterministic Python)          swarm (model calls)
   ─────────────────────────────           ─────────────────────────────────────────
   WIQL query → hydrate → comments   ───▶   Analyst → Detective → Planner → Reviewer
   next iteration                                                             │
                                                                     SprintPlan JSON
                                                                             │
   PATCH IterationPath  ◀──── only with --apply + typed "yes" ◀──────────────┘

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Full README

Sprint Planner Swarm

A small, provider-agnostic agent swarm that reads an Azure DevOps backlog, finds blockers, ranks work, drafts a justified sprint plan, and — only on explicit approval — moves the chosen items into the next sprint.

Two implementations of the same thing:

| File | What it shows | |---|---| | run_demo.py + swarm/crew.py | The practical version on CrewAI (the one you demo) | | swarm_from_scratch.py | The same swarm in ~100 lines with raw LiteLLM calls — what the framework does under the hood |

Both use the same swarm/ado_client.py, swarm/llm.py, and swarm/schemas.py.

Setup (local, no Docker)

python -m venv .venv && source .venv/bin/activate    # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env                                  # fill in endpoint, deployment, org, project, team
az login                                              # Entra ID for both Azure OpenAI and Azure DevOps

az login covers both services. azure-identity's DefaultAzureCredential picks up the CLI session. If your org issues keys or PATs instead, set AZURE_OPENAI_API_KEY and/or ADO_PAT in .env and those take precedence.

Run

python run_demo.py --offline          # smoke test with the bundled 12-item fixture
python run_demo.py                    # real backlog, dry run (nothing written)
python run_demo.py --apply            # dry run, then confirm to move items into the sprint
python run_demo.py --quiet            # progress only, no agent chatter
python swarm_from_scratch.py --offline

Outputs land in output/plan.json and output/plan.md.

Swap the model provider

One line in .env:

LLM_MODEL_OVERRIDE=anthropic/claude-sonnet-4-5   # + ANTHROPIC_API_KEY
LLM_MODEL_OVERRIDE=openai/gpt-4o                 # + OPENAI_API_KEY
LLM_MODEL_OVERRIDE=ollama/llama3.1               # local, free

Nothing else changes. That is the whole provider-agnostic story: every model call goes through LiteLLM's provider/model string.

How it works

   ADO REST (deterministic Python)          swarm (model calls)
   ─────────────────────────────           ─────────────────────────────────────────
   WIQL query → hydrate → comments   ───▶   Analyst → Detective → Planner → Reviewer
   next iteration                                                             │
                                                                     SprintPlan JSON
                                                                             │
   PATCH IterationPath  ◀──── only with --apply + typed "yes" ◀──────────────┘

Design choices worth calling out on stage:

Agents reason; code fetches and writes. The model never holds credentials and never chooses to mutate anything.
Structured output at the boundary. The reviewer must emit JSON matching SprintPlan; Pydantic validation is the last gate.
Ranking rules are explicit (priority field → unblocking value → business value → age) so the plan is explainable and the audience can argue with it.
Dry run by default. --apply is a deliberate second step with a typed confirmation.

Version notes

Tested pattern against CrewAI ≥ 0.100 and LiteLLM ≥ 1.50. CrewAI's LLM class forwards extra kwargs (api_version, azure_ad_token) to LiteLLM. If your installed version rejects a kwarg, remove it from LLMConfig.crewai_llm and rely on the AZURE_AD_TOKEN env var, which swarm/llm.py also sets.
Entra tokens last ~1 hour. Long stage delays: re-run, it fetches a fresh one.
The ADO comments endpoint is 7.1-preview.4; stable in practice.

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.

No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media

No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

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Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-10T02:15:55.158Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Sfgannon",
    "href": "https://github.com/sfgannon/as",
    "sourceUrl": "https://github.com/sfgannon/as",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T13:52:38.714Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T13:52:38.714Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-sfgannon-as/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]