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agent-squad-python

Use when building or modifying a Python app that uses the agent-squad Python package — async multi-agent orchestration for Python 3.11+: orchestrator, agents (BedrockLLMAgent, AnthropicAgent, OpenAIAgent, SupervisorAgent, GroundedAgent, ChainAgent, and more), classifier routing (Bedrock, Anthropic, OpenAI), storage (in-memory, DynamoDB, SQL/Turso), retrievers (Amazon KB, Dakera), tools (AgentTools, MCPToolProvider), and custom implementations.

Quality

86%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A high-quality framework map: every code sample is executable, the decision guide for choosing agents/orchestrator is unambiguous, and the gotchas section captures real, non-obvious failure modes. The main weakness is structure: for a package this large, the per-component API tables and config/callback enumerations are inlined in SKILL.md instead of being split into one-level-deep reference files, and a few source-readable enumerations could be trimmed.

Suggestions

Move the per-component detail (agents/classifiers/storage/retrievers tables, the AgentSquadConfig field list, and callback hook listings) into references/ files (e.g. references/agents.md, references/tools.md) and keep SKILL.md as the decision map plus quick-start, per the progressive-disclosure anchor structure.

Trim source-readable enumerations that the body itself defers to the source — e.g. the AgentSquadConfig field list and the full Go-deeper signature inventory — to a pointer plus only the fields that change behavior in non-obvious ways.

Add short error-recovery guidance for the fragile paths the body flags, e.g. 'if boto3 is absent, always pass classifier= explicitly or AgentSquad raises ValueError at construction'.

DimensionReasoningScore

Conciseness

Lean and dense — tables, gotchas, and code carry the content with no tutorials on concepts Claude already knows ('This is a guide and a map — not an API reference'). However, it enumerates source-readable API detail inline, e.g. the full 'AgentSquadConfig fields: LOG_AGENT_CHAT, LOG_CLASSIFIER_CHAT, LOG_CLASSIFIER_RAW_OUTPUT, ...' list and the callback hook listings, which the file itself says to read from source. Anchor 4 — efficient with minor trims available, not 5.

4 / 5

Actionability

Fully executable throughout: pip extras install commands, a complete runnable route_request example with the streaming/non-streaming branch, an AgentTools tool definition with docstring extraction, the awaitable MCPToolProvider.create/disconnect lifecycle, and a copy-paste minimal custom Agent subclass. Copy-paste ready and covers the common cases — anchor 5.

5 / 5

Workflow Clarity

The 'When to use what' decision list gives unambiguous selection guidance ('One assistant — a single Agent subclass; no orchestrator needed', 'Answers must not drift from data — GroundedAgent', 'Fixed pipeline — ChainAgent') and the install → first turn → components → custom → gotchas arc is coherent. No destructive or batch operations, so no validation cap applies. Anchor 4 rather than 5 because there are no error-recovery feedback loops for the fragile spots it itself flags (e.g. the construction-time ValueError when boto3 is absent).

4 / 5

Progressive Disclosure

No bundle files exist and the whole framework map lives inline in a single ~400-line SKILL.md. The 'Go deeper' section signals real repo paths one level deep ('python/src/agent_squad/orchestrator.py', 'docs/src/content/docs/'), but substantial reference material is inlined rather than split — the agent table, classifier/storage/retriever tables, the AgentSquadConfig field enumeration, and the callback hook listings. Matches anchor 3 ('content that should be separate is inline'); not 4 because for a framework this size the bulk per-component detail belongs in reference files.

3 / 5

Total

16

/

20

Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: explicit 'Use when' trigger, a concrete map of the package's component surface, and a clearly bounded niche. It is written in third-person imperative style with no fluff. Minor improvement is possible by adding a few more natural trigger phrasings users might use instead of only the package's own terminology.

DimensionReasoningScore

Specificity

Concrete actions ('building or modifying a Python app') plus a precise enumeration of the component surface ('BedrockLLMAgent, AnthropicAgent, OpenAIAgent, SupervisorAgent, GroundedAgent, ChainAgent', 'classifier routing (Bedrock, Anthropic, OpenAI)', 'storage (in-memory, DynamoDB, SQL/Turso)', 'retrievers (Amazon KB, Dakera)', 'tools (AgentTools, MCPToolProvider)'). Matches anchor 4 — highly concrete naming, but the action verbs are limited to building/modifying rather than a comprehensive action list, so not 5.

4 / 5

Completeness

Explicitly answers both: what ('orchestrator, agents (...), classifier routing (...), storage (...), retrievers (...), tools (...), and custom implementations') and when ('Use when building or modifying a Python app that uses the agent-squad Python package'). Matches the anchor-5 good example structure of what + explicit 'Use when' trigger.

5 / 5

Trigger Term Quality

Good coverage of natural terms a user of this niche would say: 'agent-squad Python package', 'Python app', 'multi-agent orchestration', 'Python 3.11+', 'orchestrator', 'DynamoDB', 'Turso', plus concrete class names. Anchor 4, not 5 — a few natural phrasings (e.g. 'agent framework', 'agent routing') and variations are missing; not 3 since coverage goes well beyond a couple of generic keywords.

4 / 5

Distinctiveness Conflict Risk

Pinned to a single named package ('the agent-squad Python package', 'Python 3.11+') with its own class vocabulary, giving a clear niche and minimal conflict risk with generic Python or other agent-framework skills.

5 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

2FastLabs/agent-squad

Path

python/SKILL.md

Reviewed

Table of Contents

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