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requirements-analysis

Analyze product and technical requirements for the PigeonPod project with software engineering rigor. Use when users ask to evaluate a feature, enhancement, non-functional requirement, integration, or migration for value, feasibility, architecture fit, implementation impact, risk, delivery scope, or tradeoffs. Do not use for bug triage or root-cause analysis; use `bug-analysis` for bugfix-oriented work. Always inspect current repository docs and code first, then use MCP tools including Context7 to verify external library, framework, or API constraints before concluding.

68

Quality

81%

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SKILL.md
Quality
Evals
Security

Quality

Content

75%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 well-structured, actionable analysis skill: concrete file paths, discovery commands, an explicit output template, and a quality-bar checkpoint back a clearly sequenced workflow. The main improvement areas are a concrete Context7 query example and an explicit error-recovery/feedback loop in the workflow.

Suggestions

Add a one-line concrete Context7/MCP query example (e.g., resolve-library-id then get-library-docs with a focused question) so the 'Use Context7 and MCP Deliberately' section is copy-paste actionable.

Insert an explicit mid-workflow feedback checkpoint, e.g. 'If evidence contradicts the stated requirement, restate assumptions and re-evaluate before deciding,' to give the workflow an error-recovery loop.

Tighten the eight 'Evaluate With These Dimensions' items and the 'Decision Heuristics' block to remove overlapping wording and reduce token weight.

DimensionReasoningScore

Conciseness

The body is efficient and assumes Claude's competence — it lists concrete file paths, discovery commands, dimensions, and an output template without padding or explaining known concepts — but the eight-item dimensions list and decision heuristics have minor wording that could be tightened.

4 / 5

Actionability

It gives concrete, executable guidance: specific repo paths, copy-paste `rg` discovery commands, an explicit markdown output template, and threshold-based decision heuristics; the main gap is that Context7 usage is described abstractly ('Resolve library ID first, then query focused questions') without a concrete query example.

4 / 5

Workflow Clarity

An eight-step sequenced 'Follow This Workflow' plus a final 'Quality Bar' verification checklist gives a clear sequence with a closing checkpoint, but there is no explicit mid-process error-recovery feedback loop, so it stops short of the top anchor.

4 / 5

Progressive Disclosure

The skill is self-contained with well-organized section headers and no content inlined that clearly belongs in a separate file; no external references are needed for this analysis methodology, which is appropriate, but it is over 50 lines with no one-level-deep reference structure.

4 / 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, well-scoped description that answers both 'what' and 'when' explicitly with concrete trigger phrases and clear boundary guidance against a sibling skill. Its only weakness is that the listed actions are abstract evaluation categories rather than crisply distinct operations, which keeps specificity and trigger-term quality at 4 rather than 5.

DimensionReasoningScore

Specificity

Enumerates five requirement types and seven evaluation criteria ('feature, enhancement, non-functional requirement, integration, or migration for value, feasibility, architecture fit, implementation impact, risk, delivery scope, or tradeoffs'), giving broad concrete coverage, but the actions are abstract evaluation categories rather than distinct concrete operations, leaving minor gaps versus a fully comprehensive 5.

4 / 5

Completeness

It clearly states what it does ('Analyze product and technical requirements for the PigeonPod project with software engineering rigor') and explicitly when to use it with concrete trigger phrases, plus negative boundary guidance, matching the top anchor.

5 / 5

Trigger Term Quality

Natural user-facing terms are well covered ('feature, enhancement, integration, migration, value, feasibility, risk, tradeoffs') in an explicit 'Use when users ask to evaluate...' clause, but a few common synonyms/variants are missing, so it is not fully comprehensive.

4 / 5

Distinctiveness Conflict Risk

It carves a clear PigeonPod-specific niche with distinct triggers and explicit conflict avoidance ('Do not use for bug triage or root-cause analysis; use `bug-analysis`'), minimizing wrong-skill activation.

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
aizhimou/pigeon-pod
Reviewed

Table of Contents

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