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research-implement-feature

Build a working artifact from a plain "implement X for me" request: a running end-to-end spine first, then one feature per rung, with every under-determined decision written to an assumption ledger BEFORE the code that depends on it and a cross-model sweep for the ones that slipped through undeclared. Use when user says "给我实现", "implement X", "帮我做一个能跑的", "先搭个原型再加功能", "build this feature", "prototype then extend", or hands over a capability description rather than an experiment plan.

69

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

81%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 highly actionable, well-sequenced workflow skill with concrete schemas, prompts, and validation gates. Its main weakness is verbosity from rhetorical prose and restated anti-patterns, and a long body that inlines content that could be offloaded to references.

Suggestions

Trim the rhetorical/philosophical prose (e.g. "A ledger reconstructed at the end… is a changelog") and fold the anti-patterns section into the phases it restates, since the invariants are already stated where they apply.

Move the full Phase 4 sweep prompt and the worked example into reference files under references/ and link to them, leaving SKILL.md as a leaner overview.

Consider condensing the redundant prose between the 'Two invariants', 'Acceptance-gate provenance', and 'Anti-patterns to refuse' sections, which restate the same declare-before-act and Type-A/Type-B distinctions.

DimensionReasoningScore

Conciseness

Mostly efficient operational guidance, but substantial rhetorical/philosophical prose and an anti-patterns section that restates invariants already covered in the phases could be tightened.

3 / 5

Actionability

Provides a fully copy-paste-ready Phase 4 prompt with a JSON output spec, concrete Codex config, ledger and build-note schemas, real commands (git diff, git rev-parse), and a concrete worked example.

5 / 5

Workflow Clarity

Phases 0–5 are clearly sequenced with explicit validation gates (exit 0 required, re-run every earlier rung), fix/sweep budgets, batch points, and feedback loops for error recovery.

5 / 5

Progressive Disclosure

Well-signaled one-level-deep shared-references links plus a See Also section give good navigation, but the body inlines large blocks (the full Phase 4 prompt, worked example, anti-patterns) that could be split into reference files.

4 / 5

Total

17

/

20

Passed

Description

96%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, specific description that clearly states what the skill does and when to use it, with rich bilingual trigger phrases. Its only weakness is mild overlap risk from the broader "implement X" / "build this feature" triggers.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — build an end-to-end spine, add one feature per rung, write an assumption ledger before dependent code, run a cross-model sweep — covering the workflow comprehensively.

5 / 5

Completeness

Explicitly answers both what (spine-then-rungs build with a declare-before-act ledger and cross-model sweep) and when ("Use when user says… or hands over a capability description rather than an experiment plan") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes six natural trigger phrases across two languages with synonyms ("implement X", "build this feature", "prototype then extend", "给我实现", "帮我做一个能跑的", "先搭个原型再加功能"), matching what a user would actually say.

5 / 5

Distinctiveness Conflict Risk

Carves a clear niche with a negative boundary against experiment plans, but triggers like "implement X" and "build this feature" are somewhat generic and could overlap with general build skills.

4 / 5

Total

19

/

20

Passed

Validation

68%

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

Validation — 11 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (584 lines); consider splitting into references/ and linking

Warning

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 15 suspicious

Warning

referenced_paths_exist

Referenced path issues: 3 missing

Warning

Total

11

/

16

Passed

Repository
wanshuiyin/Auto-claude-code-research-in-sleep
Reviewed

Table of Contents

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