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interview-cheatsheet

Generate a long-form Chinese interview-prep cheat sheet on a specific ML/LLM topic — formulas with derivations, from-scratch PyTorch code, comparison tables, and 25 高频面试题 (L1 必会 / L2 进阶 / L3 顶级 lab). Use when the user says '写面试 cheat sheet', '写一份 X 教程', '帮我准备 Y 面试题', '出一份 X 速查', or wants a 600-1000 line Chinese tutorial on a specific ML topic.

74

Quality

93%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

88%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.

The body is a tight, highly actionable workflow with strong validation/feedback loops and well-signaled external references. Minor verbosity in provenance narration and some inlined reference material keep it just below perfect conciseness and progressive-disclosure scores.

DimensionReasoningScore

Conciseness

Mostly lean and assumes Claude's competence (style-guide tables, JSON review schema, direct command), with minor padding such as the 'Provenance' narrative and a few justifying sentences ('reviewer will check executability') that could be trimmed.

4 / 5

Actionability

Provides copy-paste-ready reviewer prompt, executable render command with all flags, concrete style-guide tables, and a full JSON audit-trail schema — concrete enough to execute end to end.

5 / 5

Workflow Clarity

Seven clearly sequenced steps with explicit validation checkpoints (cross-model review PASS/FAIL, render-stage 13-check review, no-commit gate) and a defined fix/retry loop with loop-detection heuristics and a stop condition.

5 / 5

Progressive Disclosure

Well-structured with clearly signaled references to canonical style files (attention_tutorial.md/.html, flow_matching_tutorial) one level deep; no bundle directories exist, and inlined style-guide/JSON content is borderline for a separate reference file.

4 / 5

Total

18

/

20

Passed

Description

100%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.

The description is exemplary: it states concrete capabilities, lists natural trigger phrases in both English and Chinese, and explicitly pairs 'what' with 'when'. It is concise yet comprehensive and uses appropriate third-person voice.

DimensionReasoningScore

Specificity

Names the domain (ML/LLM interview prep) and lists multiple concrete actions — 'formulas with derivations, from-scratch PyTorch code, comparison tables, and 25 高频面试题 (L1/L2/L3)' — giving comprehensive coverage.

5 / 5

Completeness

Explicitly answers both 'what' (generate a long-form Chinese cheat sheet with formulas/code/tables/25 questions) and 'when' (the 'Use when' clause with five concrete trigger phrases and a length signal).

5 / 5

Trigger Term Quality

Includes natural user phrases users would actually say — '写面试 cheat sheet', '写一份 X 教程', '帮我准备 Y 面试题', '出一份 X 速查' — plus synonyms (cheat sheet / tutorial / 面试题 / 速查).

5 / 5

Distinctiveness Conflict Risk

Clear niche — Chinese ML/LLM interview-prep cheat sheets of 600-1000 lines — with very specific triggers unlikely to collide with general tutorial or rendering skills.

5 / 5

Total

20

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

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: 1 missing

Warning

Total

13

/

16

Passed

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

Table of Contents

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