CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

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.

Quality

96%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

96%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 dense, highly operational skill body: exact commands, verbatim reviewer prompts, explicit validation gates, and baked-in lessons-learned conventions make it fully executable. The only room for improvement is minor trimming of anecdotal sections and optionally moving the large inline prompt/JSON templates into reference files.

Suggestions

Trim the Provenance section and the Video Generation tutorial anecdote in Step 4 to one or two sentences — they add history, not execution guidance.

Move the verbatim Step-3 reviewer prompt and the Step-6 audit-JSON skeleton into a references/ file (e.g. references/reviewer-prompt.md and references/audit-format.json), keeping only invocation notes inline, to slim the main body and ease maintenance.

DimensionReasoningScore

Conciseness

The body is operational throughout — style-rule tables with reasons, exact commands, verbatim reviewer prompts — with almost no explanation of concepts Claude already knows. Not 5: minor trims are possible, e.g. the Provenance section and the "Video Generation tutorial (May 2026) went to 5 rounds" anecdote add narrative that isn't strictly needed to execute.

4 / 5

Actionability

Everything is copy-paste ready: the exact `python3 skills/render-html/scripts/render_html.py ...` command with all flags, the full verbatim reviewer prompt with 10 named checks and a JSON return contract, the exact audit-trail JSON schema, and a literal final report template. Not below 5: concrete examples (good/bad topics, slug mapping, reference invocations) cover the common cases.

5 / 5

Workflow Clarity

Seven clearly sequenced steps (plan → draft → fresh review → fix loop → render → combine audit → stop/report) with explicit PASS/WARN/FAIL validation gates, a fix-and-re-review feedback loop with concrete keep-going/stop heuristics, a render-stage re-check, and a detect-only commit policy. Not below 5: validation checkpoints and error-recovery loops are explicit at every stage.

5 / 5

Progressive Disclosure

Well-sectioned single-file skill; external references (docs/tutorials/attention_tutorial.md as canonical style, ../shared-references/reviewer-backends.md) are one level deep and clearly signaled. Not 5: no bundle files exist, and sizable always-used templates (the ~30-line reviewer prompt, the audit-JSON skeleton) are fully inline; these are defensible inline but could live in a references/ file to slim the main body.

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 with quantities (25 questions, 600-1000 lines, L1/L2/L3 tiers), gives an explicit 'Use when' clause with verbatim natural trigger phrases in both Chinese and English, and occupies a distinct niche. Both what and when are answered clearly.

DimensionReasoningScore

Specificity

"Generate a long-form Chinese interview-prep cheat sheet... formulas with derivations, from-scratch PyTorch code, comparison tables, and 25 高频面试题" lists multiple specific concrete actions with comprehensive coverage of the output artifacts. Not below 4: coverage includes every major output type (formulas, code, tables, questions) with quantity and difficulty-tier detail.

5 / 5

Completeness

Explicitly answers what ("Generate a long-form Chinese interview-prep cheat sheet... with formulas, PyTorch code, comparison tables, 25 questions") and when ("Use when the user says '写面试 cheat sheet'...") with concrete trigger phrases. Not below 4: the when-clause is explicit and enumerates multiple natural triggers.

5 / 5

Trigger Term Quality

Natural phrases users would actually say are quoted verbatim — '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 an English-language catch ("wants a 600-1000 line Chinese tutorial"). Not below 4: it covers synonyms in both Chinese and English rather than only technical jargon.

5 / 5

Distinctiveness Conflict Risk

Clear niche (long-form Chinese ML/LLM interview-prep cheat sheets) with distinct trigger phrases unlikely to fire for other skills. Not below 4: the Chinese-language triggers and 600-1000-line scope pin it to a narrow, unambiguous use case.

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, 1 suspicious

Warning

Total

13

/

16

Passed

Repository

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

Path

skills/skills-codex/interview-cheatsheet/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.