CtrlK
BlogDocsLog inGet started
Tessl Logo

proof-writer

Writes rigorous mathematical proofs for ML/AI theory. Use when asked to prove a theorem, lemma, proposition, or corollary, fill in missing proof steps, formalize a proof sketch, 补全证明, 写证明, 证明某个命题, or determine whether a claimed proof can actually be completed under the stated assumptions.

71

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

77%

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 clear, actionable, well-sequenced proof-writing workflow with strong verification loops, but it is monolithic with no external references and carries some redundant restatements across sections.

Suggestions

Remove redundant restatements: drop the duplicated extract-items in '## Inputs' (already covered by Step 1) and consolidate the Step 5 must-include list with '## Required File Structure'.

Consider splitting the proof file-structure template and the three output modes into a referenced PROOF_TEMPLATE.md so SKILL.md stays a lean overview.

Trim '## Key Rules' to only items not already stated inline in the workflow steps to reduce token cost.

DimensionReasoningScore

Conciseness

Mostly efficient but carries redundancy: '## Inputs' overlaps '### Step 1', the Step 5 must-include list duplicates '## Required File Structure', and '## Key Rules' restates rules already stated inline.

2 / 3

Actionability

Though instruction-only (no code), the guidance is concrete and copy-paste ready: an exact file-structure template, a named forbidden-phrase list ('clearly', 'obviously', ...), and explicit math formatting rules ($...$, $$...$$).

3 / 3

Workflow Clarity

Steps 1–6 are clearly sequenced with an explicit Step 6 verification checklist and a feedback loop that downgrades to a blockage report when a step cannot be justified, matching the validation-checkpoints anchor.

3 / 3

Progressive Disclosure

Sections are well organized, but there are no bundle files and all ~200 lines (including the file template and three output modes) live inline in SKILL.md rather than being split into one-level-deep references.

2 / 3

Total

10

/

12

Passed

Description

100%

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 specific, trigger-rich, complete, and distinctive, with explicit Use-when guidance and concrete actions in third-person voice. It is a strong model description with no clear weaknesses.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('prove a theorem, lemma, proposition, or corollary, fill in missing proof steps, formalize a proof sketch, ... determine whether a claimed proof can actually be completed'), matching the score-3 anchor for several specific actions.

3 / 3

Completeness

Explicitly answers what ('Writes rigorous mathematical proofs for ML/AI theory') and when ('Use when asked to prove...'), satisfying the both-what-and-when anchor with an explicit Use-when clause.

3 / 3

Trigger Term Quality

Covers natural phrasings a user would say ('prove a theorem', 'fill in missing proof steps', 'formalize a proof sketch') plus Chinese variants (补全证明, 写证明, 证明某个命题), giving good trigger coverage.

3 / 3

Distinctiveness Conflict Risk

A clear niche (rigorous proof writing for ML/AI theory) with distinct, specialized triggers unlikely to fire for unrelated skills; written in third person.

3 / 3

Total

12

/

12

Passed

Validation

87%

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

Validation14 / 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

Total

14

/

16

Passed

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