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collab-proof

Use when you want to understand what Claude contributed vs what you drove in a session. Triggers on: /collab-proof, session retrospective, ai contribution analysis, collaboration evidence, what did claude do.

61

Quality

77%

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SecuritybySnyk

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tessl review fix ./engineering/collab-proof/skills/collab-proof/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

78%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 instruction body with concrete commands, numeric scoring rubrics, and complete output templates. Its main weaknesses are the orphaned references/ bundle (never linked from the body) and the inlined HTML/CSS specification, plus minor validation gaps around token collection and file writes.

Suggestions

Link each references/*.md file from the relevant section (e.g., 'Design rationale: See [references/session-documentation-patterns.md]') so the existing bundle is discoverable.

Move the fixed HTML/CSS spec (~70 lines) into a template file under references/ or assets/ and reference it one level deep, keeping SKILL.md as the overview.

Add a validation step for the token-collection script: confirm the most-recent .jsonl matches the current project/session before parsing, and fall back gracefully if it does not.

DimensionReasoningScore

Conciseness

The body is dense and imperative throughout (e.g., 'Run git log --oneline -10 and git diff --stat HEAD~3..HEAD first', 'Prune any frame scoring < 0.4') with no explanations of concepts Claude already knows. Minor trimming is possible: the WORKLOG line format is specified twice and signal-level outputs are stated in both Layer 01 and Layer 03, so it does not quite reach the 'every token earns its place' anchor.

4 / 5

Actionability

Guidance is fully executable: exact git commands, numeric frame-scoring anchors, complete markdown output templates with clearly bracketed placeholders, a copy-paste-ready python3 -c token-collection script, and an exact bash heredoc for writing the HTML file. The common HIGH/MEDIUM/LOW cases are each covered with concrete instructions.

5 / 5

Workflow Clarity

The three-layer sequence is clearly ordered (signal detection -> WorkIntentClassifier -> output), with explicit branching per signal level and a gating checkpoint ('Show the user: Signal: HIGH / MEDIUM / LOW'). Minor validation gaps remain: the token script blindly takes the most-recent .jsonl with no check that it belongs to the current project, and there is no verification step after the file writes, so the explicit validate-fix-retry pattern of the level-5 anchor is not met.

4 / 5

Progressive Disclosure

The body itself is well-sectioned by layer, but the four files in references/ (ai-collaboration-evidence.md, developer-portfolio-proof.md, session-documentation-patterns.md, tamper-evident-proof.md) are never referenced anywhere in the body, making the bundle undiscoverable. Meanwhile ~70 lines of fixed HTML/CSS spec are inlined in SKILL.md where a template file belongs — matching the anchor 'references present but not clearly signaled; content that should be separate is inline' rather than level 4's 'references mostly clear'.

3 / 5

Total

16

/

20

Passed

Description

65%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 well-formed description with an explicit and concrete trigger clause, but it describes the skill's purpose rather than its concrete outputs, leaving the 'what' vague. Trigger coverage is good though not exhaustive, and the niche is reasonably distinct.

Suggestions

Name 2-3 concrete outputs in the description (e.g., 'generates DECISIONS.md entries, session-history records, and an HTML proof page showing what Claude contributed vs what you drove') to lift specificity and completeness.

Add natural trigger synonyms such as 'session summary', 'who wrote what', or 'AI pair programming' to broaden keyword coverage.

Clarify the when-clause scope: state that it applies at session end or mid-session checkpoints so it does not fire on every session-summary request.

DimensionReasoningScore

Specificity

The description names the domain ("understand what Claude contributed vs what you drove in a session") but the only stated action is the generic 'understand'; no concrete outputs such as DECISIONS.md entries, session-history records, or the HTML proof page are mentioned. It sits at the 'names the domain, actions minimal or generic' anchor rather than level 3, which requires at least one or two concrete actions.

2 / 5

Completeness

Both parts are present: an explicit 'Use when you want to...' clause plus concrete 'Triggers on:' phrases (when), and 'understand what Claude contributed vs what you drove in a session' (what). The 'what' is under-specified — it describes purpose rather than what the skill actually produces — so it does not reach the level-5 anchor's clearly concrete what-and-when pair.

4 / 5

Trigger Term Quality

"Triggers on: /collab-proof, session retrospective, ai contribution analysis, collaboration evidence, what did claude do" gives good natural coverage including the slash command and a very natural phrase. It misses common synonyms like 'session summary', 'who wrote what', or 'AI pair programming', so it falls short of the comprehensive level-5 anchor.

4 / 5

Distinctiveness Conflict Risk

Terms like 'collab-proof', 'ai contribution analysis', and 'collaboration evidence' carve a distinct niche with minimal conflict risk. There is minor overlap risk with generic session-summary, journaling, or retrospective skills triggered by the somewhat broad 'session retrospective' phrase.

4 / 5

Total

14

/

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

alirezarezvani/claude-skills

Path

engineering/collab-proof/skills/collab-proof/SKILL.md

Reviewed

Table of Contents

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