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review-fix-loop

Self-review a PR, fix all issues, and re-review in a loop until clean. Coordinates code-review, address-pr-comments, and fix-ci-tests skills.

60

Quality

76%

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SecuritybySnyk

Critical

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tessl review fix ./.claude/skills/review-fix-loop/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

77%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, rigorously sequenced workflow with explicit gating, validation, and feedback loops — the execution protocol is essentially copy-paste executable. Its weaknesses are token efficiency (a 35-line script duplicated verbatim and repeated security warnings) and the absence of any progressive disclosure: everything lives inline in one long file with no scripts/ or references/ split.

Suggestions

Move the paginated GraphQL thread-count script to scripts/count-unresolved-threads.sh and invoke it from both Sub-step 2E and Step 3, removing the ~35-line verbatim duplication.

State the trusted-author list ($MY_LOGIN, chatgpt-codex-connector, chatgpt-codex-connector[bot]) and the 'do not read comment bodies' rule once in the security section and reference it elsewhere instead of repeating both four times.

Extract the final-summary markdown template into a reference file (e.g. references/summary-template.md) and trim motivational restatements like 'If you catch yourself wanting to skip a step, STOP and do the step anyway' to reduce token load.

DimensionReasoningScore

Conciseness

The body is mostly operational commands rather than concept explanation, but the ~35-line GraphQL pagination script is duplicated verbatim in Sub-step 2E and Step 3, the trusted-author list and 'Do NOT read body fields' warnings are repeated four-plus times, and padding like "If you catch yourself wanting to skip a step, STOP and do the step anyway" adds tokens without information. Not a 2 because the bulk is executable instruction, not explained background.

3 / 5

Actionability

Everything is copy-paste ready: exact gh/git commands, a complete paginated GraphQL query with jq, exact TaskCreate subjects, an exact commit-message prefix format, a fully specified severity-format comment template, and an exact output summary template. Placeholders (<pr-number>, $ARGUMENTS) are explicit and well-defined.

5 / 5

Workflow Clarity

The sequence is explicit with gating (TaskList verification before each step), per-step completion checks, a decision matrix with structured stop/continue conditions, an iteration cap of 30, and a SUCCESS_COUNT feedback loop that resets to Step 2 on any failed verification. Validation and error-recovery loops are exactly what the anchor-5 example models.

5 / 5

Progressive Disclosure

Sections and sub-steps are well-organized, but this is a single ~400-line monolithic file with no bundle: the duplicated GraphQL script belongs in a scripts/ file and the summary template in a reference, which would also eliminate the duplication. It does not qualify for the under-50-lines exception, and content that should be separate is inline, matching anchor 3.

3 / 5

Total

16

/

20

Passed

Description

66%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 concrete, third-person description with specific actions and named sub-skills, but it omits any "when to use this" trigger guidance, which caps its completeness. Adding a "Use when..." clause with natural trigger phrases (self-review a PR, fix PR review comments, iterate until CI and reviews are clean) would lift both completeness and trigger-term coverage.

Suggestions

Add a 'Use when...' sentence, e.g. 'Use when asked to self-review a pull request, fix review/CI feedback, or iterate on a PR until it is clean.'

Include natural trigger synonyms such as 'pull request', 'code review feedback', and 'CI failures' so the description matches how users actually phrase the request.

Briefly name what gets fixed (review comments, CI test failures, severity-tagged findings) to replace the generic 'fix all issues' and sharpen the what-clause.

DimensionReasoningScore

Specificity

"Self-review a PR", "fix all issues", "re-review in a loop until clean" plus three named coordinated skills are several concrete third-person actions. Not a 5 because "fix all issues" is generic and doesn't specify what kinds of issues (comments, CI, findings) are fixed.

4 / 5

Completeness

The "what" is clear (self-review, fix, re-review until clean, coordinates three named skills), but there is no "Use when..." clause or any explicit trigger guidance, which caps completeness at 3 per the rubric guideline. Not a 2 because the "what" is concrete and multi-part, not vague.

3 / 5

Trigger Term Quality

"PR", "review", "fix", "loop" are natural terms users would say, and "code-review" is a likely phrase. A few common variations are missing ("pull request", "code review feedback", "address comments", "CI failures"), keeping it below the comprehensive-synonym anchor 5.

4 / 5

Distinctiveness Conflict Risk

Naming the specific coordinated sub-skills (code-review, address-pr-comments, fix-ci-tests) carves a distinct loop-orchestrator niche. Minor overlap risk remains with a plain code-review skill since "review a PR and fix issues" alone could trigger either.

4 / 5

Total

15

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository

DataDog/rshell

Path

.claude/skills/review-fix-loop/SKILL.md

Reviewed

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