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aris-auto-review-loop

Autonomous multi-round research review loop. Repeatedly reviews via Codex MCP, implements fixes, and re-reviews until positive assessment or max rounds reached. Use when user says "auto review loop", "review until it passes", or wants autonomous iterative improvement.

71

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Critical

Do not install without reviewing

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.

A highly actionable, well-sequenced workflow with strong validation checkpoints and feedback loops, but it is verbose and monolithic. Tightening duplicated prompt templates and splitting large blocks into reference files would meaningfully improve it.

Suggestions

Deduplicate the Phase A prompt templates: define the shared senior-reviewer instructions once and have the hard/nightmare variants reference or prepend only their deltas (Reviewer Memory, debate steps) instead of repeating the full prompt.

Move the large per-difficulty prompt blocks, the Debate Protocol, and the Reviewer Memory template into separate reference files under references/ and link to them one level deep, reducing the SKILL.md body to an overview plus the core workflow.

Collapse the repeated 'Please act as a senior ML reviewer... Be brutally honest' framing and the round-2+ template's restated format into a single canonical prompt skeleton referenced by all phases.

DimensionReasoningScore

Conciseness

The body is largely procedural and avoids explaining concepts Claude already knows, but it is long with duplicated prompt templates (the medium and hard Phase A prompts differ only by a prepended memory block, 'Be brutally honest' recurs, and the round-2+ template restates the format), so it could be tightened.

2 / 3

Actionability

Provides fully executable guidance — concrete mcp__codex__codex calls with config, a copy-paste codex exec bash block, an exact REVIEW_STATE.json schema, and verbatim markdown templates — leaving little ambiguous.

3 / 3

Workflow Clarity

Sequences a clear Init → Loop (Phase A–E) → Termination flow with an explicit STOP CONDITION (score >= 6 AND verdict ready/almost), state-persistence checkpoints after each round, and a review→fix→re-review feedback loop for error recovery.

3 / 3

Progressive Disclosure

No bundle files exist (references/scripts/assets are absent) and the body is a single ~440-line monolith with well-organized sections but everything inline; the large per-mode prompt templates and debate protocol are candidates for one-level-deep reference files.

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.

A strong, well-structured description that concisely states concrete capabilities and provides explicit, natural-language trigger guidance in third person. It cleanly answers both what the skill does and when to use it.

DimensionReasoningScore

Specificity

Names multiple concrete actions — 'Repeatedly reviews via Codex MCP', 'implements fixes', and 're-reviews until positive assessment or max rounds reached' — matching the anchor for listing several specific concrete actions.

3 / 3

Completeness

Explicitly answers both 'what' (multi-round review→fix→re-review loop via Codex MCP) and 'when' via a clear 'Use when...' clause with explicit triggers.

3 / 3

Trigger Term Quality

Includes natural phrases a user would actually say — 'auto review loop', 'review until it passes', 'autonomous iterative improvement' — giving good coverage rather than jargon.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (autonomous iterative review loop via Codex MCP) with distinct triggers, making it unlikely to fire for the wrong skill.

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
OpenLAIR/dr-claw
Reviewed

Table of Contents

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