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auto-review-loop-llm

Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".

Quality

69%

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SecuritybySnyk

Passed

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tessl review fix ./skills/skills-codex/auto-review-loop-llm/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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.

The skill delivers a well-sequenced, largely executable autonomous review loop with concrete API call methods, state persistence, and documentation templates. Its main weaknesses are redundancy (duplicated curl and prompt templates, repeated token-field guidance, changelog commentary) and a monolithic structure that inlines provider configuration and fallback details that should live in reference files.

Suggestions

Move the provider table, curl fallback variants, and the shared token-field guidance into a single reference file (e.g. references/llm-providers.md) and keep one short call example in SKILL.md, eliminating the triple repetition of max_tokens/max_completion_tokens guidance.

Add an explicit validation/recovery step in Phase B for unparseable responses or failed API calls (e.g. retry, or apply the documented 504-timeout fallback model), and make Phase D concrete by naming how experiments are monitored.

Delete changelog-style meta commentary ('Earlier wording used "or" + a stale verdict set; the AND form is authoritative') and state the authoritative condition once, trimming the Round 2+ template to only its delta from the Phase A prompt.

DimensionReasoningScore

Conciseness

The body is mostly efficient but visibly duplicated: the max_tokens/max_completion_tokens guidance appears three times, curl request templates appear in both 'API Call Method' and 'Phase A', and the Round 2+ prompt template restates the Phase A prompt verbatim, plus changelog noise like 'Earlier wording used "or" + a stale verdict set'.

3 / 5

Actionability

Concrete MCP tool invocations, copy-paste-ready curl commands, a provider configuration table, a JSON state schema, and markdown templates make the guidance mostly executable; Phase D ('Monitor remote experiments') is direction-only with no command or method.

4 / 5

Workflow Clarity

The loop is clearly sequenced (Initialization → Phases A–E → Termination) with an explicit STOP condition, per-round state persistence, and recovery from REVIEW_STATE.json, but there is no validation or recovery step for malformed or unparseable reviewer responses or failed API calls (the 504-timeout fallback is mentioned in passing, not as a workflow step).

4 / 5

Progressive Disclosure

A single ~270-line file inlines content that belongs in separate reference files (the eight-provider table, both curl fallback variants, and the prompt templates), and its only external references point outside the bundle via ../../shared-references/ paths, so structure is present but content is not appropriately split.

3 / 5

Total

14

/

20

Passed

Description

62%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 answers both what and when with explicit trigger phrases and stays concise, but it describes the mechanism (MCP/env configuration) rather than the skill's actual behavior (iterate review → fix → re-review until positive assessment). Trigger phrases are self-referential command names rather than natural user language, limiting discoverability.

Suggestions

State the concrete loop behavior in the description, e.g. 'Iteratively reviews research work via an external LLM, implements the fixes it identifies, and re-reviews until the assessment is positive or a round limit is hit.'

Broaden trigger terms beyond the literal command names to natural phrases like 'use when the user wants an LLM to critique and improve their research or paper'.

Drop or compress the configuration detail ('Configure via llm-chat MCP server or environment variables') in favor of capability language; configuration belongs in the body.

DimensionReasoningScore

Specificity

Names the domain ('Autonomous research review loop using any OpenAI-compatible LLM API') and one action area ('Configure via llm-chat MCP server or environment variables'), but never states the loop's concrete actions such as review, implement fixes, and re-review, so coverage is not comprehensive.

3 / 5

Completeness

Both 'what' (autonomous research review loop via OpenAI-compatible LLM API) and 'when' ('Trigger with "auto review loop llm" or "llm review"') are explicitly present, but the 'what' is high-level and the 'when' is limited to literal invocation phrases rather than natural use conditions.

4 / 5

Trigger Term Quality

Explicit trigger phrases ('auto review loop llm', 'llm review') plus 'llm-chat MCP server' and 'OpenAI-compatible LLM API' are relevant, but common natural variations users would say ('review my paper', 'critique my research', 'research feedback') are missing.

3 / 5

Distinctiveness Conflict Risk

The LLM-API-driven research review loop niche with llm-chat MCP references is mostly distinct, with only minor overlap risk against generic review/critique skills.

4 / 5

Total

14

/

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

relative_links

Relative link issues: 3 suspicious

Warning

Total

15

/

16

Passed

Repository

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

Path

skills/skills-codex/auto-review-loop-llm/SKILL.md

Reviewed

Table of Contents

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