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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".

68

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

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 autonomous loop with concrete commands and an explicit stop condition. Its weaknesses are redundancy (curl and prompt templates each appear twice) and inline bulk that could be offloaded to reference files for better progressive disclosure.

Suggestions

De-duplicate the curl fallback and the reviewer prompt so each appears once; reference a shared template to cut roughly 60 lines of repeated content.

Remove the stale-wording meta-commentary from the POSITIVE_THRESHOLD note; state the authoritative AND condition directly without referencing earlier drafts.

Move the provider table and the MCP settings.json example into a references/ file (e.g. LLM_PROVIDERS.md) and link to it from the body to improve progressive disclosure.

DimensionReasoningScore

Conciseness

The body is mostly operational and efficient, but repeats the curl fallback and the review prompt template in two places and includes self-referential meta-commentary ("Earlier wording used 'or' + a stale verdict set; the AND form is authoritative") that Claude does not need.

2 / 3

Actionability

It provides concrete, executable artifacts: a real MCP tool-call signature, a copy-paste curl command against ${LLM_BASE_URL}, a literal REVIEW_STATE.json schema, and a markdown documentation template with explicit placeholders.

3 / 3

Workflow Clarity

The process is clearly sequenced (Initialization → Phase A–E loop → Termination) with an explicit STOP validation checkpoint in Phase B ("score >= 6 AND verdict ∈ {ready, almost}") and a built-in review→fix→re-review feedback loop plus state recovery.

3 / 3

Progressive Disclosure

Sections are well-organized and the only file references (../../shared-references/*.md) are one level deep and clearly signaled, but the ~240-line body keeps the provider table, MCP config, and duplicate prompt templates inline rather than splitting them into reference files.

2 / 3

Total

10

/

12

Passed

Description

90%

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 clear, distinct, and supplies explicit trigger guidance plus the configuration surface area, covering both what and when. Its main weakness is specificity: it states the capability at a high level rather than listing the concrete review→fix→re-review actions.

DimensionReasoningScore

Specificity

It names the core capability ("Autonomous research review loop") and concrete mechanisms ("any OpenAI-compatible LLM API", "llm-chat MCP server", "environment variables"), but does not enumerate the multiple distinct actions (review, implement fixes, re-review) the way a score-3 anchor does.

2 / 3

Completeness

It answers both "what" ("Autonomous research review loop using any OpenAI-compatible LLM API") and "when" via an explicit trigger clause ("Trigger with..."), satisfying the explicit-trigger requirement that allows a top score.

3 / 3

Trigger Term Quality

Explicit trigger phrases are given — "Trigger with \"auto review loop llm\" or \"llm review\"" — which are natural invocation terms a user would plausibly say, giving good coverage rather than jargon-only keywords.

3 / 3

Distinctiveness Conflict Risk

The niche is specific (autonomous research review loop over an OpenAI-compatible LLM API) with distinct trigger phrases, making it unlikely to fire for unrelated skills despite the mildly generic "llm review" token.

3 / 3

Total

11

/

12

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.

Validation15 / 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
Reviewed

Table of Contents

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