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aif-loop

Run a strict multi-iteration Reflex Loop with phases (PLAN, PRODUCE||PREPARE, EVALUATE, CRITIQUE, REFINE) to improve an artifact until quality gates pass or iteration limits are reached. Use when user asks for iterative refinement, quality-gated generation, or "generate -> critique -> refine" loops.

Quality

84%

Does it follow best practices?

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SKILL.md
Quality
Evals
Security

Quality

Content

81%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 workflow skill with genuine validation feedback loops and a clean one-level reference structure. Its main weakness is token efficiency: duplicated 'Step 0' sections, a confirmation guardrail repeated four-plus times, and oversized inline JSON templates that inflate the body.

Suggestions

Merge the two 'Step 0' sections (Load Config and Load Context) into a single context-loading step and state the confirm-criteria/max-iterations/budget guardrail once instead of repeating it four-plus times across Step 2.

Move the full run.json / current.json JSON templates and the history.jsonl example into a reference file (e.g. references/STATE-SCHEMA.md), keeping only the field invariants inline.

Fix or remove the references to 'docs/loop.md' and 'subagents/claude/agents/loop-orchestrator.md', which do not exist in the skill bundle — either ship them or drop the claim that the stop-condition list is repeated verbatim in three synced places.

DimensionReasoningScore

Conciseness

The body is dense operational policy rather than padded explanation, but it is noticeably repetitive: two distinct 'Step 0' sections (Load Config and Load Context), the 'always explicitly confirm criteria/max iterations/budget' guardrail stated at least four times across Step 2, and full 40-line run.json JSON templates inlined where a schema reference would do. This sits between 'mostly efficient' (4) and 'several padded sections' (2) — the duplication is real but consists of skill-specific rules, not explanations of concepts Claude already knows.

3 / 5

Actionability

Guidance is fully executable: exact JSON templates for current.json and run.json with concrete field values, a mkdir command, the exact history.jsonl event-name list with an example line, a complete command-mode parser, a stop-reason-to-status mapping table, and copy-paste-ready CLI examples covering the common cases.

5 / 5

Workflow Clarity

The iteration workflow is clearly sequenced (Steps 0-9, six phases with parallelism and a documented sequential fallback) with explicit validation (EVALUATE scoring against thresholds), feedback loops for error recovery (retry each failed phase once, rebuild corrupted run.json from history.jsonl), a precedence-ordered stop-condition contract, and user confirmation gating the destructive 'clean' operation.

5 / 5

Progressive Disclosure

Six real, one-level-deep reference files (ACTIVE-TIME-BUDGET, CRITERIA-TEMPLATES, PHASE-CONTRACTS, RULE-SCHEMA, TERMINAL-REPORT, CONTEXT-MANAGEMENT) are clearly signaled from the body with purpose statements. Not 5 because the body still runs ~500 lines with large inline JSON that could be split out, and it references 'docs/loop.md' and 'subagents/claude/agents/loop-orchestrator.md' which do not exist in the skill bundle; not 3 because references are well-signaled and appropriately split, not buried.

4 / 5

Total

17

/

20

Passed

Description

83%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 strong description: it states a concrete, multi-phase workflow in third person and pairs it with an explicit 'Use when' trigger clause containing natural phrasings. The only gaps are minor — a few missing trigger synonyms and slight overlap risk with generic refinement requests.

DimensionReasoningScore

Specificity

Names concrete actions — running a multi-iteration 'Reflex Loop' with six explicitly listed phases (PLAN, PRODUCE||PREPARE, EVALUATE, CRITIQUE, REFINE) 'until quality gates pass or iteration limits are reached'. Falls short of 5 because it does not enumerate what the loop operates on (artifact types, command modes) beyond 'improve an artifact'; clearly above 3 since it lists several specific actions rather than 1-2.

4 / 5

Completeness

Explicitly answers both 'what' (run a strict multi-iteration loop with named phases to improve an artifact until quality gates pass) and 'when' ('Use when user asks for iterative refinement, quality-gated generation, or "generate -> critique -> refine" loops') with concrete trigger phrases, matching the top anchor structure.

5 / 5

Trigger Term Quality

Includes natural phrases users would say: 'iterative refinement', 'quality-gated generation', and the colloquial 'generate -> critique -> refine' loops. Not 5 because common variations like 'iterate on this until it passes', 'self-refine', or 'keep improving' are missing; not 3 because the present terms go beyond a single generic keyword.

4 / 5

Distinctiveness Conflict Risk

A clear niche — iterative quality-gated refinement with distinct trigger phrases — keeps conflict risk low, but 'iterative refinement' is broad enough to overlap with generic review/refine skills, so it does not fully meet the 'minimal conflict risk' bar of 5.

4 / 5

Total

17

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (526 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

referenced_paths_exist

Referenced path issues: 2 missing

Warning

Total

13

/

16

Passed

Repository

lee-to/ai-factory

Path

skills/aif-loop/SKILL.md

Reviewed

Table of Contents

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