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agent-architecture-audit

エージェントおよび LLM アプリケーション向けのフルスタック診断。12 層のエージェントスタックにおけるラッパーリグレッション、メモリ汚染、ツール規律の失敗、隠れた修復ループ、レンダリング破損を監査します。重要度順の発見事項とコードファーストの修正を生成します。エージェントアプリケーション、自律ループ、または LLM を活用した機能を構築する開発者に必須です。

Quality

76%

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SecuritybySnyk

Passed

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Fix and improve this skill with Tessl

tessl review fix ./docs/ja-JP/skills/agent-architecture-audit/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

71%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 content is a well-structured, actionable audit playbook: concrete rg commands, a phased workflow with evidence and confidence checkpoints, and an explicit output contract. Its weaknesses are mild redundancy across the three passes over failure modes and a fully monolithic layout with no progressive disclosure — the report schema and failure-pattern details would serve better as one-level-deep reference files.

Suggestions

Move the JSON report schema and the detailed failure-pattern sections into one-level-deep reference files (e.g., references/report-schema.md, references/failure-patterns.md) and signal them clearly from SKILL.md, keeping SKILL.md as a lean overview.

Consolidate the triple coverage of failure modes (12-layer table, failure-pattern sections, quick diagnostic questions) into one canonical catalogue plus one triage table to reduce repetition.

Add an explicit validation feedback loop to the workflow, e.g., 'findings with confidence < 0.6 must be re-verified against logs or a second evidence source before reporting'.

DimensionReasoningScore

Conciseness

The body is dense and earns its place: compact tables for the 12 layers and severity model, symptom lists, and a copy-pasteable rg block, with no explanation of concepts Claude already knows. Not 5: the same failure modes are covered three times (12-layer table, failure-pattern sections, quick diagnostic questions) and the ~30-line report schema is inlined, so some tightening is possible.

4 / 5

Actionability

Gives executable guidance: six annotated rg commands for anti-pattern search, a four-phase workflow with concrete inputs/outputs per phase, a documented finding record (symptom, mechanism, source layer, evidence file:line, confidence), and a default code-first fix order. Not 5: evidence-gathering details like log collection and confidence calibration remain abstract, leaving minor gaps.

4 / 5

Workflow Clarity

The audit is clearly sequenced (スコープ → エビデンス収集 → 障害マッピング → 修正戦略) with required evidence references (ファイル:行) and a 0.0–1.0 confidence rating acting as validation checkpoints. Not 5: there is no explicit feedback loop (e.g., re-verify findings below a confidence threshold before reporting them), and no output-verification step after the report is produced.

4 / 5

Progressive Disclosure

Sections are well-organized with clear headers, but the skill is a single ~250-line file with no references/, scripts/, or assets/ — everything, including the 30-line JSON report schema and five detailed failure-pattern sections, is inlined. Not 4: content that would naturally live in one-level-deep reference files (report schema, failure-pattern catalogue) sits inline in SKILL.md.

3 / 5

Total

15

/

20

Passed

Description

71%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 is specific and distinctive, clearly stating what the skill audits and produces in concrete, third-person terms. Its main weakness is completeness: it lacks an explicit 'Use when...' trigger clause, relying instead on an audience claim ('必須です' for developers building agent apps), and it would also benefit from more natural trigger phrasings that users actually say when agent behavior degrades.

Suggestions

Add an explicit trigger clause to the description, e.g. "Use before releasing agent/LLM features to production, when agent behavior degrades after adding wrapper layers, or when the model works in the playground but breaks inside the agent."

Include natural user phrasings as trigger terms, such as 'agent got worse', 'model hallucinating in our app', or 'tool calls unreliable', so the skill matches how users actually describe the problem.

Drop the promotional "必須です" (essential) claim in favor of concrete trigger conditions — it adds no information and reads as over-claim.

DimensionReasoningScore

Specificity

"ラッパーリグレッション、メモリ汚染、ツール規律の失敗、隠れた修復ループ、レンダリング破損を監査します。重要度順の発見事項とコードファーストの修正を生成します" names five concrete audit targets and two concrete outputs, giving comprehensive coverage in third-person voice. Not 4: the action list is comprehensive rather than having 'minor gaps in coverage'.

5 / 5

Completeness

The 'what' is clear (audits the 12-layer stack for five failure classes, generates severity-ordered findings and code-first fixes), but there is no 'Use when...' clause or equivalent explicit trigger guidance; "エージェントアプリケーション、自律ループ、または LLM を活用した機能を構築する開発者に必須です" is an audience/promotional claim, not a trigger statement, which caps completeness at 3 per the guidelines. Not 4: 'when' is not merely under-specified, it is absent as explicit trigger guidance.

3 / 5

Trigger Term Quality

Natural terms include "エージェント", "LLM アプリケーション", "自律ループ", and "LLM を活用した機能", which are phrases a user needing this skill would plausibly say. Not 5: common user phrasings like 'agent got worse', 'hallucinating', or 'tool calls' appear only in the body, not the description.

4 / 5

Distinctiveness Conflict Risk

Auditing the "12 層のエージェントスタック" for wrapper regressions, memory pollution, and tool discipline failures is a clear niche with distinct triggers. Not 5: the broad framing 'フルスタック診断' for agent/LLM apps leaves minor overlap risk with general agent-debugging skills.

4 / 5

Total

16

/

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

affaan-m/ECC

Path

docs/ja-JP/skills/agent-architecture-audit/SKILL.md

Reviewed

Table of Contents

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