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agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

67

Quality

84%

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

75%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 well-structured, expert-level diagnostic skill body: clear activation criteria, a layered failure taxonomy, concrete rg searches, a severity model, and a machine-readable report schema, all without explaining basics Claude already knows. The main weaknesses are triple-redundant symptom lists, reference-shaped material (report schema, command catalog) inlined in an already-long file, and no explicit post-fix validation checkpoint.

Suggestions

Consolidate the three overlapping symptom lists (12-layer table, Failure Pattern symptoms, Quick Diagnostic Questions) into one canonical mapping — e.g. keep the table and have the Quick Questions reference layer numbers instead of restating symptoms.

Move the Report Schema JSON block (and optionally the rg command catalog) into a references/report-schema.md file and reference it one level deep, shortening SKILL.md.

Add an explicit validation step at the end of Phase 4, e.g. 'Re-run the triggering scenario and confirm the symptom is gone before closing the finding' — this closes the feedback loop the workflow currently lacks.

DimensionReasoningScore

Conciseness

The body is dense and table-driven with no explanation of concepts Claude already knows, but it enumerates symptoms three times — the 12-layer table's 'What Goes Wrong' column, the Failure Pattern 'Symptoms' bullets, and the Quick Diagnostic Questions table — which is redundant token spend. It is not a 5 because of that redundancy; not a 3 because there is no padded or hand-holding prose anywhere.

4 / 5

Actionability

The rg command block in Phase 2 is copy-paste ready with per-anti-pattern searches, and evidence sources are concretely named (logs, config, memory files). Phase 1 ('Define what you're auditing') and Phase 3 field descriptions are more descriptive than executable, leaving minor gaps. Not a 5 because there is no worked example of applying the workflow or running the full audit end-to-end; not a 3 because the concrete material is executable, not pseudocode.

4 / 5

Workflow Clarity

The audit workflow has a clear four-phase sequence (Scope, Evidence Collection, Failure Mapping, Fix Strategy) with the Quick Diagnostic Questions table acting as a triage checklist and confidence scores on findings. It is not a 5 because there is no explicit validation or feedback checkpoint (e.g. verifying a recommended fix actually resolves the symptom); not a 3 because the sequence is well-defined with checkpoints present via the diagnostic table and confidence discipline. This is a read-only diagnostic, so the destructive-operation cap does not apply.

4 / 5

Progressive Disclosure

A single well-sectioned file with clear headers, no buried or nested references, and a clearly signaled 'Related Skills' boundary section — there are no bundle files, so nothing is misfiled. It is not a 5 because ~250 lines of body inline the Report Schema JSON and the rg command catalog, reference-shaped material that would sit better in separate files; not a 3 because the structure and navigation are genuinely good, with no content that clearly belongs elsewhere dominating the file.

4 / 5

Total

16

/

20

Passed

Description

88%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 concrete capabilities (auditing named failure classes across a 12-layer stack, producing severity-ranked fixes) and gives an explicit, concrete 'Use when...' trigger in third-person voice. The main gaps are missing natural synonym triggers (chatbot, assistant, agent is broken) and mild overlap risk with general agent-debugging skills.

Suggestions

Add one or two everyday user phrasings as triggers, e.g. 'Use when the user says the agent is getting worse, tools are flaky, or it worked in the playground but breaks in the app' — these natural phrases are currently only in the body, not the description.

Sharpen distinctiveness by naming the boundary inline, e.g. 'Diagnostic only (root-cause analysis), not benchmarking or security review' to reduce overlap with agent-eval and security skills.

Drop the audience-marketing clause 'Essential for developers building agent applications, autonomous loops, or any LLM-powered feature' — it restates the triggers with no new information and adds token cost.

DimensionReasoningScore

Specificity

The description enumerates multiple concrete, specific actions — 'Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption' and 'Produces severity-ranked findings with code-first fixes' — giving comprehensive coverage rather than just several actions. It is not a 4 because the coverage is both broad and precise about failure classes and the deliverable, not leaving minor gaps.

5 / 5

Completeness

It explicitly answers both what ('Full-stack diagnostic... Audits the 12-layer agent stack... Produces severity-ranked findings with code-first fixes') and when ('Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack') with concrete trigger phrases. A 4 would require the 'when' to be less explicit, which it is not.

5 / 5

Trigger Term Quality

Good keyword coverage with natural terms like 'agent', 'LLM', 'misbehaves', and 'before shipping an agent stack', but common user-side synonyms such as 'chatbot', 'assistant', 'agent is broken/flaky', or 'debugging an agent' are absent. It is not a 5 because a user describing the problem in everyday words would not reliably hit these triggers; not a 3 because the terms present are natural, not jargon-only.

4 / 5

Distinctiveness Conflict Risk

The 12-layer agent-stack audit is a clear niche distinct from code review or security scanning, but the broad trigger 'agent or LLM feature misbehaves' could also match general agent-debugging skills, creating minor overlap risk. It is not a 5 because of that overlap; not a 3 because the niche and its triggers are far more specific than generic document/skill examples.

4 / 5

Total

18

/

20

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.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository

affaan-m/ECC

Path

skills/agent-architecture-audit/SKILL.md

Reviewed

Table of Contents

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