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Audit

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oliver-kriska
audit

Project health audit and health check — architecture, performance, tests, dependencies, code quality. Use when assessing overall project health, before releases, or after refactors.

Overview

Publisheroliver-kriska
Repositoryclaude-elixir-phoenix
Skill nameaudit
Stars
555
Forks
40
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by oliver-kriska on GitHub. Read the source before you install it.

Installation

Install the Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/oliver-kriska/claude-elixir-phoenix.git /tmp/claude-elixir-phoenix
mkdir -p .claude/skills
cp -r /tmp/claude-elixir-phoenix/plugins/elixir-phoenix/skills/audit .claude/skills/audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Project Health Audit

Comprehensive project-wide health assessment using 5 parallel specialist subagents.

Usage

/phx:audit              # Full audit (default)
/phx:audit --quick      # 2-3 minute pulse check
/phx:audit --focus=security   # Deep dive single area
/phx:audit --focus=performance
/phx:audit --since abc123   # Incremental audit since commit
/phx:audit --since HEAD~10  # Audit last 10 commits

When to Use

  • Quarterly health checks
  • Before major releases
  • After large refactors
  • New team member onboarding (understand codebase health)

Iron Laws

  1. Wait for ALL agents before synthesizing — Partial results create misleading health scores because cross-category correlations get missed
  2. Scope agent prompts to specific directories — Vague prompts like "analyze the codebase" produce generic findings that waste tokens and miss real issues
  3. Never compare scores across projects — Scoring methodology depends on project size and maturity; only track trends within the same project
  4. Quick mode before full mode — Run --quick first to catch compile/test failures before spending tokens on 5 parallel agents

Subagent Architecture

Spawn 5 specialists in parallel using Agent tool. Each call routes to a declared-model plugin specialist (sonnet/opus) so the work doesn't fall through to general-purpose (Opus by default):

SubagentFocusOutput FileRoutes to
Architecture ReviewerStructure quality, coupling, cohesionarch-review.mdphoenix-patterns-analyst (sonnet)
Performance AuditorN+1, indexes, bottlenecks, scalabilityperf-audit.mdgeneral-purpose (TODO: no perf specialist exists yet)
Security AuditorOWASP scan, auth patterns, secretssecurity-audit.mdsecurity-analyzer (opus)
Test Health AuditorCoverage, quality, flaky teststest-audit.mdtesting-reviewer (sonnet)
Dependency AuditorVulnerabilities, outdated, unuseddeps-audit.mdgeneral-purpose (TODO: per-package hex-deps-triager only)

Workflow

Step 1: Create Task List and Spawn All 5 Auditors (Parallel)

Create Claude Code tasks for real-time progress visibility:

For each auditor:
  TaskCreate({subject: "{Area} audit", activeForm: "Auditing {area}..."})
  TaskUpdate({taskId, status: "in_progress"})

Then spawn all 5 agents with Agent tool (parallel). Route to declared-model specialists where they exist, keep general-purpose only where no specialist covers the audit category:

Agent(subagent_type: "phx:phoenix-patterns-analyst", prompt: "Architecture audit: analyze module structure, context boundaries, coupling, cohesion. Write findings to .claude/audit/reports/arch-review.md", run_in_background: true)
Agent(subagent_type: "general-purpose",          prompt: "Performance audit: N+1 queries, missing indexes, bottlenecks, scalability. Write findings to .claude/audit/reports/perf-audit.md", run_in_background: true)
Agent(subagent_type: "phx:security-analyzer",        prompt: "Security audit: OWASP scan, auth patterns, secret leakage. Write findings to .claude/audit/reports/security-audit.md", run_in_background: true)
Agent(subagent_type: "phx:testing-reviewer",         prompt: "Test health audit: coverage, quality, flakes. Write findings to .claude/audit/reports/test-audit.md", run_in_background: true)
Agent(subagent_type: "general-purpose",          prompt: "Dependency audit: vulnerabilities, outdated, unused. Write findings to .claude/audit/reports/deps-audit.md", run_in_background: true)

Why specialist routing matters: general-purpose subagents inherit the parent session model (usually Opus). Plugin specialists declare their own model in frontmatter (sonnet/haiku for most). Routing 3 of 5 audit tracks to declared-model specialists materially cuts Opus subagent volume per audit run.

Agent prompts must be FOCUSED. Scope each prompt to the relevant directories and patterns. Do NOT give vague prompts like "analyze the codebase."

Output efficiency: Tell each agent: "Report ONLY issues found. Do NOT list clean checks, passing categories, or 'What's Good'. One summary line per clean area suffices."

Step 2: Collect Results

Wait for ALL auditors to complete. Mark each auditor's task as completed via TaskUpdate as it finishes. NEVER proceed while any auditor is still running.

Read reports from .claude/audit/reports/.

Rate-limit circuit breaker: if 2+ auditors return empty results or rate-limit/API errors, STOP spawning. Synthesize from the reports that exist, mark missing categories as "not audited (rate limit)", and tell the user to re-run /phx:audit after the limit resets. Never leave the user typing "continue" against dead agents.

Step 3: Compress Findings

After all 5 auditors complete, spawn context-supervisor:

Agent(subagent_type: "phx:context-supervisor", prompt: """
Compress audit findings.
Input: .claude/audit/reports/
Output: .claude/audit/summaries/
Priority: Health scores per category, critical findings
only, cross-category correlations, deduplicate findings
found by 2+ agents.
""")

Read .claude/audit/summaries/consolidated.md for synthesis.

Step 4: Calculate Health Score

Each category scores 0-100. See ${CLAUDE_SKILL_DIR}/references/scoring-methodology.md.

Step 5: Generate Report

Write to .claude/audit/summaries/project-health-{date}.md.

Output Format

Report includes: Executive summary with health score (A-F, numeric/100), per-category score table (Architecture, Performance, Security, Tests, Dependencies), critical issues, top recommendations, and action plan (Immediate/Short-term/Long-term).

Quick Mode (--quick)

Only run essential checks (~2-3 minutes):

Run mix compile --warnings-as-errors, then mix hex.audit && mix deps.audit, then mix xref graph --format stats, then mix test --trace 2>&1 | tail -20.

Skip: Full security scan, N+1 analysis, test quality metrics, architecture deep dive.

Focus Mode (--focus=area)

Deep dive single area with full specialist resources:

FocusSubagentExtra Checks
securitysecurity-analyzerFull OWASP, sobelow, manual patterns
performancegeneral-purposeProfile-level analysis, query explain (no plugin specialist yet)
architecturephoenix-patterns-analystFull xref, coupling matrix, cohesion
teststesting-reviewerCoverage by context, quality metrics
depsgeneral-purposeLicense audit, maintenance status (per-package hex-deps-triager only)

Incremental Mode (--since <commit>)

Analyze only changes since a specific commit. Useful for pre-merge checks:

Run git diff --name-only <commit>...HEAD to identify changed files, then run targeted audits on changed files only (skips full project scan).

Combines with other flags: /phx:audit --since HEAD~5 --focus=security

Relationship to Other Commands

CommandScopeFrequency
/phx:reviewChanged files (diff)Every PR
/phx:auditEntire projectQuarterly
/phx:boundariesContext structureOn-demand
/phx:verifyCompile/test passAnytime

References

  • ${CLAUDE_SKILL_DIR}/references/scoring-methodology.md - How scores are calculated
  • ${CLAUDE_SKILL_DIR}/references/architecture-checks.md - Detailed architecture criteria

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Audit AI skill do?

Project health audit and health check — architecture, performance, tests, dependencies, code quality. Use when assessing overall project health, before releases, or after refactors.

Why use Audit on TypingMind?

Because you install it once and use it with any model. Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oliver-kriska/claude-elixir-phoenix/tree/main/plugins/elixir-phoenix/skills/audit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Audit?

As many as you like. As long as a model supports skills, you can use Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Audit AI skill free?

Yes. It is published on GitHub by oliver-kriska under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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