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Audit Full

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yonatangross
audit-full

Single-pass codebase analysis leveraging a 1M-token context window for comprehensive security scanning, architecture review, and dependency auditing. Loads entire codebases for cross-file pattern detection and generates structured audit reports with severity-ranked findings. Use when you need whole-project analysis before releases or security reviews.

Overview

Publisheryonatangross
Repositoryorchestkit
Skill nameaudit-full
Stars
298
Forks
35
Bundled files
17
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.

  • 17 bundled files

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

  • Open source

    Published by yonatangross on GitHub. Read the source before you install it.

Installation

Install the Audit Full 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.
    https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/audit-full
  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/yonatangross/orchestkit.git /tmp/orchestkit
mkdir -p .claude/skills
cp -r /tmp/orchestkit/plugins/ork/skills/audit-full .claude/skills/audit-full
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Audit Full 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 Full 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 Full 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.

Full-Codebase Audit

Single-pass whole-project analysis leveraging Opus 5.5's extended context window. Loads entire codebases (~50K LOC) into context for cross-file vulnerability detection, architecture review, and dependency analysis.

Quick Start

bash
audit-full                          # Full audit (all modes)
audit-full security                 # Security-focused audit
audit-full architecture             # Architecture review
audit-full dependencies             # Dependency audit

Opus 5.5: Uses complexity: max for extended thinking across entire codebases. 1M context (default and maximum on Opus 5.5) enables cross-file reasoning that chunked approaches miss. Set effort explicitly: Opus 5.5's own default is medium (one level below Opus 5's high), and CC 2.1.280+ starts a newly released model at its default rather than an older saved /effort level. Run audits at high; use xhigh for one additional cross-file pattern sweep on the hardest codebases.

1M Context Required: If CLAUDE_CODE_DISABLE_1M_CONTEXT is set, audit-full cannot perform full-codebase analysis. Check: echo $CLAUDE_CODE_DISABLE_1M_CONTEXT — if non-empty, either unset it (unset CLAUDE_CODE_DISABLE_1M_CONTEXT) or use verify for chunked analysis instead.

Effort (CC 2.1.111+): xhigh adds a second pass that re-reads cross-module boundaries specifically looking for patterns the first pass normalized over. Reserve xhigh for the hardest codebases (see the Opus 5.5 note above for the default). Silently falls back to high on other models; doctor warns on mismatch.

Switching to Opus (CC 2.1.144+): /model now affects the current session only — pick Opus for this audit without it persisting. Press d in the picker only if you want it as the default for new sessions too.


STEP 0: Verify User Intent with AskUserQuestion

BEFORE creating tasks, clarify audit scope using the interactive dialog.

Load: Read("references/audit-scope-dialog.md") for the full AskUserQuestion dialog with mode options (Full/Security/Architecture/Dependencies) and scope options (Entire codebase/Specific directory/Changed files).


CRITICAL: Task Management is MANDATORY

python
# 1. Create main task IMMEDIATELY
TaskCreate(
  subject="Full-codebase audit",
  description="Single-pass audit using extended context",
  activeForm="Running full-codebase audit"
)

# 2. Create subtasks for each phase
TaskCreate(subject="Estimate token budget and plan loading", activeForm="Estimating token budget")  # id=2
TaskCreate(subject="Load codebase into context", activeForm="Loading codebase")                    # id=3
TaskCreate(subject="Run audit analysis", activeForm="Analyzing codebase")                          # id=4
TaskCreate(subject="Generate audit report", activeForm="Generating report")                        # id=5

# 3. Set dependencies for sequential phases
TaskUpdate(taskId="3", addBlockedBy=["2"])  # Loading needs budget estimate
TaskUpdate(taskId="4", addBlockedBy=["3"])  # Analysis needs codebase loaded
TaskUpdate(taskId="5", addBlockedBy=["4"])  # Report needs analysis done

# 4. Update status as you progress
TaskUpdate(taskId="2", status="in_progress")  # When starting
TaskUpdate(taskId="2", status="completed")    # When done — repeat for each subtask

STEP 1: Estimate Token Budget

Before loading files, estimate whether the codebase fits in context.

Load: Read("references/token-budget-planning.md") for estimation rules (tokens/line by file type), budget allocation tables, auto-exclusion list, and fallback dialog when codebase exceeds budget.

Run estimation: bash ${CLAUDE_SKILL_DIR}/scripts/estimate-tokens.sh /path/to/project

Two tiers — pick by the estimate

audit-full has two execution tiers. The estimate decides which:

Estimate vs budgetTierPath
Fits (~≤125K LOC / ≤1M tokens)Single-context (default — the skill's edge: whole-codebase cross-file reasoning in one window)continue to STEP 2
Exceeds budgetMap-reduce (scale tier — shard → per-shard audit → cross-shard boundary synthesis → refute)invoke the workflow below; STEP 2–3.5 run inside it

Over-budget → run the map-reduce workflow (don't punt, don't silently truncate the load):

python
# Derive shards from STEP 1 (top-level modules/dirs by size: src, apps/api, apps/web, packages/*).
Workflow({
  "scriptPath": "${CLAUDE_SKILL_DIR}/workflows/audit-full-mapreduce.js",
  "args": { "shards": ["<repo-relative dirs>"], "mode": "<full|security|architecture|dependencies>", "effort": "<high|xhigh>" }
})

It preserves cross-file reasoning within each shard and recovers cross-shard edges (taint/auth/dep-direction that span modules) in a dedicated synthesis pass, then runs the same STEP 3.5 adversarial refutation. Its return (merged findings + refutation ledger) feeds STEP 4. The single-context tier remains the default because it's cheaper and loses no boundaries when the repo fits — only reach for map-reduce when it genuinely doesn't.

Fan-out rule: a single pass is never a fallback for an over-budget repo when the selected mode is full coverage. If the map-reduce Workflow call does not run, end the run as BLOCKED and say why; never audit a truncated load. A mode the user picks that narrows the load on purpose (directory scoping, priority loading) is a declared scope cut, not a truncated load.


STEP 2: Load Codebase into Context

Load: Read("references/report-structure.md") for loading strategy, inclusion patterns by language (TS/JS, Python, Config), and batch reading patterns.


STEP 3: Audit Analysis

With codebase loaded, perform the selected audit mode(s).

Security Audit

Load: Read("references/security-audit-guide.md") for the full checklist.

Key cross-file analysis patterns:

  1. Data flow tracing: Track user input from entry point → processing → storage
  2. Auth boundary verification: Ensure all protected routes check auth
  3. Secret detection: Scan for hardcoded credentials, API keys, tokens
  4. Injection surfaces: SQL, command, template injection across file boundaries
  5. OWASP Top 10 mapping: Classify findings by OWASP category

Architecture Review

Load: Read("references/architecture-review-guide.md") for the full guide.

Key analysis patterns:

  1. Dependency direction: Verify imports flow inward (clean architecture)
  2. Circular dependencies: Detect import cycles across modules
  3. Layer violations: Business logic in controllers, DB in routes, etc.
  4. Pattern consistency: Same problem solved differently across codebase
  5. Coupling analysis: Count cross-module imports, identify tight coupling

Dependency Audit

Load: Read("references/dependency-audit-guide.md") for the full guide.

Key analysis patterns:

  1. Known CVEs: Check versions against known vulnerabilities
  2. License compliance: Identify copyleft licenses in proprietary code
  3. Version currency: Flag significantly outdated dependencies
  4. Transitive risk: Identify deep dependency chains
  5. Unused dependencies: Detect installed but never imported packages

Progressive Output (CC 2.1.76)

Output findings incrementally as each audit mode completes — don't batch until the report:

  1. Security findings first — show critical/high vulnerabilities immediately, don't wait for architecture review
  2. Architecture findings — show dependency direction violations, circular deps as they surface
  3. Dependency findings — show CVE matches, license compliance issues

For multi-mode audits (Full), each mode's findings appear as they complete. This lets users act on critical security findings while architecture analysis is still running.


STEP 3.5: Adversarial Refutation (effort-gated)

Before the report, a separate blind refuter verifies CRITICAL/HIGH findings — the structural fix for self-preferential bias (a single-context pass grading its own findings anchors on its own reasoning). low/medium skip this step; high runs single advisory refuters; xhigh runs the engine's quorum (3 for CRITICAL, 2 for HIGH).

Load the protocol + audit-full bindings: Read("references/adversarial-refutation.md") (which loads the shared engine ${CLAUDE_PLUGIN_ROOT}/shared/rules/adversarial-refutation.md).

These refuters are audit-full's only sub-agent spawns (the producer is single-context), always isolated Agent(...) with no team_name, fed only a neutral claim (category + file:line). Deterministic ground truth is exempt — CVE/CVSS matches, failing build/test, type errors are never refuted; only a reachability claim on top of a CVE is. Cross-file findings the refuter can't reproduce from its narrow slice stay UPHELD (engine §5). Refutation never silently drops a CRITICAL — the ledger (refutation-ledger.json) records survived/killed/downgraded, and removing a CRITICAL/HIGH from the report needs user confirmation.


STEP 4: Generate Report

Load the report template: Read("assets/audit-report-template.md").

Report structure and severity classification: Read("references/report-structure.md") for finding table format, severity breakdown (CRITICAL/HIGH/MEDIUM/LOW with timelines), and architecture diagram conventions.

Severity matrix: Read("assets/severity-matrix.md") for classification criteria.

Completion Checklist

Before finalizing the report, verify with Read("checklists/audit-completion.md").

PushNotification on Completion (CC 2.1.110+)

A full-codebase 1M-context audit typically runs 15–60 minutes on medium projects and can exceed that on large ones. After the report file is written and the completion checklist passes, call PushNotification so the finding counts are visible even if the user walked away.

python
PushNotification(
  message=f"ork:audit-full complete — {SCOPE}: {critical}C/{high}H/{medium}M/{low}L · {report_path}",
  status="proactive"
)

Full rule: Read("${CLAUDE_PLUGIN_ROOT}/skills/chain-patterns/rules/push-notification-on-completion.md").


When NOT to Use

SituationUse Instead
Small targeted check (1-5 files)Direct Read + analysis
CI/CD automated scanningsecurity-scanning skill
Multi-agent graded verificationverify
Exploring unfamiliar codebaseexplore
Codebase > 125K LOC (exceeds 1M)stay here — STEP 1 routes to the map-reduce tier (workflows/audit-full-mapreduce.js); verify only if you want multi-agent graded verification instead of an audit

Notes for whole-codebase passes

Oversized reads (CC 2.1.144+): When loading large files, Read returns a [PARTIAL view] truncated first page instead of a hard error if the whole-file read exceeds the token limit. Detect that notice and re-read with explicit offset/limit to page through the rest — a partial read silently omits code an audit must not miss.

When context fills (CC 2.1.141+): Use the rewind menu's "Summarize up to here" to compress earlier turns while keeping recent findings, instead of restarting the audit. Reactive compaction (CC 2.1.142+) sizes the first summarize to the actual overflow, so a wasted second pass mid-turn is now rare.


Running unattended with /goal

Set a completion condition with /goal (CC 2.1.139+) and this skill will keep working across turns until the condition is met. Works in interactive, -p, and Remote Control. The overlay panel shows live elapsed / turns / tokens.

Example completion condition for this skill:

/goal until findings.critical == 0 OR no_new_critical_for_3_turns, or stop after 30 turns

Stops when: zero critical issues remain across 3 consecutive passes, or a structural anti-pattern budget is reached. Compatible with claude.ai Remote Control runs.


Related Skills

  • security-scanning — Automated scanner integration (npm audit, Semgrep, etc.)
  • ork:security-patterns — Security architecture patterns and OWASP vulnerability classification
  • ork:architecture-patterns — Architectural pattern reference
  • ork:quality-gates — Quality assessment criteria
  • ork:verify — Multi-agent verification (fallback for codebases exceeding 1M context)

References

Load on demand with Read("references/<file>"):

FileContent
references/security-audit-guide.mdCross-file vulnerability patterns
references/architecture-review-guide.mdPattern and coupling analysis
references/dependency-audit-guide.mdCVE, license, currency checks
references/adversarial-refutation.mdBlind-refuter bindings (STEP 3.5) — loads the shared engine
references/token-estimation.mdFile type ratios and budget planning
assets/audit-report-template.mdStructured output format
assets/severity-matrix.mdFinding classification criteria
checklists/audit-completion.mdPre-report verification
scripts/estimate-tokens.shAutomated LOC to token estimation
workflows/audit-full-mapreduce.jsScale tier — shard→audit→synthesize→refute for repos that exceed 1M context (run via the Workflow tool)

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 Full AI skill do?

Single-pass codebase analysis leveraging a 1M-token context window for comprehensive security scanning, architecture review, and dependency auditing. Loads entire codebases for cross-file pattern detection and generates structured audit reports with severity-ranked findings. Use when you need whole-project analysis before releases or security reviews.

Why use Audit Full on TypingMind?

Because you install it once and use it with any model. Audit Full 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 Full in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/audit-full. 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 Full?

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 Full?

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

Is the Audit Full AI skill free?

Yes. It is published on GitHub by yonatangross 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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