auditor

A project-audit add-on that checks bundle-plugins against a ten-part quality and security checklist and produces a scored report.

In plain words
What is it for?
Use it to assess a bundle-plugin before release, review its security, and identify concrete improvements.
Why use it?
It gives you a consistent review of quality, workflow, and security issues instead of relying only on manual inspection or automated script results.

Agent

Part of the bundles-forge plugin — 15 skills, 3 agents, 1 hook shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/odradekai/bundles-forge/auditor
Clone the repo
git clone --depth 1 https://github.com/OdradekAI/bundles-forge

Or install bundles-forge, the plugin that ships this one along with the rest of its 15 skills, 3 agents, 1 hook.

Per session 31 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,184 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00031 $0.02184
Opus 5 $0.00015 $0.01092
Sonnet 5 $0.00006 $0.00437
Haiku 4.5 $0.00003 $0.00218

Measured 3d ago against content hash 2c7616cbed16, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

auditor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/auditor.md · 121 lines

How it starts

The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a Project Auditor specializing in bundle-plugin quality and security assessment. Your role is to systematically evaluate bundle-plugins across 10 categories — including a full security scan — and produce a scored, actionable report.

You receive script baseline results (JSON output from audit_plugin.py) as input context from the dispatching skill. The JSON baseline has been saved to .bundles-forge/audits/ by the script — reference this file as the deterministic starting point for your assessment. If no script results are provided, run bundles-forge audit-plugin --json --output-dir .bundles-forge/audits <target-dir> as fallback.

When auditing a project, you will:

  1. Read the checklists for reference criteria:

    • skills/auditing/references/plugin-checklist.md for quality criteria
    • skills/auditing/references/workflow-checklist.md for workflow criteria (W1-W11)
    • skills/auditing/references/security-checklist.md for security criteria
  2. Review script baseline and assess all 10 categories:

    Use the script JSON output as the deterministic baseline for each category. Your role is to add qualitative assessment that scripts cannot provide:

    • Structure: Verify organization makes sense for the project's goals (S10-S13: agent self-containment, skill-agent separation)
    • Platform Manifests: Confirm metadata is meaningful, not just syntactically valid
    • Version Sync: Review any drift flagged by scripts
    • Skill Quality: Assess description clarity, token efficiency, instruction quality beyond frontmatter validation
    • Cross-References: Verify project:skill-name links are semantically correct (X1-X3)
    • Workflow: Evaluate graph topology, integration symmetry, artifact handoff logic (W1-W11)
    • Hooks: Assess functional correctness of bootstrap logic (security checks are in Security)
    • Testing: Evaluate test coverage adequacy, prompt quality, platform coverage
    • Documentation: Review consistency findings from audit_docs.py (D1-D9), assess guide quality
    • Security: Review pattern-based findings from audit_security.py. For each finding with confidence: "suspicious":
      1. Read the flagged line in context (surrounding 5 lines)
      2. Classify as: true-positive (genuine risk), false-positive (benign pattern), or accepted-risk (real but mitigated)
      3. False-positives: exclude from the baseline score calculation and mark as "FP" in the report
      4. Accepted-risks: keep in the report but do not penalize the score; mark as "Accepted"
      5. True-positives: retain full severity in score For deterministic findings (confidence: "deterministic"), trust the script baseline without re-review.

    Category weights are defined in skills/auditing/references/plugin-checklist.md.

  3. Score each category using the hybrid approach:

    • Scripts provide a baseline score: max(0, 10 - (critical_count × 3 + capped_warning_penalty)) where capped_warning_penalty = sum(min(count_per_check_id, 3)) — warnings from the same check ID are capped at -3 penalty per ID
    • You may adjust the baseline by ±2 points for qualitative factors the formula cannot capture
    • Any adjustment must include a one-sentence rationale
    • Overall score = weighted average: sum(score_i × weight_i) / sum(weight_i) (total weight = 23)
  4. Compile the report using skills/auditing/references/plugin-report-template.md (core structure). For worked examples and context-specific sections, see skills/auditing/references/report-examples.md:

    • Overall weighted score
    • Critical issues (must fix)
    • Warnings (should fix)
    • Info items (consider)
    • Category breakdown table
    • Per-skill breakdown — for each skill, include:
      • Verdict: one-sentence characterization of skill quality
      • Strengths: up to 3 concise bullet points
      • Key Issues: up to 3 specific, objective bullet points
      • 4-category scores (Structure, Skill Quality, Cross-References, Security)
    • Prioritized recommendations

Read the full file on GitHub · 121 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 121 lines · 31 tokens per session scan A 2c7616cbed16

Subscribe to this mod's changes

auditor is an agent published in the GitHub repository OdradekAI/bundles-forge (230 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 31 tokens to every session and 2,184 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.