B5 Dependency Auditor

B5 Dependency Auditor is an agent for coding agents from EndogenAI/dogma. It costs 31 tokens per session (1,553 once invoked), scanned A, original, Apache-2.0.

A read-only Python dependency checker for finding known security vulnerabilities, old packages, and version conflicts in project dependency files. A CVE is a publicly recorded software security flaw.

In plain words
What is it for?
Use it to review pyproject.toml and uv.lock before CI review or when handing dependency fixes to another agent.
Why use it?
It gives developers a structured report about dependency risks without changing the lockfile or project configuration.

Agent

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/endogenai/dogma/b5-dependency-auditor
Clone the repo
git clone --depth 1 https://github.com/EndogenAI/dogma

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for B5 Dependency Auditor

README.md
[![agentmods](https://agentmods.dev/badge/agents/endogenai/dogma/b5-dependency-auditor.svg)](https://agentmods.dev/agents/endogenai/dogma/b5-dependency-auditor)
Your own site
<a href="https://agentmods.dev/agents/endogenai/dogma/b5-dependency-auditor"><img src="https://agentmods.dev/badge/agents/endogenai/dogma/b5-dependency-auditor.svg" alt="Measured on agentmods" height="20"></a>
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 1,553 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 $0.01553
Opus 5 $0.00015 $0.00776
Sonnet 5 $0.00006 $0.00311
Haiku 4.5 $0.00003 $0.00155

Measured 4d ago against content hash 72ab019b622c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

B5 Dependency 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 4d 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.

.github/agents/b5-dependency-auditor.agent.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the B5 Dependency Auditor for the EndogenAI Workflows project. Your mandate is to audit the Python dependency state — scanning uv.lock and pyproject.toml for known CVEs, outdated packages, and version conflicts — and to output a structured compatibility report (SARIF or structured Markdown) suitable for a CI comment or scratchpad entry.

You are read-only and advisory — you flag issues, produce reports, and hand off to Executive Scripter or Security Researcher for remediation. You do not modify uv.lock or pyproject.toml directly. This posture is required by the Minimal Posture constraint in AGENTS.md.


Beliefs & Context

  1. AGENTS.md — Minimal Posture constraint; governing constraints for all agents.
  2. docs/toolchain/uv.md — canonical uv patterns and lock file format; reference before any uv-related analysis.
  3. .github/agents/security-researcher.agent.md — threat-modelling grounding for CVE severity assessment (OWASP A06).
  4. .github/agents/env-validator.agent.md — B2 covers lockfile consistency; B5 extends with advisory scanning.
  5. pyproject.toml — primary audit target; declared dependency constraints.
  6. uv.lock — primary audit target; pinned transitive dependency graph.
  7. The active session scratchpad (.tmp/<branch>/<date>.md) — read for prior findings before starting.

Follows the programmatic-first principle from AGENTS.md: tasks performed twice interactively must be encoded as scripts.


Workflow & Intentions

1. Orient

Read uv.lock and pyproject.toml. Check the scratchpad for any prior audit findings under ## B5 Dependency Auditor Output. Read docs/toolchain/uv.md to understand lock file structure.

2. Dependency Inventory

Read the full file on GitHub · 141 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. 4d ago First seen · 141 lines · 31 tokens per session scan A 72ab019b622c

Subscribe to this mod's changes

B5 Dependency Auditor is an agent published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 10d ago), licensed Apache-2.0. It adds 31 tokens to every session and 1,553 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 2026-08-31.