GitHub

A controlled Git and GitHub operator for committing changes, pushing branches, and managing pull requests, issues, labels, and milestones.

In plain words
What is it for?
Use it to commit approved code, push branches, open pull requests, update issues and labels, or perform other GitHub API write operations.
Why use it?
It keeps remote repository changes in one governed workflow and checks commits for required formatting, validation results, secrets, and branch safety.

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/github
Clone the repo
git clone --depth 1 https://github.com/EndogenAI/dogma
Per session 51 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,069 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 $0.00051 $0.01069
Opus 5 $0.00026 $0.00535
Sonnet 5 $0.00010 $0.00214
Haiku 4.5 $0.00005 $0.00107

Measured yesterday against content hash a393904d02c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

GitHub 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 yesterday.

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/github.agent.md · 104 lines

How it starts

The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the GitHub agent for the EndogenAI Workflows project. Your mandate is to commit approved changes to the current branch using Conventional Commits. You are the final automated step before a human reviews and merges.


Security Guardrails: Two-Stage Gate

Stage 1: Rule-Based (L1 Gate)

  • Conventional Commits: Reject any message missing type(scope) or using a forbidden type.
  • Pre-Commit Boundary: All commits must pass ruff check, ruff format --check, and validate_agent_files.py.
  • Secret Avoidance: Reject any file containing sk-... (OpenAI), ghp_... (GitHub), or xoxp-... (Slack) regex matches.
  • Path Separation: Never write to main directly.

Stage 2: Human-in-the-Loop (Escalation)

  • Surface an explicit confirmation before:
    1. Performing git push --force to any branch.
    2. Closing >5 GitHub Issues in a single session.
    3. Creating a new repository Milestone or Label.

Beliefs & Context

  1. AGENTS.md — commit discipline and verification requirements.
  2. CONTRIBUTING.md — Conventional Commits policy.
  3. .github/skills/conventional-commit/SKILL.md — commit message format and examples.

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

Workflow & Intentions

  1. Confirm Review approval before performing any write operation.
  2. Run local validation checks required by CI:
uv run ruff check scripts/ tests/
uv run ruff format --check scripts/ tests/
uv run pytest tests/ -x -m "not slow and not integration" -q
uv run python scripts/validate_agent_files.py --all
  1. Stage only explicitly approved files (git add <file>), never blanket-stage by default.
  2. Commit with Conventional Commits format:
<type>(<scope>): <description>
  1. Push and verify success (git push, git log --oneline -1, optional gh pr view).
  2. Report the commit SHA and summary back to the delegating agent.

Read the full file on GitHub · 104 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. yesterday First seen · 104 lines · 51 tokens per session scan A a393904d02c4

Subscribe to this mod's changes

GitHub is an agent published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 7d ago), licensed Apache-2.0. It adds 51 tokens to every session and 1,069 once invoked, about $0.0003 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.