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.
npx agentmods add agents/endogenai/dogma/githubgit clone --depth 1 https://github.com/EndogenAI/dogmaWhat 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.
| Model | Per session | Once 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 |
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.
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, andvalidate_agent_files.py. - Secret Avoidance: Reject any file containing
sk-...(OpenAI),ghp_...(GitHub), orxoxp-...(Slack) regex matches. - Path Separation: Never write to
maindirectly.
Stage 2: Human-in-the-Loop (Escalation)
- Surface an explicit confirmation before:
- Performing
git push --forceto any branch. - Closing >5 GitHub Issues in a single session.
- Creating a new repository Milestone or Label.
- Performing
Beliefs & Context
AGENTS.md— commit discipline and verification requirements.CONTRIBUTING.md— Conventional Commits policy..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
- Confirm Review approval before performing any write operation.
- 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
- Stage only explicitly approved files (
git add <file>), never blanket-stage by default. - Commit with Conventional Commits format:
<type>(<scope>): <description>
- Push and verify success (
git push,git log --oneline -1, optionalgh pr view). - Report the commit SHA and summary back to the delegating agent.
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.
- yesterday First seen · 104 lines · 51 tokens per session scan A a393904d02c4
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.
Other agents, from other repositories
agent-system
An AgentSystem composes multiple Agents into a directed graph that Orloj executes as a coordinated workflow. The graph defines how messages flow between agents during task execution.
acx-tester
AgentCortex /test phase executor. Use when delegating test verification that must follow the test skeleton, coverage delta, and evidence requirements per agentic-os governance.
acx-handoff
AgentCortex /handoff phase executor. Use when delegating handoff work that must produce a resumable state summary with full Work Log archival per agentic-os governance.
acx-implementer
AgentCortex /implement phase executor. Use when delegating implementation work that must follow agentic-os gates, evidence requirements, and skill injection.
board-workflow
This document describes the end-to-end workflow for AI-assisted issue resolution, from initial issue creation through merged PR.
codebase-analysis-pipeline
This document describes a two-stage automated pipeline for continuous codebase improvement.