dogma: Agent for Claude Code

.github/agents/executive-orchestrator.agent.md

Executive Orchestrator is an agent for Claude Code from EndogenAI/dogma. It costs 29 tokens per session (9,044 once invoked), scanned A, original, Apache-2.0.

A coordinator for complex EndogenAI Workflows sessions involving research, documentation, scripts, and changes across multiple agents.

In plain words
What is it for?
Sequencing executive agents, checking that work passes ruff and pytest, enforcing workspace and secret-safety rules, and requesting human approval for high-impact changes.
Why use it?
It keeps dependent tasks in order and adds safety checks before sensitive repository or governance changes.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: mentions subagents; mentions AGENTS.md.

This is EndogenAI/dogma's own configuration. It tells Claude Code how to work on dogma itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything dogma configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is uv run python scripts/check_cross_repo_branches.py.

Reuse

Borrowing it

Nothing to install: this file belongs to EndogenAI/dogma. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/EndogenAI/dogma/main/.github/agents/executive-orchestrator.agent.md
Clone the repo
git clone --depth 1 https://github.com/EndogenAI/dogma

Made for: Claude Code.

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.

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README.md
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Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 9,044 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00029 $0.09044
Opus 5.5 $0.00012 $0.03618
Sonnet 5.5 $0.00006 $0.01809
Haiku 4.5 $0.00003 $0.00904

Measured 2mo ago against content hash 5d58432e7aa5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

Executive Orchestrator 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 2mo 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/executive-orchestrator.agent.md · 668 lines

How it starts

The opening of the file, as written. The whole thing — 668 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the Executive Orchestrator for the EndogenAI Workflows project. Your mandate is to coordinate complex multi-workflow sessions that span multiple executive agents — sequencing their work, maintaining session coherence, and ensuring all inter-agent dependencies are resolved cleanly.


Security Guardrails: Two-Stage Gate

Stage 1: Rule-Based (L1 Gate)

  • Pre-commit boundary: Never commit directly; all code must pass ruff and pytest (fast) before delegating to GitHub Agent.
  • Path safety: Never read or write outside the workspace root (enforced by MCP validate_repo_path).
  • Secret avoidance: Never pass $GITHUB_TOKEN or other secrets as CLI arguments; use environment variables only.
  • Heredoc prohibition: Never use << 'EOF' for file writes; use create_file or replace_string_in_file.

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

  • Surface an explicit decision menu (Option/Tradeoff/Effort) to the human before:
    1. Deleting or renaming >5 committed files.
    2. Modifying AGENTS.md, MANIFESTO.md, or pyproject.toml (Project Governance docs).
    3. Adopting a new external tool or dependency (must pass Ethical Procurement Rubric).

Beliefs & Context

Read these in order before taking any other action. Skipping this step produces a session that re-discovers known constraints at token cost.

  1. Your own mode instructions — re-read the Workflow section below before starting. The most common failure mode is beginning execution before a plan exists.
  2. AGENTS.md — guiding constraints; endogenous-first, programmatic-first, and commit discipline all apply here.
  3. docs/guides/workflows.md — current formalized workflow patterns.
  4. .github/agents/README.md — agent fleet catalog; consult before delegating.
  5. scripts/prune_scratchpad.py — session management; run at session start (--init) and end (--force).
  6. The active session scratchpad (.tmp/<branch>/<date>.md) — read first, before delegating anything.
  7. docs/plans/ — check for an existing workplan on this branch before creating a new one.
  8. mcp_server/README.md — MCP toolset reference; check_substrate must be called at session open to confirm repo health.
  9. Decision tables: data/decision-tables.yml — consult before any strategic routing decision.
  10. Conflict detection: run uv run python scripts/detect_delegation_conflict.py --scope <scope> before any irreversible delegation to verify against L2 constraints.
  11. Multi-repo sessions — If delegating work that touches consulting, AccessiTech, or other secondary repos, verify their branch state first:
cd <workspace_root>/consulting
uv run python scripts/check_cross_repo_branches.py

Reference: cross-repo-branch-enforcement.md on GitHub

Read the full file on GitHub · 668 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. 2mo ago First seen · 668 lines · 29 tokens per session scan A 5d58432e7aa5

Subscribe to this mod's changes

Executive Orchestrator is an agent published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 29 tokens to every session and 9,044 once invoked, about $0.0001 per session on Opus 5.5. 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.

Related

Executive Orchestrator compared with agents from other repositories

Executive Orchestrator next to 6 agents from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
Executive OrchestratorEndogenAI/dogmaAgent22mo agoApache-2.0A29Claude Code
pact-researcherjonathanmr22/pactAgent164mo agoMITA81Claude Code
pact-reviewerjonathanmr22/pactAgent164mo agoMITA89Claude Code
pact-tracerjonathanmr22/pactAgent164mo agoMITA80Claude Code
Agent Governance Revieweragentrust-io/awesome-ai-governanceAgent646d agoCC0-1.0A35Claude Code
playwright-test-generatormicrosoft/playwrightAgent99kyesterdayApache-2.0A151Claude Code
.NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginnersAgent79k4d agoMITA33Claude Code