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 skills/endogenai/dogma/phase-gate-sequencenpx skills add EndogenAI/dogma --skill phase-gate-sequencegit 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 | $0.00131 | $0.01973 |
| Opus 5 | $0.00066 | $0.00986 |
| Sonnet 5 | $0.00026 | $0.00395 |
| Haiku 4.5 | $0.00013 | $0.00197 |
Grade A, and why
phase-gate-sequence 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.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phase Gate Sequence
This skill enacts the Algorithms-Before-Tokens axiom from MANIFESTO.md: the 6-step gate is a deterministic algorithm encoded once and shared across all executive agents, eliminating parallel re-derivation and enforcing consistent phase discipline fleet-wide.
Beliefs & Context
- Governing axiom: Axiom 2 — Algorithms Before Tokens — deterministic procedure over repeated instruction
- GitHub issue: #79 — Skills as Decision Codifiers
- Formal FSM spec:
data/phase-gate-fsm.yml— machine-readable state specification for this gate loop (states: INIT, PHASE_RUNNING, GATE_CHECK, COMPACT_CHECK, COMMIT, CLOSED) - Agents that use this skill: Executive Orchestrator, Executive Researcher, Executive Fleet, Executive Docs
- Foundation docs:
AGENTS.md— compaction-aware writing, commit discipline, Programmatic-Firstexecutive-orchestrator.agent.md— canonical per-phase sequence (lines 169–196) and context window alert protocol (lines 198–242)
Workflow
Run this sequence after every ## Phase N Output write, before delegating the next domain phase.
Step 0: Loop Audit (Pre-Phase)
Before any complex phase execution, run the orchestration loop detector:
uv run python scripts/detect_orchestration_loop.py \
--task "<current-phase-name>" \
--scratchpad ".tmp/$(git branch --show-current | tr '/' '-')/$(date +%Y-%m-%d).md"
- If
loop_detected: false→ proceed to Step 1 - If
loop_detected: true→ do not execute. Write to scratchpad:## Loop Detected — [task] — iteration [N]; awaiting user direction. Surface to user: "I detected that I attempted this task [N] iterations ago. What should I do differently?"
Encoding point: Governed by AGENTS.md § Guardrails. Script: scripts/detect_orchestration_loop.py. Research basis: docs/research/orchestrator-autopilot-failure.md § Recommendation 4.
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.
- 3d ago First seen · 164 lines · 131 tokens per session scan A 417c357aee91
phase-gate-sequence is a skill published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 9d ago), licensed Apache-2.0. It adds 131 tokens to every session and 1,973 once invoked, about $0.0007 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 skills, from other repositories
dispatching-parallel-agents
Evaluate when to dispatch parallel agents; use a 4-step pattern to split, coordinate, and integrate results.
verification-before-completion
Enforce "no evidence = no completion"; run Gate Function verification before declaring done.
test-driven-development
Drive changes with Red→Green→Refactor; ensure behavior is verifiable and regression-safe.
doc-lookup
Check official docs when framework APIs or configuration may be version-sensitive.
air-blackbox-sales-agent
AIR Blackbox's autonomous sales prospecting agent. Finds Python AI projects on GitHub that need EU AI Act compliance, identifies the right person to contact (CEO, CTO, lead maintainer), runs a free compliance scan, and drafts personalized outreach emails that convert to engagement. The sales flow: free scan as the…
interpret-results
Interprets AIR Blackbox scan results and maps findings to specific EU AI Act articles, recitals, and remediation steps. Use when the user has scan output and wants to understand what to fix, why it matters, or how to prioritize.