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 skills add nxtg-ai/forge-plugin --skill ceo-loopgit clone --depth 1 https://github.com/nxtg-ai/forge-pluginWrote 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.
[](https://agentmods.dev/skills/nxtg-ai/forge-plugin/ceo-loop)<a href="https://agentmods.dev/skills/nxtg-ai/forge-plugin/ceo-loop"><img src="https://agentmods.dev/badge/skills/nxtg-ai/forge-plugin/ceo-loop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nxtg-ai/forge-plugin/ceo-loop"><img src="https://agentmods.dev/badge/skills/nxtg-ai/forge-plugin/ceo-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00142 | $0.04766 |
| Opus 5.5 | $0.00057 | $0.01906 |
| Sonnet 5.5 | $0.00028 | $0.00953 |
| Haiku 4.5 | $0.00014 | $0.00477 |
Grade B, and why
ceo-loop scanned grade B with 1 finding 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 12d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
1. **State file**: `cat .claude/ceo-loop-state.json` How it starts
The opening of the file, as written. The whole thing — 385 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CEO Decision Loop — ORBIT Protocol
Overview
The CEO-LOOP ORBIT model turns the CEO agent from a single-pass decision oracle into a continuous governance engine. Each invocation of this skill is one ORBIT iteration. The Stop hook (ceo-loop-stop.sh) re-invokes this skill after each iteration until the queue is empty or limits are reached.
Five contributions ORBIT adds to CEO-LOOP:
- Actual loop mechanism — Stop hook + state file, not pseudocode
- Cross-context continuity — progress file bridges compaction
- Iteration tracking + retrograde — was the last decision correct?
- Adaptive depth — ESLint fix in 30s, architecture in 15 min with Agent Teams
- Trust calibration — accuracy tracked, demotion alert surfaces to human
State Files (all in $PROJECT_ROOT/.claude/)
These are written and read by the loop. Never modify manually during an active loop.
ceo-loop-state.json — Hook state
{
"active": true,
"iteration": 7,
"max_iterations": 20,
"time_limit_minutes": 30,
"started_at": "2026-03-08T10:00:00Z",
"trust_level": "standard",
"depth_escalation_counter": 0,
"correct_decisions": 47,
"incorrect_decisions": 2
}
active: Stop hook checks this. If false, hook is a no-op.iteration: Current iteration number. Hook increments this before re-feed.depth_escalation_counter: How many consecutive trivial-only iterations. At 3, go proactive.trust_level:"standard"|"elevated"|"demoted". Demoted = escalate more.
ceo-loop-decisions.jsonl — Decision journal (append-only)
{
"id": "iter-7-1",
"iteration": 7,
"ts": "2026-03-08T10:15:00Z",
"tier": "auto_approve",
"depth": "trivial",
"category": "quality",
"input": "Add ESLint auto-fix to CI pipeline",
"decision": "APPROVED",
"reason": "Code quality improvements are always welcome. Low risk, high alignment.",
"next_action": "Commit and move to next task.",
"verification": "PENDING",
"retrograde": { "previous_id": null, "outcome": "N/A" },
"confidence": 0.98
}
Tier values: auto_approve | quick_review | deep_think | escalate
Depth values: trivial | light | medium | heavy
Category values: feature | architecture | quality | scope | deploy
Decision values: APPROVED | NEEDS_REVIEW | REJECTED
Verification values: CONFIRMED | FAILED | PENDING
Retrograde outcome values: CORRECT | INCORRECT | PENDING | N/A
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.
- 12d ago Changed · +4 tokens per session a93de35616cd
- 1mo ago First seen · 385 lines · 138 tokens per session scan B d1652878e8b9
ceo-loop is a skill published in the GitHub repository nxtg-ai/forge-plugin (6 stars, last pushed 4d ago), licensed MIT. It adds 142 tokens to every session and 4,766 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
rag-retrieval
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, agentic RAG, multimodal RAG, query decomposition, reranking, or pgvector search.
error-analysis
Evals-first error analysis for LLM apps: clusters real Langfuse or JSONL traces into a human-confirmed failure taxonomy with counts, then recommends binary pass/fail evals for recurring named modes. Use to learn what to measure before writing evals. Not for CI failures.
dev
One-command dev loop boot. Spins up portless (named HTTPS subdomain), emulate (stateful API mocks), the project's dev server, and an agent-browser session, all keyed to the current git branch. Use when starting a feature branch, switching worktrees, or returning to a project after a break. Skips silently with install…
testing-llm
LLM and AI testing patterns — mock responses, evaluation with DeepEval/RAGAS, structured output validation, and agentic test patterns (generator, healer, planner). Use when testing AI features, validating LLM outputs, or building evaluation pipelines.
accessibility
Accessibility patterns for WCAG 2.2 compliance, keyboard focus management, React Aria component patterns, cognitive inclusion, native HTML-first philosophy, and user preference honoring. Use when implementing screen reader support, keyboard navigation, ARIA patterns, focus traps, accessible component libraries…
manage-schemas
Creates, views, edits, deletes, and validates note schemas for the MCP Task Orchestrator in .taskorchestrator/config.yaml — the templates that define which notes agents must fill at each workflow phase. Also recommends schema designs from a library of workflow patterns (autonomous coding loops, spec-driven teams…