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/richfrem/agent-plugins-skills/co-pilot-loopnpx skills add richfrem/agent-plugins-skills --skill co-pilot-loopgit clone --depth 1 https://github.com/richfrem/agent-plugins-skillsWrote 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/richfrem/agent-plugins-skills/co-pilot-loop)<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/co-pilot-loop"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/co-pilot-loop.svg" alt="Measured on agentmods" 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 | $0.00080 | $0.01078 |
| Opus 5 | $0.00040 | $0.00539 |
| Sonnet 5 | $0.00016 | $0.00216 |
| Haiku 4.5 | $0.00008 | $0.00108 |
Grade A, and why
co-pilot-loop 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cooperative Co-Pilot Loop (Supervisor Protocol)
The Co-Pilot Loop splits software engineering tasks between a Supervisor (Outer Loop — you) and an Executor (Inner Loop — a lightweight companion sub-agent). The Supervisor acts as the product manager and QA director, while the Executor performs spec writing, planning, and coding inside an isolated worktree.
You do not know which CLI or chat interface the user is using — and that's fine. The skill is model-agnostic. It reads the cheapest model for the active CLI at runtime.
1. Setup & Orientation
Step 1A — Determine the active CLI backend
Ask the user once (or detect from context):
"Which CLI backend is available for the sub-agent? (
agy,claude,copilot,codex,llama)"
Step 1B — Look up the cheapest model for that backend
Consult plugins/agent-orchestration/references/cheapest_models.json (or references/cheapest_models.md) to select the cheapest model for the detected CLI backend.
Step 1C — Spawn the sub-agent
Use run_agent.py with the resolved CLI and model:
# For agy (recommended — cheapest Gemini)
python ./scripts/run_agent.py <PERSONA_FILE> <PACKET_FILE> <OUTPUT_FILE> "<INSTRUCTION>" \
--cli agy --model "Gemini 3.5 Flash (Low)" < /dev/null
# For claude CLI
python ./scripts/run_agent.py <PERSONA_FILE> <PACKET_FILE> <OUTPUT_FILE> "<INSTRUCTION>" \
--cli claude --model claude-haiku-4.5 < /dev/null
# For copilot CLI
python ./scripts/run_agent.py <PERSONA_FILE> <PACKET_FILE> <OUTPUT_FILE> "<INSTRUCTION>" \
--cli copilot --model gpt-5.4-nano < /dev/null
CRITICAL: Always append
< /dev/nullto preventSIGTTINprocess suspension in background execution.
2. Strategy Packet & Handoff
Create an isolated Git worktree or branch for the Executor. Generate a Strategy Packet (via scripts/agent_orchestrator.py packet) containing:
- Objective — what feature/bug is being implemented.
- Constraints — TDD rules, symlink policy, coding conventions, no deletions.
- No-Git Rule — the Executor is strictly forbidden from running any
gitcommands. - Spec output path — e.g.
docs/superpowers/specs/YYYY-MM-DD-<feature>-spec.md. - Plan output path — e.g.
implementation_plan.md.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · -15 lines a0dd9da9ffeb
- 4d ago First seen · 112 lines · 80 tokens per session scan A 5d8415e356f9
co-pilot-loop is a skill published in the GitHub repository richfrem/agent-plugins-skills (6 stars, last pushed yesterday), licensed MIT. It adds 80 tokens to every session and 1,078 once invoked, about $0.0004 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.
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