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/everyone-needs-a-copilot/claude-copilot/protocolnpx skills add Everyone-Needs-A-Copilot/claude-copilot --skill protocolgit clone --depth 1 https://github.com/Everyone-Needs-A-Copilot/claude-copilotWrote 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/everyone-needs-a-copilot/claude-copilot/protocol)<a href="https://agentmods.dev/skills/everyone-needs-a-copilot/claude-copilot/protocol"><img src="https://agentmods.dev/badge/skills/everyone-needs-a-copilot/claude-copilot/protocol.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 5.1 | $0.00056 | $0.01352 |
| Opus 5 | $0.00028 | $0.00676 |
| Sonnet 5 | $0.00011 | $0.00270 |
| Haiku 4.5 | $0.00006 | $0.00135 |
Grade A, and why
protocol 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 today.
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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protocol
$protocol is the primary Codex Copilot entrypoint for new work.
Use it at the start of a task to decide how the work should proceed.
Purpose
It should behave like the Claude Copilot /protocol command in intent:
- classify the request
- choose the correct specialist workflow
- require the right kind of thinking before implementation
- route the work through the correct agents
In Codex, that means:
- invoke specialist skills in the main session by default
- use
spawn_agentonly when the user explicitly asks for subagents or parallel execution - use
tcas the durable record for substantial work
Repository Decision Instruments
Before routing substantial work, check whether the repository defines its own decision instruments:
- If a root
SOUL.mdexists, read it before substantial product-facing work. Use it to decide whether the product direction should be built, reshaped, or rejected before specialist routing continues. - If
docs/01-architecture/12-architecture-guiding-principles.mdexists, read it before durable technical, architecture, migration, data, security, performance, AI pipeline, or productized implementation work. Use it as the technical decision lens.
Keep these instruments separate:
SOUL.mdanswers whether the product direction fits the product's purpose, taste, anti-patterns, and quality bar.- architecture principles answer how accepted product direction should be built safely, scalably, efficiently, and securely.
Request Classification
| Request type | Signals | Workflow |
|---|---|---|
| defect | broken behavior, regression, failing tests, bug fix | bug |
| technical | architecture, refactor, backend, migration, optimization | technical_feature |
| experience | user-facing feature, workflow, screen, UI, UX | experience_feature |
| physical-digital | hardware, connected product, tangible service touchpoint, physical object plus software | physical_digital_feature |
| UI polish | visual refinement, component styling, layout polish | ui_polish |
| security-sensitive | auth, permissions, secrets, trust boundaries | security_sensitive |
| infrastructure | CI, deploy, environment, observability, worktrees, release automation | infrastructure |
| ambiguous | improve, update, change, enhance without clear direction | ask for clarification before routing |
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.
- today First seen · 138 lines · 56 tokens per session scan A 5df0b6ea857c
protocol is a skill published in the GitHub repository Everyone-Needs-A-Copilot/claude-copilot (13 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 1,352 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 2026-09-04.
Other skills, from other repositories
ship-first
Final task completion protocol: report → user-note → deploy → smoke test → close? → guide? → routing.db → propagate → STATUS → sessions. Invoked from fast-track and pipeline flows after task execution, not directly by the user. Use when: invoked after the execute step of a fast-track or pipeline task.
ui-ai-first
Final audit of a large task before closure — finds which operations are available only via code / curl / SQL and decides per each: automate with a skill or AI agent (A) or create a UI task (B). Protects against invisible usability debt. Walks through each implemented block: reads task.md + reports + guides + code →…
cadence-first
Meta-orchestrator: assigns skill-chain cadence per block (Tier 1/2/3) via Q1-Q6 rules. Reads target taskblocks + task.md, writes cadence-decisions-{R}.md artifact. Standalone (executor invokes) or Batch (generator hand-off). Use when: deciding cadence for a batch of blocks, generator hand-off from decomposition skills.
decision-first
Makes an architectural / project / scope decision using a 5-part model INSTEAD of asking the user. Structure: 🎯 Decision / Why / 🛡 Security / 📈 Scalability / Alternatives / Plain-language analogy. 1 question = 1 atomic artifact. Use when: the agent is about to ask an architectural / scope question…
library-first
Mandatory protocol before executing any fast-track task. Analyzes the task, builds a table: what we do / where it comes from / how many lines of code. Principle: maximum reuse of existing libraries and components, minimum new code. Waits for explicit user approval — does nothing until confirmed. Use when: fast-track…
fixture-new
Creates a parity fixture — the frozen scenario plus the contract its output must satisfy. Asks which skill and case, what shape the run must produce, and writes input.md and expect.yml. Ends by proving the new fixture actually fails on an empty directory. A fixture that passes when nothing ran is worse than no…