Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/event4u-app/agent-confignpx agentmods add skills/event4u-app/agent-config/copilot-agents-optimizationWrote 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/event4u-app/agent-config/copilot-agents-optimization)<a href="https://agentmods.dev/skills/event4u-app/agent-config/copilot-agents-optimization"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/copilot-agents-optimization/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/event4u-app/agent-config/copilot-agents-optimization"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/copilot-agents-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.02723 |
| Opus 5 | $0.00021 | $0.01362 |
| Sonnet 5 | $0.00008 | $0.00545 |
| Haiku 4.5 | $0.00004 | $0.00272 |
Grade A, and why
copilot-agents-optimization 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 7d 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Copilot & AGENTS Optimizer Skill
When to use
Use this skill when:
- Running
/docs-optimizeto refactor AGENTS.md and copilot-instructions.md - Adding new content to either file and needing to check if it belongs there
- After changes to
.augment/(new skills, rules, guidelines) that may make content in these files redundant - When either file exceeds the line budget
Do NOT use when:
- Writing application code
- Creating new skills or commands
Procedure: Optimize copilot/agents files
0. Analyze current state
Before changing anything, read and understand:
- Read
AGENTS.md— current content, line count, what's duplicated vs. unique. - Read
.github/copilot-instructions.md— same analysis. - Scan
.augment/— which skills, rules, and guidelines already cover topics from the files above. 3b. Scan the playbooks too, not only.augment/. Apackages/<n>/AGENTS.mdmay duplicate a playbook rather than a shipped rule, and dedup against.augment/alone misses it entirely — the content is not in.augment/at all. RunfindRestatedSteps(src/scripts/derive_playbooks.ts) over each workspace file: a line carrying a playbook'sinvokesid outside a link is a restatement to replace with a pointer. The same id inside a link label is the shape the contract wants — seeagents-md-thin-root§ Workspace files. - Identify duplication — what content exists in both AGENTS.md and
.augment/?
Only after this analysis, proceed with optimization.
AGENTS.md — Project Entry Point for AI Agents
| Property | Value |
|---|---|
| Audience | Augment Agent, other AI agents |
Can read .augment/? |
✅ Yes — can follow references |
| Size budget | ≤ 3,000 chars (package root; consumer template ≤ 2,500) — enforced by lint_agents_md.ts; see size-and-scope.md |
| Purpose | Project-specific setup, Docker, testing, quality tools |
What belongs here:
- Tech stack and framework versions
- Development setup (Docker, Make targets, env files)
- Database and multi-tenancy setup
- Testing framework, suites, and conventions
- Quality tool commands (PHPStan, Rector, ECS)
- Project structure overview (brief, link to module docs)
- Agent infrastructure overview (layer table, key references)
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.
- 7d ago First seen · 259 lines · 42 tokens per session scan A 7d696bee949d
copilot-agents-optimization is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 2,723 once invoked, about $0.0002 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 2026-09-03.
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