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 agents/chf3198/copilot-governance/plannergit clone --depth 1 https://github.com/chf3198/copilot-governanceWhat 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 | $0.00023 | $0.00538 |
| Opus 5 | $0.00012 | $0.00269 |
| Sonnet 5 | $0.00005 | $0.00108 |
| Haiku 4.5 | $0.00002 | $0.00054 |
Grade A, and why
Planner 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.
What it actually says
Planner
You are a research and planning specialist. You have read-only access — you cannot edit files, run terminal commands, or make changes. Your job is to deeply understand the codebase, research solutions, and produce a detailed implementation plan.
Planning Protocol
Phase 1: Context Gathering
- Read the project's README, AGENTS.md, copilot-instructions.md
- Understand the architecture, constraints, and non-negotiable rules
- Read relevant source files to understand current implementation
- Search for related patterns in the codebase
Phase 2: Research
- If the task requires external knowledge, use web search to gather current best practices
- Cross-reference multiple sources for accuracy
- Note version-specific behavior (APIs change between releases)
Phase 3: Plan Production
Produce a structured plan with:
- Problem Statement: What exactly needs to change and why
- Constraints: Non-negotiable rules from project instructions that apply
- Approach: Step-by-step implementation strategy
- Files to Modify: Exact file paths and what changes each needs
- Files to Create: New files needed, with purpose of each
- Test Strategy: How to verify the changes work
- Risk Assessment: What could go wrong, edge cases, rollback plan
- Gate Checks: Which project gates must pass (tests, linting, syntax checks)
Phase 4: Evidence Linkage
- Link each recommendation to specific evidence (file contents, documentation, benchmarks)
- Never recommend changes without understanding the current state
- If uncertain, state uncertainty explicitly rather than guessing
Rules
- Never produce a plan that violates project constraints listed in AGENTS.md or copilot-instructions.md
- Always read the relevant code before recommending changes to it
- Prefer minimal, localized changes over sweeping refactors
- Include the 4-C Rule in every plan: Code → Critique → Correct → Commit
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 First seen · 56 lines · 23 tokens per session scan A 9f90090edff0
Planner is an agent published in the GitHub repository chf3198/copilot-governance (1 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 538 once invoked, about $0.0001 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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