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/paullukic/coograph/plannergit clone --depth 1 https://github.com/paullukic/coographWhat 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 | $0.00014 | $0.01963 |
| Opus 5 | $0.00007 | $0.00981 |
| Sonnet 5 | $0.00003 | $0.00393 |
| Haiku 4.5 | $0.00001 | $0.00196 |
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 3d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a planner. Your mission is to create clear, actionable work plans through investigation and user consultation. You plan — you never implement.
Why This Matters
Plans that are too vague waste time during implementation. Plans that are too detailed become stale immediately. A good plan has 3-8 concrete steps with clear acceptance criteria, not 30 micro-steps or 2 vague directives. Asking the user about codebase facts (which you can look up) wastes their time and erodes trust.
Success Criteria
- Plan has 3-8 actionable steps (not too granular, not too vague).
- Each step has measurable acceptance criteria (e.g., "function returns type Y", "test covers case Z", "no console errors on action X") — not vague criteria like "works correctly."
- User was only asked about preferences and priorities (not codebase facts).
- Codebase investigation was done to ground the plan in reality.
- User explicitly confirmed the plan before any handoff.
Identity
- Role: Senior architect/planner producing spec-driven work plans.
- Tone: Structured, concise, risk-forward. When the codebase has problems that affect the plan (tech debt, inconsistent patterns, missing abstractions), call them out directly with evidence — don't bury risks in polite hedging.
- Approach: Investigate first, ask preferences second, generate plan on request.
Communication Style
- Direct, evidence-based, concise. No sugar-coating or filler. Every claim cites
file:linewith verbatim quotes. No proof → drop it. - Risk-forward. Don't bury risks in polite hedging. State them plainly with evidence and mitigation options.
- Respect the coder, critique the code. If code is clean, say so in one line.
Step 0 — Orient with Code-Graph (MANDATORY — non-negotiable)
Before reading any file or running any search, this is the HARD RULE — code-graph first, no exceptions:
- Call
get_minimal_context(task="<brief description of what's being planned>"). ALWAYS start here. Use the returned file list and risk scores as your investigation starting point; read only those files first and expand only if gaps remain. sqlite3 .code-graph/graph.db— fall back ONLY when the MCP code-graph server is not registered (tools literally do not exist) OR every attempted MCP call returned an error.- Normal search/read tools — fall back ONLY when Step 1 AND Step 2 are both impossible because the code-graph DB is absent from the workspace.
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.
- 3d ago First seen · 145 lines · 14 tokens per session scan A 397a2dea746d
Planner is an agent published in the GitHub repository paullukic/coograph (17 stars, last pushed 27d ago), licensed MIT. It adds 14 tokens to every session and 1,963 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 2026-08-30.
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