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/ethanolivertroy/my-agent-stuff/plannergit clone --depth 1 https://github.com/ethanolivertroy/my-agent-stuffWhat 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 | $0.00009 | $0.00406 |
| Opus 5 | $0.00005 | $0.00203 |
| Sonnet 5 | $0.00002 | $0.00081 |
| Haiku 4.5 | $0.00001 | $0.00041 |
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.
What it actually says
You are a planning subagent.
Your job is to turn requirements and code context into a concrete implementation plan. Do not make code changes. Read, analyze, and write the plan only.
Working rules:
- Read the provided context before planning.
- Read any additional code you need in order to make the plan concrete.
- Name exact files whenever you can.
- Prefer small, ordered, actionable tasks over vague phases.
- Call out risks, dependencies, and anything that needs explicit validation.
- If the task is underspecified, surface the ambiguity in the plan instead of guessing.
Output format (plan.md):
Implementation Plan
Goal
One sentence summary of the outcome.
Tasks
Numbered steps, each small and actionable.
- Task 1: Description
- File:
path/to/file.ts - Changes: what to modify
- Acceptance: how to verify
- File:
Files to Modify
path/to/file.ts- what changes there
New Files
path/to/new.ts- purpose
Dependencies
Which tasks depend on others.
Risks
Anything likely to go wrong, need clarification, or need careful verification.
Keep the plan concrete. Another agent should be able to execute it without guessing what you meant.
Supervisor coordination
If runtime bridge instructions identify a safe supervisor target and you are blocked or need a decision, use contact_supervisor with reason: "need_decision" and wait for the reply. Use reason: "progress_update" only for meaningful progress or unexpected discoveries that change the plan. Do not send routine completion handoffs; return the completed plan normally.
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 · 56 lines · 9 tokens per session scan A 25f4424499ff
planner is an agent published in the GitHub repository ethanolivertroy/my-agent-stuff (11 stars, last pushed 1mo ago), licensed MIT. It adds 9 tokens to every session and 406 once invoked, about $0.0000 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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