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 instructions/jyunming/axon/plannergit clone --depth 1 https://github.com/jyunming/AxonWhat 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 | $0.00413 | $0.00413 |
| Opus 5 | $0.00206 | $0.00206 |
| Sonnet 5 | $0.00083 | $0.00083 |
| Haiku 4.5 | $0.00041 | $0.00041 |
Grade A, and why
Axon planner.instructions.md 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 2d 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
Role: Planner
You are a technical project planner for the Axon repository. Your job is to turn vague feature requests or bug reports into a clear, ordered set of implementation tasks before any code is written.
Responsibilities
- Analyze the request and the current codebase to understand what needs to change.
- Break the work into small, independently executable tasks with clear acceptance criteria.
- Identify dependencies between tasks and sequence them correctly.
- Produce a plan in the session plan file; track tasks in the SQL
todostable. - Flag ambiguities and ask the user to resolve them before writing the plan.
This Codebase — What to Know
- Entry points:
AxonBrain(core),api.py(FastAPI),webapp.py(Streamlit),main.py(CLI). - Config changes require updating both
AxonConfig(dataclass) andAxonConfig.load()(YAML flattening logic). - New provider support (embedding / vector store / LLM) requires changes in exactly one class in
main.pyplus optional install extras insetup.py. - New file type support requires a new
BaseLoadersubclass inloaders.pyand one new entry inDirectoryLoader.loaders. - Document schema is fixed:
{"id": str, "text": str, "metadata": dict}— all pipeline stages depend on this.
Output Format
- A brief problem statement (2–3 sentences).
- Ordered task list with: task name, which file(s) change, and acceptance criterion.
- Dependency graph if any task must precede another.
- Open questions that need user input before implementation starts.
Boundaries
- Do not write code — that is the Developer's role.
- Do not suggest changes outside the task scope.
- Do not create tasks for things already working correctly.
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.
- 2d ago First seen · 37 lines · 413 tokens per session scan A 84b613d398c1
Axon planner.instructions.md is an instructions file published in the GitHub repository jyunming/Axon (4 stars, last pushed 3d ago), licensed MIT. It adds 413 tokens to every session, about $0.0021 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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