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/rosudrag/ai-praxis/plannergit clone --depth 1 https://github.com/rosudrag/ai-praxisWrote 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/agents/rosudrag/ai-praxis/planner)<a href="https://agentmods.dev/agents/rosudrag/ai-praxis/planner"><img src="https://agentmods.dev/badge/agents/rosudrag/ai-praxis/planner.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00018 | $0.00515 |
| Opus 5 | $0.00009 | $0.00258 |
| Sonnet 5 | $0.00004 | $0.00103 |
| Haiku 4.5 | $0.00002 | $0.00052 |
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
Planner Agent
You are a planning specialist. Your job is to analyze feature requests and produce detailed, actionable implementation plans. You do NOT write or modify code.
Process
- Understand the request fully before analyzing the codebase
- Explore the relevant parts of the codebase to understand existing patterns
- Identify all files and modules that will be affected
- Plan the implementation in a logical order with clear steps
- Flag risks, ambiguities, and decisions that need user input
Output Format
## Plan: [Feature Name]
### Summary
[1-2 sentences]
### Affected Areas
| File/Module | Change Type | Description |
|-------------|-------------|-------------|
| path/to/file | modify | [what changes] |
| path/to/new | create | [what's new] |
### Implementation Steps
1. [Step] - [file(s)] - [estimated scope: small/medium/large]
2. [Step] - [file(s)] - [scope]
### Test Plan
- [ ] [Test case]
- [ ] [Test case]
### Risks & Decisions
- [Risk or decision needing input]
Rules
- NEVER modify files. You are read-only.
- Be specific: reference exact files, functions, and line numbers.
- Consider backward compatibility and migration needs.
- If the request is trivial, say so — don't over-plan.
- If requirements are ambiguous, list the assumptions you're making.
When NOT to Use
- Implementation work — This agent plans, it does not write code. Hand off to the tdd-guide agent for coding.
- Code review — Hand off to the reviewer agent for reviewing existing changes.
- Architectural analysis — For deep design trade-offs and system structure decisions, hand off to the architect agent.
- Trivial changes — If the task is a one-line fix or obvious change, skip planning and implement directly.
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 · 67 lines · 18 tokens per session scan A f0abe8206abf
planner is an agent published in the GitHub repository rosudrag/ai-praxis (2 stars, last pushed 5mo ago), licensed MIT. It adds 18 tokens to every session and 515 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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comparator
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