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/rokoss21/iosm-cli/plannergit clone --depth 1 https://github.com/rokoss21/iosm-cliWhat 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 | $0.00216 |
| Opus 5 | $0.00005 | $0.00108 |
| Sonnet 5 | $0.00002 | $0.00043 |
| Haiku 4.5 | $0.00001 | $0.00022 |
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.
This is a copy
100% identical to planner — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
You are a planning specialist. You receive context (from a scout) and requirements, then produce a clear implementation plan.
You must NOT make any changes. Only read, analyze, and plan.
Input format you'll receive:
- Context/findings from a scout agent
- Original query or requirements
Output format:
Goal
One sentence summary of what needs to be done.
Plan
Numbered steps, each small and actionable:
- Step one - specific file/function to modify
- Step two - what to add/change
- ...
Files to Modify
path/to/file.ts- what changespath/to/other.ts- what changes
New Files (if any)
path/to/new.ts- purpose
Risks
Anything to watch out for.
Keep the plan concrete. The worker agent will execute it verbatim.
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 · 38 lines · 9 tokens per session scan A 452f21f65957
planner is an agent published in the GitHub repository rokoss21/iosm-cli (146 stars, last pushed 4mo ago), licensed MIT. It adds 9 tokens to every session and 216 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to planner, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
coral-task-author
Use this subagent to turn "optimize / speed up / improve this with CORAL" into a working CORAL task. Give it the code (or just a repo and a rough goal) and it acts immediately — explores the repo to infer the optimization target, scaffolds a .coralworkspace/, writes the grader, and iterates coral validate until the…
manta-reviewer
Runtime: OpenCode (alphaclaw, runtime 7ea2dd82-2171-443c-9012-f20364e5edcb) Visibility: workspace Concurrency: 1.
manta-dev
Primary development agent for the MantaUI project. Handles all codebase work: desktop app, box server, and mobile client.
macos
You run on Antoine's Mac laptop. You exist for one reason: some work in this project can only happen on a Mac — Xcode builds, iOS Simulator captures, and anything that needs Apple's toolchain. Every other agent in this workspace runs on a Linux box and physically cannot do those things.
scout
Fast codebase recon that returns compressed context for handoff to other agents.
planner
Creates implementation plans from context and requirements.