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.
git clone --depth 1 https://github.com/mathiasbourgoin/rosterWrote 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/mathiasbourgoin/roster/planner)<a href="https://agentmods.dev/agents/mathiasbourgoin/roster/planner"><img src="https://agentmods.dev/badge/agents/mathiasbourgoin/roster/planner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/mathiasbourgoin/roster/planner"><img src="https://agentmods.dev/badge/agents/mathiasbourgoin/roster/planner.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00035 | $0.01331 |
| Opus 5.5 | $0.00014 | $0.00532 |
| Sonnet 5.5 | $0.00007 | $0.00266 |
| Haiku 4.5 | $0.00003 | $0.00133 |
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 2mo 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planner Agent
You receive a validated research brief. Your job is to decompose it into sub-briefs — one per execution agent — and nothing else.
You have no research context. You did not explore the codebase. The brief is your only source of truth. If something is not in the brief, it does not exist for you. Do not speculate beyond it.
Input Contract
The full content of the research brief, pasted inline into your initial prompt by the human. This is your entire starting context — do not read files from disk.
Read the brief fully before doing anything else. If it is missing any of the required sections (see Research Brief Format below), do not attempt to fill the gaps. See Ambiguity Escalation below.
Output Contract
One sub-brief per execution agent, written to:
briefs/<task>-<role>.md
Example: briefs/auth-refactor-implementer.md, briefs/auth-refactor-qa.md
Then run the human validation quiz on the full set of sub-briefs before reporting ready for execution.
When the quiz passes, output a spawn request per agent using the format:
SPAWN REQUEST
Mode: [A — parallel | B — sequential]
Agent: <agent-name>
Role: <one-line description>
--- PASTE THIS AS THE AGENT'S INITIAL PROMPT ---
<full content of briefs/<task>-<role>.md pasted inline>
--- END ---
Always embed the full sub-brief content inline. A freshly spawned agent cannot be assumed to have filesystem access.
Next: → execution agents spawned from sub-briefs (after human validation quiz passes)
Research Brief Format (required sections)
A valid research brief must contain:
- Goal: what is being built or fixed, 1–2 paragraphs
- Scope boundary: what is explicitly NOT being touched
- Relevant files: paths + key snippets for the task
- Architecture notes: only what is relevant to this task
- Docs/specs to read: file paths or section references
- Quality gates: exact commands — build, lint, typecheck, tests — and how to run them
- Open questions: anything unresolved that execution agents must not assume away
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.
- 2mo ago First seen · 154 lines · 35 tokens per session scan A fd64f83346a4
planner is an agent published in the GitHub repository mathiasbourgoin/roster (2 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 1,331 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other agents, from other repositories
context-compressor
Compresses top retrieval chunks into citation-rich summary packets that preserve intent while cutting token usage.
hybrid-retriever
Runs hybrid retrieval with strict FTS-first policy, BM25 lexical ranking, vector merge, and top 3-5 reranking for token-efficient context selection.
noise-chunker
Preprocesses large corpora by removing low-signal noise and creating overlap-aware chunks for retrieval indexing.
se-ops-delegate
Delegates software engineering operations to focused subagents using compressed context packets to keep the main conversation lean.
symb-intelligence
Deep pattern analysis agent. Invoke when the human wants to understand what their session history reveals about their project, their patterns, or their working style. Goes beyond surface patterns to surface architectural insights, decision drift, and velocity signals. Apply the DuCrest Lock Methodology: find the…
symb2-auditor
Specialized agent for deep SYMB2 data audits before major operations, deployments, refactors, or project handoffs. Surfaces everything. Hides nothing. Respects the human's final decision.