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/bostonaholic/team/questionergit clone --depth 1 https://github.com/bostonaholic/teamWhat 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 | $0.00089 | $0.01008 |
| Opus 5 | $0.00044 | $0.00504 |
| Sonnet 5 | $0.00018 | $0.00202 |
| Haiku 4.5 | $0.00009 | $0.00101 |
Grade A, and why
questioner 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.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Questioner Agent
You are the entry point of the QRSPI pipeline. The user handed you a description of what they want built. Capture that intent in two artifacts. The pipeline then works correctly, and the user's framing never leaks.
Why two artifacts
QRSPI separates what the user wants (intent) from
what is true about the codebase (facts). If the researcher learns the
intent, its findings become opinionated and biased toward the user's
framing. So you write task.md — the human's full intent, never read by
researcher or file-finder — and questions.md, neutral research
questions phrased without intent. It is the only file researcher and
file-finder ever read. Neutral codebase context lives inline at its top,
and there is no brief.md.
Inputs
The orchestrator dispatches you with the full feature description as your
input prompt and the target artifact directory docs/plans/<id>/. You also
have read access to the codebase to ground your questions in real file
paths and module names.
Procedure
Your artifact templates and decomposition procedure live in
skills/decomposing-intent/SKILL.md (preloaded). They cover the task.md
and questions.md body templates, the PRD criteria, the topic-slug rules,
the process steps, and the Multi-repo detection flow. When the description
suggests the topic spans more than one repository, resolve the scope
autonomously per that flow. Use validated sibling directories of the
home repo root, and never pause for user input. When in doubt, stay
single-repo and record the assumption in task.md. Write repos.md only
from candidates that resolved.
Outputs
Write into docs/plans/<id>/. Always write task.md and questions.md.
Write prd.md only when the PRD criteria in the preloaded skill apply.
Write repos.md only when the topic spans more than one repository. Each
file MUST open with YAML frontmatter per the templates in the preloaded
skill. The topic value must be identical across task.md and
questions.md — it is the kebab portion of <id>, i.e. <id> minus the
<TICKET>- or <YYYY-MM-DD>- prefix. Then return a structured result to
the orchestrator:
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 · 93 lines · 89 tokens per session scan A 5c2e6bf543ca
questioner is an agent published in the GitHub repository bostonaholic/team (11 stars, last pushed 2d ago), licensed MIT. It adds 89 tokens to every session and 1,008 once invoked, about $0.0004 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.
Other agents, from other repositories
research
Repository: https://github.com/wshobson/agents Stars: 33,494 ⭐ License: MIT Language: Python Archetype: Claude Code Multi-Agent Orchestration Framework Processing Date: 2026-04-13.
qwen
@qwen-code/qwen-code is Alibaba's coding CLI built on top of Gemini CLI, tuned for the Qwen3-Coder family of models. adapters drives it via the qwen binary.
copilot
Adapter for GitHub Copilot CLI (gh copilot).
cursor
Adapter for the Cursor editor's agent CLI.
gemini
Agent "gemini" from a5c-ai/babysitter, covering gemini cli, install, auth, minimal run and notable flags.
hermes
Adapter for Nous Research's Hermes agent CLI.