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 skills add bostonaholic/team --skill team-questiongit clone --depth 1 https://github.com/bostonaholic/teamWrote 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/skills/bostonaholic/team/team-question)<a href="https://agentmods.dev/skills/bostonaholic/team/team-question"><img src="https://agentmods.dev/badge/skills/bostonaholic/team/team-question.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 5.1 | $0.00032 | $0.01260 |
| Opus 5 | $0.00016 | $0.00630 |
| Sonnet 5 | $0.00006 | $0.00252 |
| Haiku 4.5 | $0.00003 | $0.00126 |
Grade A, and why
team-question 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.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Team Question — Decompose the Task
Run the QUESTION phase only, then stop. The Question phase decomposes the user's intent into the artifacts that the rest of the QRSPI pipeline consumes:
1-task.md— the human's full intent. Read bydesign-authorand downstream phases that need intent. Never read byresearcherorfile-finder— they only see2-questions.md.2-questions.md— neutral research questions phrased without intent. The only fileresearcherandfile-finderever read.3-prd.md— written only when the request is vague, multi-story, cross-cutting, or replaces existing behavior (criteria inskills/product-requirements-doc/SKILL.md, loaded conditionally throughskills/decomposing-intent/SKILL.md). Referenced from1-task.md. read downstream bydesign-author.4-repos.md— written only when the topic spans more than one repository. Lists each involved repo's slug, absolute path, and role. Its presence switches the rest of the pipeline into multi-repo mode (one worktree per repo, slice/step[repo: <slug>]annotations, one PR per repo). Seeskills/qrspi-workflow/SKILL.mdfor the schema andskills/decomposing-intent/SKILL.mdfor the detection rules.
These files live in docs/plans/<id>/ where <id> is either a
ticket-derived slug (ENG-1234-add-rate-limiting) or a date-derived slug
(2026-05-01-add-rate-limiting).
Input
$ARGUMENTS may be:
- A ticket identifier (e.g.
ENG-1234) — recorded asticketIdon1-task.md's frontmatter. The orchestrator does not call any ticketing system. The ID is stored for the user's reference. - An issue URL (e.g.
https://github.com/org/repo/issues/42) — fetched withgh issue view(or equivalent) to extract the title and body before decomposition. - Free-form text — treated directly as the feature/task description.
When $ARGUMENTS is empty, discover, do not demand: ground in repo
context before asking. Read recent git log activity and the repo's
README / CLAUDE.md to propose a likely topic, then use
AskUserQuestion with labeled options to fill any genuine gap in intent.
Never bare-stop with a plain "describe it" demand when context is already
available.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · -4 lines · -14 tokens per session 6cc794c2aa92
- 3d ago First seen · 105 lines · 46 tokens per session scan A d3c3a03b0633
team-question is a skill published in the GitHub repository bostonaholic/team (11 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 1,260 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
gsd-tools
Central utility skill for GSD operations. Provides config parsing, slug generation, timestamps, path operations, and orchestrates calls to other specialized skills. Acts as the unified entry point that the original gsd-tools.cjs provided via its lib/ modules (commands, config, core, init).
babysit-babysitter-issues
This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.
spec-driven-development
Specification creation and management for the Pilot Shell methodology. Covers semantic search, clarifying questions, structured spec generation, and iterative refinement.
cog-meeting-processing
Process meeting recordings and transcripts into decisions, action items, and team dynamics.
cog-onboarding
Personalize COG Second Brain workflow through role pack selection and vault initialization.
cog-team-intelligence
Cross-reference GitHub, Linear, Slack, and PostHog with bidirectional sync for team briefs.