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 groom-backloggit 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/groom-backlog)<a href="https://agentmods.dev/skills/bostonaholic/team/groom-backlog"><img src="https://agentmods.dev/badge/skills/bostonaholic/team/groom-backlog/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/skills/bostonaholic/team/groom-backlog"><img src="https://agentmods.dev/badge/skills/bostonaholic/team/groom-backlog.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 | $0.00047 | $0.00793 |
| Opus 5 | $0.00023 | $0.00396 |
| Sonnet 5 | $0.00009 | $0.00159 |
| Haiku 4.5 | $0.00005 | $0.00079 |
Grade A, and why
groom-backlog 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 4d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
groom-backlog — plan, ask, wait, then execute
Grooming mutates shared state that a whole team reads. Placement, dates, and ticket rewrites
are judgment calls with no mechanical ground truth. A wrong one stays invisible until someone
acts on a board that now lies. So this skill plans, asks the consequential questions, and
waits. It acts only on approval. That is the shape pr-open-comments takes for an item below
its auto-apply bar. That checkpoint is the ethos applied, not a hole in it. The pipeline's
autonomous middle earns its autonomy from mechanical gates. A grooming judgment has none, so
the user's answer stays this skill's one gate until a loop-driven controller replaces it.
The shape is principle-plan-present-wait: plan the mutations to a file,
present each consequential choice with one recommendation, and execute only the answered
subset.
Procedure references
Read each reference completely when reaching that stage. Follow them in order; later stages depend on state and gates established earlier.
- Vocabulary
- Input
- The board-level pass
- Step 1 — Load once, in bulk
- Step 2 — Compute the gap inventory, do not eyeball it
- Step 3 — Verify claims against the code
- Step 4 — Rank the verified candidates
- Step 5 — Cluster by outcome, not by component
- Step 6 — Find the dependencies, then propose the links
- Step 7 — Write the plan to a file
- Step 8 — Present the consequential choices and wait
- Step 9 — Execute in dependency order
- Step 10 — Verify by re-querying, never by memory
- Step 11 — Report, including what you did not change
- The promotion standard
- Tracker recipes
- Hard rules
What ships with it
18 files 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.
- agents/openai.yaml 179 B
- references/01-vocabulary.md 2.6 KB
- references/02-input.md 2.1 KB
- references/03-the-board-level-pass.md 277 B
- references/04-step-1-load-once-in-bulk.md 5.0 KB
- references/05-step-2-compute-the-gap-inventory-do-not-eyeball-it.md 972 B
- references/06-step-3-verify-claims-against-the-code.md 4.2 KB
- references/07-step-4-rank-the-verified-candidates.md 1.2 KB
- references/08-step-5-cluster-by-outcome-not-by-component.md 1.5 KB
- references/09-step-6-find-the-dependencies-then-propose-the-links.md 2.2 KB
- references/10-step-7-write-the-plan-to-a-file.md 1.3 KB
- references/11-step-8-present-the-consequential-choices-and-wait.md 3.0 KB
- references/12-step-9-execute-in-dependency-order.md 3.3 KB
- references/13-step-10-verify-by-re-querying-never-by-memory.md 1.1 KB
- references/14-step-11-report-including-what-you-did-not-change.md 2.0 KB
- references/15-the-promotion-standard.md 7.5 KB
- references/16-tracker-recipes.md 6.3 KB
- references/17-hard-rules.md 7.6 KB
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.
- 4d ago Changed · -795 lines · -178 tokens per session f6d104917e8a
- 6d ago Changed · +17 lines 617b25b6abad
- 9d ago First seen · 828 lines · 225 tokens per session scan A 04ab4aa70b21
groom-backlog is a skill published in the GitHub repository bostonaholic/team (11 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 793 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 2026-08-30.
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cog-team-intelligence
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