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 sfc-gh-eraigosa/dotfiles --skill issue-groominggit clone --depth 1 https://github.com/sfc-gh-eraigosa/dotfilesWrote 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/sfc-gh-eraigosa/dotfiles/issue-grooming)<a href="https://agentmods.dev/skills/sfc-gh-eraigosa/dotfiles/issue-grooming"><img src="https://agentmods.dev/badge/skills/sfc-gh-eraigosa/dotfiles/issue-grooming/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/sfc-gh-eraigosa/dotfiles/issue-grooming"><img src="https://agentmods.dev/badge/skills/sfc-gh-eraigosa/dotfiles/issue-grooming.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 | $0.00140 | $0.02062 |
| Opus 5.5 | $0.00056 | $0.00825 |
| Sonnet 5.5 | $0.00028 | $0.00412 |
| Haiku 4.5 | $0.00014 | $0.00206 |
Grade A, and why
issue-grooming 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 today.
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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
issue-grooming — groom the backlog against what actually shipped
Grooming answers three questions for every open issue, with evidence from the repo rather than from the issue's own text: is it already addressed, how important is it now, and does its body still describe work that fits the repo. The output is a labelled, trimmed backlog and a report the owner can scan in a minute.
Why the rigor: issues are written at a moment in time. In this repo code moves (src/ → sdk/),
leaves of a plan merge under PR titles that never mention the issue, follow-ups get absorbed by
later objectives, and a bot keeps filing reports. An issue's text is the claim; main, merged
PRs, and docs/mbo/index.md are the evidence. Only the evidence decides.
0 — Setup (one command)
bash ai/skills/issue-grooming/scripts/groom-snapshot.sh -o <scratch>/groom
bash ai/skills/issue-grooming/scripts/groom-labels.sh # ensures priority:1..5 exist (idempotent)
The snapshot writes issues.json/issues.txt (bodies + comments), labels.json,
merged-prs.json (with the issues each PR closed), and clusters.txt (a first-cut grouping).
Read clusters.txt and regroup by hand: a cluster is a set of issues that share a program,
tool, or plan (e.g. all fleet-connect sub-issues, all gss tech-debt, one bot's reports).
Clusters are the unit of triage because verifying them needs the same files.
Hooks note (this repo): the privacy hook blocks command text containing the login name or an
absolute home path, so refer to scratch dirs by a glob or ~. The Bash tool runs zsh; build
flag lists as arrays.
1 — Verify, one cluster at a time
For each cluster, establish what shipped before judging any issue:
docs/mbo/index.mdrow(s) and the plan'sTRACKING.mdledger — the state column and the "pending" lists are the fastest map of what landed and what is still owed.merged-prs.json— grep titles for the slug / leaf names and the issue number; many fixes cite the issue only in the PR title (… (#331)), not in a closing reference.- The code or docs the issue names — open the path on
main(git show main:<path>when the working tree is on a branch) and confirm the verb/flag/test/doc exists.
What ships with it
8 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.
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.
- today First seen · 136 lines · 140 tokens per session scan A bd0202e318b1
issue-grooming is a skill published in the GitHub repository sfc-gh-eraigosa/dotfiles (50 stars, last pushed today), licensed Apache-2.0. It adds 140 tokens to every session and 2,062 once invoked, about $0.0006 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 2026-10-20.
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