Borrowing it
Nothing to install: this file belongs to alexjbarnes/cockpit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/alexjbarnes/cockpit/main/.claude/skills/refine-issue/SKILL.mdgit clone --depth 1 https://github.com/alexjbarnes/cockpitWrote 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/alexjbarnes/cockpit/refine-issue)<a href="https://agentmods.dev/skills/alexjbarnes/cockpit/refine-issue"><img src="https://agentmods.dev/badge/skills/alexjbarnes/cockpit/refine-issue/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/alexjbarnes/cockpit/refine-issue"><img src="https://agentmods.dev/badge/skills/alexjbarnes/cockpit/refine-issue.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 7 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00076 | $0.03215 |
| Opus 5.5 | $0.00030 | $0.01286 |
| Sonnet 5.5 | $0.00015 | $0.00643 |
| Haiku 4.5 | $0.00008 | $0.00321 |
Grade A, and why
refine-issue 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Refine a cockpit issue
Turn a rough cockpit issue into a precise plan another agent will execute without interpretation. The plan replaces the issue description. The bar: an implementation agent should read the plan end-to-end and start writing code without asking a single follow-up question.
Input
The issue key (e.g. CK-12) from the invocation. If none was given, ask which issue, or use mcp__cockpit-config__list_issues to show open candidates and confirm one.
Invocation modes
This skill runs in two modes. Determine which from how it was invoked.
- Pipeline (autonomous): a ticket enters
Refine Readyand an agent runs the skill end to end with no human in the loop. Perform the detail check gate, the self-review loop, all status transitions, and skip the preview/confirm step. The agent never waits for a human. - Interactive: a human runs the skill directly (e.g. "refine CK-12"). Same flow, but do the preview/confirm step before writing.
The default is pipeline mode. Treat it as interactive only when a human invoked it directly in this session.
Refinement and the adversarial review are one stage. The skill investigates once (warm context), then self-reviews and fixes in the same run, posting each review round's findings as a comment. There is no separate review stage in the autonomous pipeline. The issue moves Refining -> Plan Review directly.
Steps
1. Read the issue and resolve the repo
mcp__cockpit-config__get_issuefor the key. One call returns the full title, description verbatim, labels, status, and every comment — capture all of it, including any context a reporter left in a comment.- Resolve the repo to investigate:
list_projects, match the issue'sprojectId, and use that project'srepoPathif set. Otherwise use the session's current working directory (today's behaviour, and the only case while cockpit is the sole project). Investigation and file reads in the steps below happen in that repo. - If the description or a comment links an image, view it directly from its URL; attachments returned by
get_issueare also plain URLs, viewable directly.
What ships with it
2 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.
- 2mo ago First seen · 181 lines · 76 tokens per session scan A 9370fae5774d
refine-issue is a skill published in the GitHub repository alexjbarnes/cockpit (16 stars, last pushed 3d ago), licensed Apache-2.0. It adds 76 tokens to every session and 3,215 once invoked, about $0.0003 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 2026-08-30.
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