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 jmagar/claude-homelab --skill gh-address-commentsgit clone --depth 1 https://github.com/jmagar/claude-homelabWrote 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/jmagar/claude-homelab/gh-address-comments)<a href="https://agentmods.dev/skills/jmagar/claude-homelab/gh-address-comments"><img src="https://agentmods.dev/badge/skills/jmagar/claude-homelab/gh-address-comments/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/jmagar/claude-homelab/gh-address-comments"><img src="https://agentmods.dev/badge/skills/jmagar/claude-homelab/gh-address-comments.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00085 | $0.01420 |
| Opus 5 | $0.00043 | $0.00710 |
| Sonnet 5 | $0.00017 | $0.00284 |
| Haiku 4.5 | $0.00009 | $0.00142 |
Grade A, and why
gh-address-comments 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 12d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Comment Handler with Resolution Tracking
⚠️ MANDATORY SKILL INVOCATION ⚠️
YOU MUST invoke this skill (NOT optional) when the user mentions ANY of these triggers:
- "address PR comments", "fix review feedback", "handle PR review"
- "resolve PR threads", "respond to review", "work through comments"
- "address the feedback", "mark threads resolved", "clear review comments"
- Any mention of systematically handling GitHub pull request review comments
Failure to invoke this skill when triggers occur violates your operational requirements.
Find the open PR for the current branch and systematically address all review comments with mandatory resolution verification. This workflow ensures no feedback slips through the cracks by tracking threads as tasks, linking commits to specific reviews, and blocking completion if any threads remain unresolved. Run all gh commands with elevated network access.
Prerequisites: Verify gh is authenticated by running gh auth status with escalated permissions (workflow/repo scopes required). If not authenticated, run gh auth login --scopes repo,workflow. If sandboxing blocks gh auth status, rerun with sandbox_permissions=require_escalated.
Workflow
1) Fetch all PR comments
Run python3 $HOME/.claude/skills/gh-address-comments/scripts/fetch_comments.py to fetch all comments and review threads on the PR. Store output for later verification.
Example:
python3 $HOME/.claude/skills/gh-address-comments/scripts/fetch_comments.py > /tmp/pr_comments.json
2) Create tracking checklist
Parse the fetched comments and create a task checklist using TaskCreate for each review thread. Each task should:
- Include the thread number, file path, line number, and comment summary
- Have status
pendinginitially - Store the thread ID in task metadata for later resolution
Task format:
- Subject:
Address comment #N: [file]:[line] - Description: Comment author, body preview, and what needs to be done
- Metadata:
{"thread_id": "PRRT_kwDOABC...", "file": "path/to/file.ts", "line": 42}
What ships with it
14 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 303 B
- assets/github-small.svg 853 B
- assets/github.png 1.8 KB
- examples/basic-workflow.sh 2.9 KB runs code
- LICENSE.txt 11 KB
- load-env.sh 1.6 KB runs code
- README.md 6.5 KB
- references/api-endpoints.md 6.5 KB
- references/quick-reference.md 8.7 KB
- references/resolution-workflow.md 8.1 KB
- references/troubleshooting.md 9.3 KB
- scripts/fetch_comments.py 6.8 KB runs code
- scripts/mark_resolved.py 2.9 KB runs code
- scripts/verify_resolution.py 3.1 KB runs code
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.
- 12d ago First seen · 143 lines · 85 tokens per session scan A a5a3dd04767d
gh-address-comments is a skill published in the GitHub repository jmagar/claude-homelab (78 stars, last pushed 1mo ago), licensed MIT. It adds 85 tokens to every session and 1,420 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.
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