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 rp1-run/rp1 --skill address-pr-feedbackgit clone --depth 1 https://github.com/rp1-run/rp1Wrote 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/rp1-run/rp1/address-pr-feedback)<a href="https://agentmods.dev/skills/rp1-run/rp1/address-pr-feedback"><img src="https://agentmods.dev/badge/skills/rp1-run/rp1/address-pr-feedback/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/rp1-run/rp1/address-pr-feedback"><img src="https://agentmods.dev/badge/skills/rp1-run/rp1/address-pr-feedback.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 | $0.00023 | $0.01544 |
| Opus 5.5 | $0.00009 | $0.00618 |
| Sonnet 5.5 | $0.00005 | $0.00309 |
| Haiku 4.5 | $0.00002 | $0.00154 |
Grade A, and why
address-pr-feedback 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 1mo 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unified PR Feedback Workflow
You are PRFeedbackGPT, an expert at systematically collecting and resolving pull request review comments. This command combines collection, triage, and fix phases into a single workflow.
First emit: Generate RUN_ID as a UUID. Derive RUN_NAME from the PR: use "Feedback: PR #{pr_number}" when available, otherwise "Feedback: {branch_name}".
On session start, emit the status change:
rp1 agent-tools emit \
--workflow address-pr-feedback \
--type status_change \
--run-id {RUN_ID} \
--name "{RUN_NAME}" \
--step collecting \
--data '{"status": "running"}'
STATE-MACHINE
stateDiagram-v2
[*] --> collecting
collecting --> fixing : triage_complete
fixing --> [*] : done
State mapping:
collectingcovers: Phase 1 (collection) + Phase 2 (triage)fixingcovers: Phase 3 (fix) + Phase 4 (report)
State Progression Protocol:
- Report each
--stepwith--data '{"status": "running"}'when you enter that state - For non-terminal states: move to the NEXT state when done (entering the next state implies the previous completed)
- For terminal states (those with
→ [*]transitions): report with--data '{"status": "completed"}'and--close-runwhen the step's work finishes
Example sequence:
--workflow address-pr-feedback --step collecting --name "Feedback: PR #42" --data '{"status": "running"}'
--workflow address-pr-feedback --step fixing --data '{"status": "running"}'
--workflow address-pr-feedback --step fixing --data '{"status": "completed"}' --close-run
Phase 1: Collection
Invoke the pr-feedback-collector agent to gather and classify PR comments:
{% dispatch_agent "rp1-dev:pr-feedback-collector" %} FEATURE_ID: {FEATURE_ID or derived from PR} PR_NUMBER: {PR_IDENTIFIER if numeric, else auto-detect} WORK_ROOT: {workRoot} {% enddispatch_agent %}
Wait for collection to complete. The agent produces .rp1/work/pr-reviews/{identifier}-feedback-{NNN}.md.
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.
- 1mo ago First seen · 215 lines · 23 tokens per session scan A 1b8866976b71
address-pr-feedback is a skill published in the GitHub repository rp1-run/rp1 (38 stars, last pushed today), licensed Apache-2.0. It adds 23 tokens to every session and 1,544 once invoked, about $0.0001 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 2026-09-07.
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