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 karashiiro/agent-skills --skill organizing-pr-feedbackgit clone --depth 1 https://github.com/karashiiro/agent-skillsWrote 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/karashiiro/agent-skills/organizing-pr-feedback)<a href="https://agentmods.dev/skills/karashiiro/agent-skills/organizing-pr-feedback"><img src="https://agentmods.dev/badge/skills/karashiiro/agent-skills/organizing-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/karashiiro/agent-skills/organizing-pr-feedback"><img src="https://agentmods.dev/badge/skills/karashiiro/agent-skills/organizing-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 | $0.00040 | $0.00943 |
| Opus 5.5 | $0.00016 | $0.00377 |
| Sonnet 5.5 | $0.00008 | $0.00189 |
| Haiku 4.5 | $0.00004 | $0.00094 |
Grade A, and why
organizing-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 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Organizing PR Feedback
Overview
Collect all feedback threads from a GitHub PR, cross-reference author replies to identify resolved vs. open items, and produce a consolidated revision table grouped by topic.
When to Use
- Reviewing or organizing PR comments
- Identifying what still needs addressing after reviews
- Preparing a revision pass
Fetching PR Feedback via Gateway
Issue-level comments (general discussion)
local comments = github.issue_read({
owner = "OWNER",
repo = "REPO",
issue_number = PR_NUMBER,
method = "get_comments",
perPage = 100
}):await()
local out = {}
for _, c in ipairs(comments.comments or {}) do
table.insert(out, {
id = c.id,
author = c.user and c.user.login or "unknown",
created = c.created_at,
body = c.body
})
end
result(out)
Inline review threads (code-level comments)
local res = github.pull_request_read({
method = "get_review_comments",
owner = "OWNER",
repo = "REPO",
pullNumber = PR_NUMBER,
perPage = 100
}):await()
local threads = {}
if res.review_comments and res.review_comments.reviewThreads then
for _, thread in ipairs(res.review_comments.reviewThreads) do
local t = {
isResolved = thread.IsResolved,
isOutdated = thread.IsOutdated,
path = nil,
comments = {}
}
if thread.Comments and thread.Comments.Nodes then
for _, c in ipairs(thread.Comments.Nodes) do
table.insert(t.comments, {
author = c.Author and c.Author.Login or "unknown",
body = c.Body,
path = c.Path,
line = c.Line
})
t.path = t.path or c.Path
end
end
table.insert(threads, t)
end
end
result(threads)
Note: Review thread fields use PascalCase (GraphQL style): IsResolved, Comments.Nodes, Author.Login, Body, Path, Line.
Top-level reviews (approve/request-changes verdicts)
local res = github.pull_request_read({
method = "get_reviews",
owner = "OWNER",
repo = "REPO",
pullNumber = PR_NUMBER,
perPage = 100
}):await()
local reviews = {}
for _, r in ipairs(res.reviews or {}) do
table.insert(reviews, {
author = r.user and r.user.login or "unknown",
state = r.state,
body = r.body
})
end
result(reviews)
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 · 133 lines · 40 tokens per session scan A 68c78b3c645f
organizing-pr-feedback is a skill published in the GitHub repository karashiiro/agent-skills (2 stars, last pushed 6mo ago), licensed Unlicense. It adds 40 tokens to every session and 943 once invoked, about $0.0002 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 2026-08-31.
organizing-pr-feedback compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| organizing-pr-feedbackkarashiiro/agent-skills | Skill | 2 | 6mo ago | Unlicense | A | 40 | Claude Code, Codex |
| autoreviewopenclaw/clawhub | Skill | 9.5k | today | MIT | A | 17 | Claude Code, Codex |
| omh-code-reviewrlaope/oh-my-hermes | Skill | 5.2k | today | MIT | A | 48 | Claude Code, Codex |
| agent-teams-simplify-and-hardenLeoYeAI/openclaw-master-skills | Skill | 2.2k | 6d ago | MIT | A | 115 | Claude Code, Codex |
| revdiff-planumputun/revdiff | Skill | 947 | 2d ago | MIT | A | 84 | Claude Code |
| explore-unknownsdzhng/skills | Skill | 1.4k | today | MIT | A | 162 | Claude Code, Codex |
| re0-mergeLilMGenius/paperthin | Skill | 1.4k | 4d ago | MIT | A | 70 | Claude Code |