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 agentmods add skills/strvcom/flutter-template/review-pr-commentsnpx skills add strvcom/flutter-template --skill review-pr-commentsgit clone --depth 1 https://github.com/strvcom/flutter-templateWhat 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 | $0.00117 | $0.02203 |
| Opus 5 | $0.00059 | $0.01102 |
| Sonnet 5 | $0.00023 | $0.00441 |
| Haiku 4.5 | $0.00012 | $0.00220 |
Grade A, and why
review-pr-comments scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flutter Template Review PR Comments
Handle review feedback on the current PR from both automated AI reviewers and human reviewers. Results are always presented split into AI and Human sections so the user can read them separately.
Read First
AGENTS.mddocs/PROJECT_OVERVIEW.mddocs/PROJECT_GUIDELINES.md- the files referenced by PR comments
- nearby source files when a comment depends on local context
Preconditions
- The GitHub CLI
ghmust be installed and authenticated. - The current branch must have an open PR.
- Do not apply fixes, reply to comments, commit, or push until the user approves those actions.
Step 1: Find the PR
gh pr list --head "$(git branch --show-current)" --json number,url --jq '.[0]'
If no PR is found, inform the user and stop. If gh is not available or not authenticated, report the blocker and the command that failed.
Step 2: Fetch All Review Comments
Extract the PR number and owner/repo:
number="$(gh pr list --head "$(git branch --show-current)" --json number --jq '.[0].number')"
repo="$(gh repo view --json nameWithOwner --jq '.nameWithOwner')"
Fetch all PR review comments, including inline comments on code:
gh api "repos/$repo/pulls/$number/comments" --paginate \
--jq '.[] | {id: .id, user: .user.login, user_type: .user.type, path: .path, line: .line, original_line: .original_line, diff_hunk: .diff_hunk, body: .body}'
Also fetch top-level issue-style comments on the PR (some reviewers use these):
gh api "repos/$repo/issues/$number/comments" --paginate \
--jq '.[] | {id: .id, user: .user.login, user_type: .user.type, body: .body}'
Optionally fetch the PR review summaries if the user asks for every review artifact:
gh api "repos/$repo/pulls/$number/reviews" --paginate \
--jq '.[] | {id: .id, user: .user.login, user_type: .user.type, state: .state, body: .body}'
Step 3: Classify Each Comment — AI vs Human
Split every comment into one of two buckets:
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.
- 2d ago First seen · 210 lines · 117 tokens per session scan A fa20e3a708f5
review-pr-comments is a skill published in the GitHub repository strvcom/flutter-template (24 stars, last pushed 2mo ago), licensed MIT. It adds 117 tokens to every session and 2,203 once invoked, about $0.0006 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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