Streamlit is a Python framework that turns scripts into interactive web applications for data exploration, dashboards, reports, and chat interfaces. Data scientists, machine-learning practitioners, and developers use it to build and share data apps without implementing the web interface from scratch. The catalogue add-ons support coding-agent workflows for Streamlit applications.
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/streamlit/streamlit/addressing-pr-review-commentsnpx skills add streamlit/streamlit --skill addressing-pr-review-commentsgit clone --depth 1 https://github.com/streamlit/streamlitWrote 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/streamlit/streamlit/addressing-pr-review-comments)<a href="https://agentmods.dev/skills/streamlit/streamlit/addressing-pr-review-comments"><img src="https://agentmods.dev/badge/skills/streamlit/streamlit/addressing-pr-review-comments.svg" alt="Measured on agentmods" 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 | $0.00061 | $0.02534 |
| Opus 5 | $0.00030 | $0.01267 |
| Sonnet 5 | $0.00012 | $0.00507 |
| Haiku 4.5 | $0.00006 | $0.00253 |
Grade A, and why
addressing-pr-review-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 6d 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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Address PR Comments
Address actionable review comments on the PR for the current branch using gh CLI.
Workflow checklist
Copy and track progress:
- [ ] 1. Verify auth: gh auth status
- [ ] 2. Fetch PR data and comments
- [ ] 3. Analyze and categorize comments
- [ ] 4. Present options to user
- [ ] 5. Apply selected fixes
- [ ] 6. Show summary, next steps, and offer to post replies
1. Verify authentication
gh auth status
If auth fails, prompt user to run gh auth login.
2. Fetch PR data
You must fetch both inline review comments and general PR (issue) comments. Use both when building the list of feedback to address. General comments have no file or line. Treat them as PR-level feedback.
# PR details for current branch (extract PR number from here)
gh pr view --json number,title,url,state,author,headRefName,baseRefName,reviewDecision,reviews,comments
# Inline review comments with file/line info (--paginate fetches all pages)
gh api --paginate repos/streamlit/streamlit/pulls/{PR_NUMBER}/comments
# General PR discussion comments (--paginate fetches all pages)
gh api --paginate repos/streamlit/streamlit/issues/{PR_NUMBER}/comments
Get unresolved review threads via GraphQL (inline only; used for file/path/line). The query returns the first comment of each thread for display when listing threads; full comment bodies are already fetched via the REST pulls/{PR_NUMBER}/comments and issues/{PR_NUMBER}/comments APIs above. Note: reviewThreads(first: 100) returns at most 100 threads; for PRs with more unresolved threads, use cursor-based pagination (pageInfo.hasNextPage / endCursor) or rely on the REST comments list.
gh api graphql -f query="
{
repository(owner: \"streamlit\", name: \"streamlit\") {
pullRequest(number: {PR_NUMBER}) {
reviewThreads(first: 100) {
nodes {
id
isResolved
path
line
comments(first: 1) {
nodes { author { login } body }
}
}
}
}
}
}" --jq '[.data.repository.pullRequest.reviewThreads.nodes[] | select(.isResolved == false)]'
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.
- 6d ago First seen · 249 lines · 61 tokens per session scan A f54fd8348913
addressing-pr-review-comments is a skill published in the GitHub repository streamlit/streamlit (45,690 stars, last pushed today), licensed Apache-2.0. It adds 61 tokens to every session and 2,534 once invoked, about $0.0003 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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