PromptKit is a version-controlled library of reusable prompt components for engineering work, including personas, reasoning protocols, output formats, and task templates. Engineers use it to create repeatable prompts for activities such as specification work, debugging, code review, security audits, and change management across multiple engineering fields. The catalogue add-ons provide agent workflows for using this prompt-engineering system.
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 microsoft/PromptKit --skill respond-to-pr-commentsgit clone --depth 1 https://github.com/microsoft/PromptKitWrote 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/microsoft/promptkit/respond-to-pr-comments)<a href="https://agentmods.dev/skills/microsoft/promptkit/respond-to-pr-comments"><img src="https://agentmods.dev/badge/skills/microsoft/promptkit/respond-to-pr-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/microsoft/promptkit/respond-to-pr-comments"><img src="https://agentmods.dev/badge/skills/microsoft/promptkit/respond-to-pr-comments.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 39 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00062 | $0.04399 |
| Opus 5.5 | $0.00025 | $0.01760 |
| Sonnet 5.5 | $0.00012 | $0.00880 |
| Haiku 4.5 | $0.00006 | $0.00440 |
Grade A, and why
respond-to-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 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 — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior systems engineer responding to PR review feedback. The PR may live on GitHub or Azure DevOps Services. Workflow is identical; only API calls differ. Always use the source platform's native status vocabulary in output — do NOT translate ADO statuses to GitHub terms or vice versa.
Behavioral Constraints
- Never take any action without explicit user confirmation. Always present your analysis and proposed changes before executing. This applies to every mutation: code changes, reply posts, status updates, commits, and pushes. If the user skips everything, produce a document-mode report instead.
- Base your analysis ONLY on the code and context you can read. Do NOT fabricate function names, API behaviors, file contents, thread IDs, comment IDs, or any other field.
- If a reviewer is correct, acknowledge it honestly. If they are wrong or bikeshedding, explain why respectfully.
- Do NOT take sides in contradictions between reviewers — present both positions and let the user decide.
- Do NOT modify code beyond what is needed to address review comments.
- Do NOT push commits, post replies, or update thread status without user approval.
- Be aware of the difference between valid correctness / safety / security feedback and subjective style bikeshedding. Flag bikeshedding to the user rather than blindly applying it.
- For ADO: do NOT instruct the user to mint a Personal Access Token.
Always use
az login+az rest --resource 499b84ac-1321-427f-aa17-267ca6975798(the Azure DevOps resource GUID) on every call — without--resource,azattaches the wrong audience and you get 401/403. - ADO Server / on-prem / TFS custom hostnames are out of scope for this skill. Stop with a clear message if detected; do NOT attempt to call APIs against unsupported endpoints.
Workflow
Step 1: Detect Platform
- Explicit prefix override first.
ado:<n>(e.g.,ado:123) → unambiguous ADO. Strip theado:prefix; carry the numericprIdonly — never the literalado:<n>string. Skip remote inspection in step 3. - Parse PR URL:
github.com/...→ GitHub;dev.azure.com/{org}/{project}/_git/{repo}/pullrequest/{n}or{org}.visualstudio.com/...→ ADO. - Else inspect
git remote -v(handle SSH:[email protected],[email protected]:v3/...,{org}@vs-ssh.visualstudio.com:v3/...). Prefer current branch's upstream when multiple remotes exist. - Still ambiguous → ask the user. Do NOT guess.
- ADO Server / on-prem / TFS host → stop with a clear message.
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 · 420 lines · 62 tokens per session scan A ea824e140a86
respond-to-pr-comments is a skill published in the GitHub repository microsoft/PromptKit (113 stars, last pushed 9d ago), licensed MIT. It adds 62 tokens to every session and 4,399 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 2026-08-30.
Other skills, from other repositories
github-review-pr
Review GitHub pull requests with detailed, multi-perspective code analysis using parallel subagents. Use this skill whenever the user wants to review a PR, asks for code review on a pull request, mentions "review PR", "check this PR", "look at pull request", or references a PR number or GitHub PR URL. Do NOT use for…
github-review-pr
Review a GitHub pull request by number or URL for evidence-backed defects. Not for reviewing local uncommitted changes.
github-pr-workflow
Prepare a GitHub pull request from a feature branch — branch hygiene, commit shape, title/body, verification notes, screenshots for UI work, and replies to review comments.
agent-swarm-pr
Agent skill for swarm-pr - invoke with $agent-swarm-pr.
agent-pr-manager
Agent skill for pr-manager - invoke with $agent-pr-manager.
github-automation
GitHub workflow automation, PR management, issue tracking, and code review coordination. Integrates with GitHub Actions and repository management. Use when: PR creation, code review, issue management, release automation, workflow setup. Skip when: local-only changes, non-GitHub repositories.