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Iterate Pr

OrganizationPopular
getsentry
iterate-pr

Iterate on a PR until actionable CI passes and high/medium review feedback is addressed. Use for PR CI failures, review feedback, or green-check loops; do not wait for human approval, draft status, or merge gates.

Overview

Publishergetsentry
Repositoryskills
Skill nameiterate-pr
Stars
1K
Forks
53
Bundled files
6
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by getsentry on GitHub. Read the source before you install it.

Installation

Install the Iterate Pr AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/getsentry/skills/tree/main/skills/iterate-pr
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/getsentry/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/iterate-pr .claude/skills/iterate-pr
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Iterate Pr in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Iterate Pr on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Iterate Pr is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Iterate on PR Until CI Passes

Goal: fix actionable CI failures and high/medium review feedback. Stop and report human approval, draft-readiness, and merge-readiness gates.

Requires:

  • authenticated gh
  • uv
  • target repository root as cwd
  • skill-root-relative script paths, for example scripts/fetch_pr_checks.py

Bundled Scripts

ScriptRunOutput
scripts/fetch_pr_checks.pyuv run scripts/fetch_pr_checks.py [--pr NUMBER]JSON: pr, summary, checks, failure snippets
scripts/fetch_pr_feedback.pyuv run scripts/fetch_pr_feedback.py [--pr NUMBER]JSON buckets: high, medium, low, bot, resolved
scripts/monitor_pr_checks.pyuv run scripts/monitor_pr_checks.py [--pr NUMBER]terminal marker plus tab-separated checks
scripts/reply_to_thread.pyuv run scripts/reply_to_thread.py THREAD_ID BODY [...]JSON reply results

Check summary fields include failed, pending, actionable_pending, and human_gate_pending.

Monitor markers:

  • ALL_CHECKS_PASSED
  • CHECKS_DONE_WITH_FAILURES
  • NO_CHECKS_REGISTERED
  • DRAFT_PR_WITH_NO_CHECKS
  • CHECKS_BLOCKED_BY_REVIEW_GATE

Workflow

1. Identify PR

Run:

bash
gh pr view --json number,url,headRefName,isDraft,reviewDecision

Stop when:

  • no PR exists
  • draft PR has no checks after monitor grace period: report DRAFT_PR_WITH_NO_CHECKS

Draft rule: inspect existing checks/feedback only. Do not mark ready for review unless asked.

2. Handle Feedback

Run uv run scripts/fetch_pr_feedback.py [--pr NUMBER].

BucketAction
highfix
mediumfix
lowask user which to address
botskip informational comments
resolvedskip

Feedback fix checklist:

  • verify root cause
  • search related code
  • fix all instances
  • for review_bot: true: fix real issues, explain false positives

Low-priority prompt format:

text
Found 3 low-priority suggestions:
1. [l] "Consider renaming this variable" - @reviewer in api.py:42
2. [nit] "Could use a list comprehension" - @reviewer in utils.py:18
3. [style] "Add a docstring" - @reviewer in models.py:55

Which should I address? ("1,3", "all", or "none")

3. Check CI Status

Run uv run scripts/fetch_pr_checks.py [--pr NUMBER].

StateAction
failed > 0 and actionable_pending == 0fix failures
actionable_pending > 0wait; poll feedback while waiting
pending > 0 and actionable_pending == 0report CHECKS_BLOCKED_BY_REVIEW_GATE
no checks after grace periodreport NO_CHECKS_REGISTERED or DRAFT_PR_WITH_NO_CHECKS
all actionable checks passedrun post-CI feedback check

Wait for actionable review bots: sentry, warden, cursor, bugbot, seer, codeql. Do not wait for approval, isDraft, REVIEW_REQUIRED, Codecov, or informational bots.

4. Fix CI Failures

For each failure:

  1. read full log: gh run view <run-id> --log-failed
  2. trace from assertion/exception/lint rule to source
  3. state the cause before editing: "fails because X, affected by Y"
  4. search related call sites/patterns
  5. fix root cause, not symptom
  6. add focused test coverage when needed

5. Verify Locally, Then Commit and Push

Before commit:

  • test fix: rerun specific test
  • lint/type fix: rerun affected checker
  • code fix: rerun covering tests
  • local failure: fix before pushing
bash
git add <files>
git commit -m "fix: <descriptive message>"
git push

6. Monitor CI and Address Feedback

Loop:

  1. run uv run scripts/fetch_pr_checks.py
  2. handle table in step 3
  3. while actionable_pending > 0, run uv run scripts/fetch_pr_feedback.py
  4. fix new high/medium feedback immediately
  5. if changed, verify, commit, push, restart loop
  6. otherwise sleep 30 seconds and repeat
  7. after checks pass, wait 10 seconds, fetch feedback once more
  8. if new high/medium feedback exists, return to step 4

Claude Code optional: run uv run scripts/monitor_pr_checks.py through MonitorTool with persistent: false; set timeout to normal repo CI duration. Restart the monitor after every push.

Exit Conditions

ExitConditions
Successactionable CI passed; post-CI feedback clean; low-priority choice handled
Ask usersame failure after 2 attempts; feedback unclear; infrastructure issue
Stopno PR; branch needs rebase; no checks; draft no-checks; only human gates remain

Fallback

If scripts fail, use gh CLI directly:

  • gh pr view --json number,url,headRefName,isDraft,reviewDecision
  • gh pr checks --json name,state,bucket,description,link
  • gh run view <run-id> --log-failed
  • gh api repos/{owner}/{repo}/pulls/{number}/comments

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Iterate Pr AI skill do?

Iterate on a PR until actionable CI passes and high/medium review feedback is addressed. Use for PR CI failures, review feedback, or green-check loops; do not wait for human approval, draft status, or merge gates.

Why use Iterate Pr on TypingMind?

Because you install it once and use it with any model. Iterate Pr is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Iterate Pr in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/getsentry/skills/tree/main/skills/iterate-pr. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Iterate Pr?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Iterate Pr?

As many as you like. As long as a model supports skills, you can use Iterate Pr with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Iterate Pr AI skill free?

Yes. It is published on GitHub by getsentry under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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