Agent skill

Iterate PR

by meshery in meshery/meshery-operator

Iterate on a PR until CI passes. An agent skill from meshery/meshery-operator.

Apache-2.0Auto-check passedDevelopment

Install Iterate PR

skills CLI
$ npx skills add meshery/meshery-operator --skill iterate-pr -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install meshery/meshery-operator iterate-pr --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/meshery/meshery-operator.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/iterate-pr .claude/skills/iterate-pr && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
iterate-pr
GitHub stars
151
Used in
7 other repos
Token cost
~2.2k tokens
SKILL.md length
976 words
Files
5 (incl. scripts)
Skills in repo
8
Repo updated
First seen
Licence
Apache-2.0

At a glance

Iterate on a PR until CI passes. An agent skill from meshery/meshery-operator.

  • Works in 8 steps: Identify PR → Gather Review Feedback → Handle Feedback by LOGAF Priority → …
  • You need to fix CI failures
  • SKILL.md covers Bundled Scripts, Workflow, Exit Conditions and Fallback
  • Runs Python scripts from its folder; calls uv, gh and git

What it does

Iterate PR is an agent skill from meshery/meshery-operator. Iterate on a PR until CI passes. Use when you need to fix CI failures, address review feedback, or continuously push fixes until all checks are green. Automates the feedback-fix-push-wait cycle.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts (for example `scripts/fetch_pr_checks.py`, `scripts/fetch_pr_feedback.py` and `scripts/reply_to_thread.py`).

It sits in Development, covering Pull requests and Failing and flaky tests. It works with Kubernetes. The repository describes itself as: Meshery Operator is a Kubernetes Operator that deploys and manages the lifecycle of two Meshery components critical to Meshery's operations of Kubernetes clusters. The licence is Apache-2.0.

When your agent uses it

  • You need to fix CI failures
  • Address review feedback
  • Continuously push fixes until all checks are green

Example prompts

  • “/iterate-pr”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Identify PR
  2. Gather Review Feedback
  3. Handle Feedback by LOGAF Priority
  4. Check CI Status
  5. Fix CI Failures
  6. Verify Locally, Then Commit and Push
  7. Monitor CI and Address Feedback
  8. Repeat

What it can do on your machine

Read from SKILL.md and the folder at commit 632cd41. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 4 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv
    • gh
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.astral.sh
    • develop.sentry.dev

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Iterate PR loads about 2.2k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 976 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from meshery/meshery-operator at commit 632cd41, republished under its Apache-2.0 licence (© meshery). 976 words, ~2,162 tokens.

Download SKILL.mdSave it as .claude/skills/iterate-pr/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
iterate-pr
description
Iterate on a PR until CI passes. Use when you need to fix CI failures, address review feedback, or continuously push fixes until all checks are green. Automates the feedback-fix-push-wait cycle.

Iterate on PR Until CI Passes

Continuously iterate on the current branch until all CI checks pass and review feedback is addressed.

Requires: GitHub CLI (gh) authenticated.

Requires: The uv CLI for python package management, install guide at https://docs.astral.sh/uv/getting-started/installation/

Important: All scripts must be run from the repository root directory (where .git is located), not from the skill directory. Use the full path to the script via ${HOME}/.agents/skills/iterate-pr.

Bundled Scripts

scripts/fetch_pr_checks.py

Fetches CI check status and extracts failure snippets from logs.

bash
uv run ${HOME}/.agents/skills/iterate-pr/scripts/fetch_pr_checks.py [--pr NUMBER]

Returns JSON:

json
{
  "pr": {"number": 123, "branch": "feat/foo"},
  "summary": {"total": 5, "passed": 3, "failed": 2, "pending": 0},
  "checks": [
    {"name": "tests", "status": "fail", "log_snippet": "...", "run_id": 123},
    {"name": "lint", "status": "pass"}
  ]
}
scripts/fetch_pr_feedback.py

Fetches and categorizes PR review feedback using the LOGAF scale.

bash
uv run ${HOME}/.agents/skills/iterate-pr/scripts/fetch_pr_feedback.py [--pr NUMBER]

Returns JSON with feedback categorized as:

  • high - Must address before merge (h:, blocker, changes requested)
  • medium - Should address (m:, standard feedback)
  • low - Optional (l:, nit, style, suggestion)
  • bot - Informational automated comments (Codecov, Dependabot, etc.)
  • resolved - Already resolved threads

Review bot feedback (from Sentry, Warden, Cursor, Bugbot, CodeQL, etc.) appears in high/medium/low with review_bot: true — it is NOT placed in the bot bucket.

Each feedback item may also include:

  • thread_id - GraphQL node ID for inline review comments (used for replies via reply_to_thread.py)
scripts/reply_to_thread.py

Replies to PR review threads. Batches multiple replies into a single GraphQL call.

bash
uv run ${HOME}/.agents/skills/iterate-pr/scripts/reply_to_thread.py THREAD_ID "body" [THREAD_ID "body" ...]

Arguments are alternating (thread_id, body) pairs. The script sends the reply body without adding signatures, attribution, or sign-off text. Example:

bash
uv run ${HOME}/.agents/skills/iterate-pr/scripts/reply_to_thread.py \
  PRRT_abc "Fixed the null check." \
  PRRT_def "Replaced with path-segment counting."

Workflow

1. Identify PR
bash
gh pr view --json number,url,headRefName

Stop if no PR exists for the current branch.

2. Gather Review Feedback

Run ${HOME}/.agents/skills/iterate-pr/scripts/fetch_pr_feedback.py to get categorized feedback already posted on the PR.

3. Handle Feedback by LOGAF Priority

Auto-fix (no prompt):

  • high - must address (blockers, security, changes requested)
  • medium - should address (standard feedback)

When fixing feedback:

  • Understand the root cause, not just the surface symptom
  • Check for similar issues in nearby code or related files
  • Fix all instances, not just the one mentioned

This includes review bot feedback (items with review_bot: true). Treat it the same as human feedback:

  • Real issue found → fix it
  • False positive → skip, but explain why in a brief comment
  • Never silently ignore review bot feedback — always verify the finding

Prompt user for selection:

  • low - present numbered list and ask which to address:
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 would you like to address? (e.g., "1,3" or "all" or "none")

Skip silently:

  • resolved threads
  • bot comments (informational only — Codecov, Dependabot, etc.)
Replying to Comments

After processing each inline review comment, reply on the PR thread to acknowledge the action taken. Only reply to items with a thread_id (inline review comments).

When to reply:

  • high and medium items — whether fixed or determined to be false positives
  • low items — whether fixed or declined by the user

How to reply: Use ${HOME}/.agents/skills/iterate-pr/scripts/reply_to_thread.py. Batch all replies for a round into a single call:

bash
uv run ${HOME}/.agents/skills/iterate-pr/scripts/reply_to_thread.py \
  PRRT_abc "Fixed — description of change." \
  PRRT_def "Not applicable — reason."

Reply format:

  • 1-2 sentences: what was changed, why it's not an issue, or acknowledgment of declined items
  • Never add a signature, attribution line, tag, or vendor/model mention in replies
  • Keep replies tool-agnostic and identity-free
  • If the script fails, log and continue — do not block the workflow
4. Check CI Status

Run ${HOME}/.agents/skills/iterate-pr/scripts/fetch_pr_checks.py to get structured failure data.

Wait if pending: If review bot checks (sentry, warden, cursor, bugbot, seer, codeql) are still running, wait before proceeding—they post actionable feedback that must be evaluated. Informational bots (codecov) are not worth waiting for.

5. Fix CI Failures

For each failure in the script output:

  1. Read the log_snippet and trace backwards from the error to understand WHY it failed — not just what failed
  2. Read the relevant code and check for related issues (e.g., if a type error in one call site, check other call sites)
  3. Fix the root cause with minimal, targeted changes
  4. Find existing tests for the affected code and run them. If the fix introduces behavior not covered by existing tests, extend them to cover it (add a test case, not a whole new test file)

Do NOT assume what failed based on check name alone—always read the logs. Do NOT "quick fix and hope" — understand the failure thoroughly before changing code.

Show full SKILL.md (340 more words)Show less
6. Verify Locally, Then Commit and Push

Before committing, verify your fixes locally:

  • If you fixed a test failure: re-run that specific test locally
  • If you fixed a lint/type error: re-run the linter or type checker on affected files
  • For any code fix: run existing tests covering the changed code

If local verification fails, fix before proceeding — do not push known-broken code.

bash
git add <files>
gh auth status -a
git commit --signoff -m "fix: <descriptive message>"
git push

Always add exactly one sign-off to each commit for the active authenticated GitHub user. Check gh auth status -a before committing to confirm the active account, use git commit --signoff, and do not add any other trailers, signatures, or tool attribution to commits or PR comments.

7. Monitor CI and Address Feedback

Poll CI status and review feedback in a loop instead of blocking:

  1. Run uv run ${HOME}/.agents/skills/iterate-pr/scripts/fetch_pr_checks.py to get current CI status
  2. If all checks passed → proceed to exit conditions
  3. If any checks failed (none pending) → return to step 5
  4. If checks are still pending: a. Run uv run ${HOME}/.agents/skills/iterate-pr/scripts/fetch_pr_feedback.py for new review feedback b. Address any new high/medium feedback immediately (same as step 3) c. If changes were needed, commit and push (this restarts CI), then continue polling d. Sleep 30 seconds (don't increase on subsequent iterations), then repeat from sub-step 1
  5. After all checks pass, do a final feedback check: sleep 10, then run uv run ${HOME}/.agents/skills/iterate-pr/scripts/fetch_pr_feedback.py. Address any new high/medium feedback — if changes are needed, return to step 6.
8. Repeat

If step 7 required code changes (from new feedback after CI passed), return to step 2 for a fresh cycle. CI failures during monitoring are already handled within step 7's polling loop.

Exit Conditions

Success: All checks pass, post-CI feedback re-check is clean (no new unaddressed high/medium feedback including review bot findings), user has decided on low-priority items.

Ask for help: Same failure after 2 attempts, feedback needs clarification, infrastructure issues.

Stop: No PR exists, branch needs rebase.

Fallback

If scripts fail, use gh CLI directly:

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

© meshery, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts) in .claude/skills/iterate-pr of meshery/meshery-operator.

  • SKILL.md
  • scripts/__pycache__/reply_to_thread.cpython-314.pyc
  • scripts/fetch_pr_checks.py
  • scripts/fetch_pr_feedback.py
  • scripts/reply_to_thread.py

Open the folder on GitHubat commit 632cd41

Used in 7 other repositories

We found 18 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 7 other GitHub owners. This page covers the copy in meshery/meshery-operator, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Iterate PR next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Iterate PR compared with similar skills
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Fix Flakeskubernetes-sigs/agent-sandbox4.2k—~1.1kAutomated safety check: PassApache-2.0
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
PlotJuggler Ship CheckPlotJuggler/PlotJuggler6.2k—~1.3kAutomated safety check: PassMPL-2.0
Review Envoy Gateway PRenvoyproxy/gateway3.1k—~850Automated safety check: PassApache-2.0

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Works with

Questions about Iterate PR

What does Iterate PR do?

Iterate on a PR until CI passes. An agent skill from meshery/meshery-operator. Iterate PR is an agent skill from meshery/meshery-operator. Iterate on a PR until CI passes.

When should I use Iterate PR?

Iterate PR fits situations like: you need to fix CI failures; address review feedback; continuously push fixes until all checks are green.

How do I install Iterate PR in Claude Code?

Run `npx skills add meshery/meshery-operator --skill iterate-pr -a claude-code`. Or copy the skill folder (.claude/skills/iterate-pr in meshery/meshery-operator) into .claude/skills/iterate-pr in your project. Claude Code loads it when a task matches its description.

How do I install Iterate PR in Codex?

Run `npx skills add meshery/meshery-operator --skill iterate-pr -a codex`. Or copy the skill folder (.claude/skills/iterate-pr in meshery/meshery-operator) into .agents/skills/iterate-pr in your project. Codex loads it when a task matches its description.

Can I use Iterate PR in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add meshery/meshery-operator --skill iterate-pr -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/iterate-pr, .gemini/skills/iterate-pr, .github/skills/iterate-pr and .opencode/skills/iterate-pr in your project.

What does Iterate PR need to run?

Going by SKILL.md and its folder, Iterate PR needs Python for the scripts in its folder and the command-line tools its instructions call (uv, gh and git). Our summary lists: Python 3.

Does Iterate PR access the network?

SKILL.md names 2 domains. As links in the text: docs.astral.sh and develop.sentry.dev. This is read from the text; nothing was executed.

Is Iterate PR safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Iterate PR use?

Iterate PR is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Iterate PR use?

About 2.2k tokens (SKILL.md is roughly 8.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Iterate PR?

Skills that share tags, products or a category with Iterate PR: Unblock Dependabot PR (kubernetes-sigs/cloud-provider-azure, 294 stars), Fix Flakes (kubernetes-sigs/agent-sandbox, 4.2k stars), PR Babysitter (openinterpreter/openinterpreter, 69k stars) and PlotJuggler Ship Check (PlotJuggler/PlotJuggler, 6.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Iterate PR?

meshery (a GitHub organization) maintains it in meshery/meshery-operator, which has 151 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 21, 2026.

Source: meshery/meshery-operator on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.