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Greploop Apps

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michaelshimeles
greploop-apps

Iteratively improves a PR (GitHub), MR (GitLab), or shelved changelist (Perforce) until Greptile gives it a 5/5 confidence score with zero unresolved comments. Identical to greploop, but triggers reviews by tagging @greptile-apps, which bypasses Greptile's file-count limit on huge PRs that the plain @greptile mention refuses to review. Use when the user wants to fully optimize a large PR/MR/CL against Greptile's code review standards.

Overview

Publishermichaelshimeles
Repositoryskills
Skill namegreploop-apps
Stars
1.2K
Forks
198
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Greploop Apps 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/michaelshimeles/skills/tree/main/greploop-apps
  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/michaelshimeles/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/greploop-apps .claude/skills/greploop-apps
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Greploop Apps 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 Greploop Apps 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 Greploop Apps 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.

Greploop Apps

Iteratively fix a PR/MR/CL until Greptile gives a perfect review: 5/5 confidence, zero unresolved comments. This variant tags @greptile-apps to trigger reviews, which bypasses the file-count limit that makes Greptile skip huge PRs (e.g. "Too many files changed for review"). Because auto-review skips those PRs, every push needs a fresh @greptile-apps review comment.

Inputs

  • PR/MR/CL number (optional): If not provided, detect the PR/MR for the current branch, or the default pending changelist for p4.
  • --max-iterations N (optional, default 10): cap on review-fix-push cycles before the loop stops and reports the current state. Raise it for large PRs where Greptile keeps surfacing new findings; lower it to bound cost.

Instructions

0. Detect platform

First check for Perforce, then fall back to git remote detection:

bash
# Check for Perforce environment
if p4 info >/dev/null 2>&1; then
  VCS="perforce"
else
  REMOTE_URL=$(git remote get-url origin)
  if echo "$REMOTE_URL" | grep -qi "gitlab"; then
    VCS="gitlab"
  else
    VCS="github"
  fi
fi

For self-hosted GitLab instances whose hostname doesn't contain "gitlab", the user can override by passing --vcs gitlab as an input. For Perforce, pass --vcs perforce.

1. Identify the PR/MR/CL

GitHub:

bash
gh pr view --json number,headRefName -q '{number: .number, branch: .headRefName}'

GitLab:

bash
glab mr view --output json | jq '{iid: .iid, branch: .source_branch}'

Switch to the PR/MR branch if not already on it.

Perforce:

bash
# List pending changelists for current user/client
p4 changes -s pending -u $P4USER -c $P4CLIENT

# Describe a specific CL
p4 describe -s <CL_NUMBER>

Ensure the correct workspace (p4 client) is set before proceeding.

Key field differences:

  • GitHub: number, headRefName, headRefOid
  • GitLab: iid, source_branch, sha
  • Perforce: changelist number, P4CLIENT, shelved files

2. Loop

Repeat the following cycle at most --max-iterations times (default 10) to avoid runaway loops.

A. Trigger Greptile review

Push/shelve the latest changes (if any):

GitHub/GitLab:

bash
git push

Perforce:

bash
# Re-shelve to update the shelved files for review
p4 shelve -f -c <CL_NUMBER>

Wait for checks to start after push/shelve:

bash
sleep 5

GitHub — check if Greptile is already running before posting a new trigger comment:

bash
GREPTILE_STATE=$(gh pr checks <PR_NUMBER> --json name,state | jq -r '.[] | select(.name | test("greptile"; "i")) | .state')

If Greptile is not already running (PENDING or IN_PROGRESS), request a fresh review:

bash
if [ "$GREPTILE_STATE" != "PENDING" ] && [ "$GREPTILE_STATE" != "IN_PROGRESS" ]; then
  gh pr comment <PR_NUMBER> --body "@greptile-apps review"
fi

Then poll for the Greptile check run to complete:

bash
HEAD_SHA=$(gh pr view <PR_NUMBER> --json headRefOid -q .headRefOid)
ATTEMPTS=0
MAX_ATTEMPTS=60
POLL_INTERVAL_SECONDS=10

while true; do
  ATTEMPTS=$((ATTEMPTS + 1))
  if [ "$ATTEMPTS" -gt "$MAX_ATTEMPTS" ]; then
    echo "Timed out waiting for the Greptile check run after approximately 10 minutes." >&2
    exit 1
  fi

  GREPTILE_CHECK=$(gh api "repos/{owner}/{repo}/commits/$HEAD_SHA/check-runs" \
    --jq '.check_runs[] | select(.name | test("greptile"; "i"))' 2>/dev/null)
  
  if [ -z "$GREPTILE_CHECK" ]; then
    echo "Waiting for Greptile check to appear..."
    sleep "$POLL_INTERVAL_SECONDS"
    continue
  fi
  
  STATUS=$(echo "$GREPTILE_CHECK" | jq -r '.status // "completed"')
  CONCLUSION=$(echo "$GREPTILE_CHECK" | jq -r '.conclusion // "pending"')
  
  if [ "$STATUS" = "completed" ]; then
    if [ "$CONCLUSION" = "success" ]; then
      echo "Greptile check passed!"
    else
      echo "Greptile check completed with: $CONCLUSION"
    fi
    break
  fi
  
  echo "Waiting for Greptile... (status: $STATUS)"
  sleep "$POLL_INTERVAL_SECONDS"
done

Huge-PR fallback: on PRs over Greptile's file limit, the tagged review may never create a check run for the new head — Greptile instead edits its existing summary comment when it finishes. If the check run has not appeared after a few attempts, poll the latest Greptile-authored issue comment instead and stop when its updated_at moves past the time you posted the trigger and its body carries a confidence score (rather than the "Too many files changed" notice):

bash
gh api --paginate "repos/{owner}/{repo}/issues/<PR_NUMBER>/comments?per_page=100" \
  --jq '[.[] | select(.user.login | test("greptile"; "i"))] | sort_by(.updated_at) | last | {updated_at, body}'

If polling times out, stop the greploop workflow and report the timeout. Do not continue with stale or missing review results.

GitLab — check if Greptile is already running before posting a trigger comment:

bash
PIPELINES=$(glab api "projects/:fullpath/merge_requests/<MR_IID>/pipelines")
GREPTILE_RUNNING=$(echo "$PIPELINES" | jq '[.[] | select(.status == "running" or .status == "pending")] | length')

If no pipeline is running, post a trigger comment:

bash
if [ "$GREPTILE_RUNNING" = "0" ]; then
  glab mr note <MR_IID> --message "@greptile-apps review"
fi

Perforce — Perforce does not have native check runs. If Greptile is integrated via a webhook triggered on p4 shelve, wait for it to process. Check your Greptile installation's webhook endpoint or dashboard for the review status. Poll by re-fetching the Greptile review comment on the CL until a score appears.

Then poll for the Greptile pipeline job to complete (see GitLab API reference):

bash
HEAD_SHA=$(glab mr view <MR_IID> --output json | jq -r '.sha')
ATTEMPTS=0
MAX_ATTEMPTS=60
POLL_INTERVAL_SECONDS=10

while true; do
  ATTEMPTS=$((ATTEMPTS + 1))
  if [ "$ATTEMPTS" -gt "$MAX_ATTEMPTS" ]; then
    echo "Timed out waiting for the Greptile pipeline job after approximately 10 minutes." >&2
    exit 1
  fi

  PIPELINES=$(glab api "projects/:fullpath/merge_requests/<MR_IID>/pipelines")
  # Find the most recent pipeline for this SHA
  PIPELINE_ID=$(echo "$PIPELINES" | jq -r --arg sha "$HEAD_SHA" \
    '[.[] | select(.sha == $sha)] | sort_by(.id) | last | .id // empty')

  if [ -z "$PIPELINE_ID" ]; then
    echo "Waiting for Greptile pipeline to appear..."
    sleep "$POLL_INTERVAL_SECONDS"
    continue
  fi

  JOBS=$(glab api "projects/:fullpath/pipelines/$PIPELINE_ID/jobs")
  GREPTILE_JOB=$(echo "$JOBS" | jq '.[] | select(.name | test("greptile"; "i"))')

  if [ -z "$GREPTILE_JOB" ]; then
    echo "Waiting for Greptile job to appear..."
    sleep "$POLL_INTERVAL_SECONDS"
    continue
  fi

  JOB_STATUS=$(echo "$GREPTILE_JOB" | jq -r '.status')

  if [ "$JOB_STATUS" = "success" ] || [ "$JOB_STATUS" = "failed" ] || [ "$JOB_STATUS" = "canceled" ]; then
    echo "Greptile job completed with: $JOB_STATUS"
    break
  fi

  echo "Waiting for Greptile... (status: $JOB_STATUS)"
  sleep "$POLL_INTERVAL_SECONDS"
done

If polling times out, stop the greploop workflow and report the timeout. Do not continue with stale or missing review results.

B. Fetch Greptile review results

Greptile may surface its score in several places — check all of the relevant sources:

GitHub:

1. PR description (body):

bash
gh pr view <PR_NUMBER> --json body -q '.body'

2. General PR comments (issue comments):

bash
gh api --paginate "repos/{owner}/{repo}/issues/<PR_NUMBER>/comments?per_page=100"

Filter for Greptile-authored comments and use the body from the most recently updated comment (updated_at), not the most recently created comment. Greptile may edit the same general PR comment on each review cycle; parse the current body, including the "Prompt to fix all with AI" section, before deciding there are no remaining issues.

3. PR reviews:

bash
gh api repos/{owner}/{repo}/pulls/<PR_NUMBER>/reviews

Look for the most recent entry from greptile-apps[bot] or greptile-apps-staging[bot].

GitLab:

1. MR description (body):

bash
glab mr view <MR_IID> --output json | jq -r '.description'

2. MR notes (comments):

bash
glab api "projects/:fullpath/merge_requests/<MR_IID>/notes"

Filter for notes from the Greptile bot user (check the author.username field — the exact username may vary per installation; verify on first run).

Perforce:

1. CL description:

bash
p4 describe -s <CL_NUMBER>

Check the description field for a Greptile-appended score block.

2. CL comments / review notes: If your installation uses a review tool such as Helix Swarm, fetch comments via its API.

Example (Swarm API): GET /api/v11/comments?topic=reviews/<REVIEW_ID>

Response fields of interest typically include:

  • user (author username)
  • body (comment text)
  • flags/state indicating whether the comment is resolved

Filter to comments authored by the Greptile bot:

  • Prefer exact username match if known
  • Otherwise, use a heuristic where the author name contains "greptile" (case-insensitive)

For all platforms, parse the text for:

  • Confidence score: a pattern like 3/5 or 5/5 (or Confidence: 3/5).
  • Comment count: Number of inline review comments noted in the summary.

Use whichever source has the most recently updated score. For GitHub, prefer updated_at from issue comments when comparing an edited Greptile summary against older review entries.

Also fetch all unresolved inline comments:

GitHub:

bash
gh api repos/{owner}/{repo}/pulls/<PR_NUMBER>/comments

Also carry forward actionable items from the latest Greptile general PR comment, especially the "Prompt to fix all with AI" section, even if the inline comment endpoint returns zero unresolved comments.

GitLab:

bash
glab api "projects/:fullpath/merge_requests/<MR_IID>/discussions"

Filter to DiffNote type discussions (notes[0].type == "DiffNote") from Greptile that are on the latest commit and not yet resolved ("resolved": false).

Perforce: If using Swarm:

Fetch inline diff comments for the review associated with the CL

GET /api/v11/comments?topic=reviews/<REVIEW_ID>

Filter to comments from the Greptile bot user that have not been marked as resolved/addressed.

C. Check exit conditions

Stop the loop if any of these are true:

  • Confidence score is 5/5 AND there are zero unresolved comments
  • --max-iterations reached (report current state)
D. Fix actionable comments

For each unresolved Greptile comment:

  1. Read the file and understand the comment in context.
  2. Determine if it's actionable (code change needed) or informational.
  3. If actionable, make the fix.
  4. If informational or a false positive, note it but still resolve the thread.
E. Resolve threads

GitHub — fetch unresolved review threads and resolve all that have been addressed (see GraphQL reference):

bash
gh api graphql -f query='
query($cursor: String) {
  repository(owner: "OWNER", name: "REPO") {
    pullRequest(number: PR_NUMBER) {
      reviewThreads(first: 100, after: $cursor) {
        pageInfo { hasNextPage endCursor }
        nodes {
          id
          isResolved
          comments(first: 1) {
            nodes { body path author { login } }
          }
        }
      }
    }
  }
}'

Resolve addressed threads:

bash
gh api graphql -f query='
mutation {
  t1: resolveReviewThread(input: {threadId: "ID1"}) { thread { isResolved } }
  t2: resolveReviewThread(input: {threadId: "ID2"}) { thread { isResolved } }
}'

GitLab — fetch unresolved discussions and resolve each one (see GitLab API reference):

bash
glab api "projects/:fullpath/merge_requests/<MR_IID>/discussions?per_page=100"

Filter for "resolved": false discussions. Then resolve each by its id:

bash
glab api --method PUT \
  "projects/:fullpath/merge_requests/<MR_IID>/discussions/<DISCUSSION_ID>" \
  --field resolved=true

Repeat for each unresolved discussion ID. (GitLab has no batch resolution — loop through each one.)

F. Commit and push / re-shelve

GitHub/GitLab:

bash
git add -A
git commit -m "address greptile review feedback (greploop iteration N)"
git push

Perforce:

bash
# Stage changes back into the CL and re-shelve for the next review round
p4 shelve -f -c <CL_NUMBER>

Wait for checks to start after push/shelve:

bash
sleep 5

Then go back to step A.

3. Report

After exiting the loop, summarize:

FieldValue
PlatformGitHub / GitLab / Perforce
IterationsN
Final confidenceX/5
Comments resolvedN
Remaining commentsN (if any)

If the loop exited due to max iterations, list any remaining unresolved comments and suggest next steps.

Output format

Greploop complete.
  Platform:      GitHub
  Iterations:    2
  Confidence:    5/5
  Resolved:      7 comments
  Remaining:     0

If not fully resolved (<N> is the effective cap — the --max-iterations value supplied, or 10 by default):

Greploop stopped after <N> iterations (--max-iterations <N>).
  Platform:      GitLab
  Confidence:    4/5
  Resolved:      12 comments
  Remaining:     2

Remaining issues:
  - src/auth.ts:45 — "Consider rate limiting this endpoint"
  - src/db.ts:112 — "Missing index on user_id column"

Perforce example:

Greploop complete.
  Platform:      Perforce
  Changelist:    12345
  Iterations:    3
  Confidence:    5/5
  Resolved:      9 comments
  Remaining:     0

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 Greploop Apps AI skill do?

Iteratively improves a PR (GitHub), MR (GitLab), or shelved changelist (Perforce) until Greptile gives it a 5/5 confidence score with zero unresolved comments. Identical to greploop, but triggers reviews by tagging @greptile-apps, which bypasses Greptile's file-count limit on huge PRs that the plain @greptile mention refuses to review. Use when the user wants to fully optimize a large PR/MR/CL against Greptile's code review standards.

Why use Greploop Apps on TypingMind?

Because you install it once and use it with any model. Greploop Apps 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 Greploop Apps in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/michaelshimeles/skills/tree/main/greploop-apps. 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 Greploop Apps?

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 Greploop Apps?

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

Is the Greploop Apps AI skill free?

Yes. It is published on GitHub by michaelshimeles under the MIT 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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