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Warden Sweep

  • 15 installs
  • 6.2k repo stars
  • Updated August 4, 2026
  • getsentry/xcodebuildmcp

warden-sweep scans the full repo, verifies issues, and opens draft PRs.

About

The warden-sweep skill runs phased full-repo analysis: scan every file, deep-verify findings, create issues, generate patches, and organize results under .warden/sweeps with manifest.json tracking phase state. Scripts scan.py, verify, create_issue.py, and organize.py run via uv with incremental resume skipping completed verify JSON and patches.jsonl entries. Output includes summary.md, per-finding markdown, security index, and PR links table distinguishing created versus existing PRs. Designed for batch code analysis requests like sweep the repo or find all bugs. Phases are idempotent for resume after partial runs.

  • Scans all repository files with Warden batch tooling.
  • Verifies findings via deep tracing before PR creation.
  • Creates draft PRs for validated issues only.
  • Stores structured manifest and JSONL under .warden/sweeps.
  • Supports incremental resume across scan phases.

Warden Sweep by the numbers

  • 15 all-time installs (skills.sh)
  • Ranked #778 of 1,352 Code Review & Quality skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
At a glance

warden-sweep capabilities & compatibility

Capabilities
phased scan verify issue patch organize pipeline · resuming a sweep incremental rules · output directory structure
Works with
github
Use cases
code review · security audit
Platforms
macOS · Linux
From the docs

What warden-sweep says it does

Full-repository code sweep
SKILL.md
npx skills add https://github.com/getsentry/xcodebuildmcp --skill warden-sweep

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Listed on Skillselion
Installs15
repo stars6.2k
Last updatedAugust 4, 2026
Repositorygetsentry/xcodebuildmcp

How do I run a full codebase Warden sweep?

Sweep an entire repository with Warden, verify findings, and open draft PRs for validated issues.

Who is it for?

Maintainers running periodic full-repo Warden audits.

Skip if: Skip for single-file pre-commit checks use warden skill.

When should I use this skill?

User requests full repo sweep, scan everything, or batch analysis.

What you get

Sweep summary with verified findings and created PR links.

Files

SKILL.mdMarkdownGitHub ↗

Warden Sweep

Full-repository code sweep: scan every file, verify findings with deep tracing, create draft PRs for validated issues.

Requires: warden, gh, git, jq, uv

Important: Run all scripts from the repository root using ${CLAUDE_SKILL_ROOT}. Output goes to .warden/sweeps/<run-id>/.

Bundled Scripts

scripts/scan.py

Runs setup and scan in one call: generates run ID, creates sweep dir, checks deps, creates warden label, enumerates files, runs warden per file, extracts findings.

uv run ${CLAUDE_SKILL_ROOT}/scripts/scan.py [file ...]
  --sweep-dir DIR     # Resume into existing sweep dir

scripts/index_prs.py

Fetches open warden-labeled PRs, builds file-to-PR dedup index, caches diffs for overlapping PRs.

uv run ${CLAUDE_SKILL_ROOT}/scripts/index_prs.py <sweep-dir>

scripts/create_issue.py

Creates a GitHub tracking issue summarizing sweep results. Run after verification, before patching.

uv run ${CLAUDE_SKILL_ROOT}/scripts/create_issue.py <sweep-dir>

scripts/organize.py

Tags security findings, labels security PRs, updates finding reports with PR links, posts final results to tracking issue, generates summary report, finalizes manifest.

uv run ${CLAUDE_SKILL_ROOT}/scripts/organize.py <sweep-dir>

scripts/extract_findings.py

Parses warden JSONL log files and extracts normalized findings. Called automatically by scan.py.

uv run ${CLAUDE_SKILL_ROOT}/scripts/extract_findings.py <log-path-or-directory> -o <output.jsonl>

scripts/generate_report.py

Builds summary.md and report.json from sweep data. Called automatically by organize.py.

uv run ${CLAUDE_SKILL_ROOT}/scripts/generate_report.py <sweep-dir>

scripts/find_reviewers.py

Finds top 2 git contributors for a file (last 12 months).

uv run ${CLAUDE_SKILL_ROOT}/scripts/find_reviewers.py <file-path>

Returns JSON: {"reviewers": ["user1", "user2"]}

---

Phase 1: Scan

Run (1 tool call):

uv run ${CLAUDE_SKILL_ROOT}/scripts/scan.py

To resume a partial scan:

uv run ${CLAUDE_SKILL_ROOT}/scripts/scan.py --sweep-dir .warden/sweeps/<run-id>

Parse the JSON stdout. Save runId and sweepDir for subsequent phases.

Report to user:

## Scan Complete

Scanned **{filesScanned}** files, **{filesTimedOut}** timed out, **{filesErrored}** errors.

### Findings ({totalFindings} total)

| # | Severity | Skill | File | Title |
|---|----------|-------|------|-------|
| 1 | **HIGH** | security-review | `src/db/query.ts:42` | SQL injection in query builder |
...

Render every finding from the findings array. Bold severity for high and above.

On failure: If exit code 1, show the error JSON and stop. If exit code 2, show the partial results. List timed-out files separately from errored files so users know which can be retried.

---

Phase 2: Verify

Deep-trace each finding using Task subagents to qualify or disqualify.

For each finding in `data/all-findings.jsonl`:

Check if data/verify/<finding-id>.json already exists (incrementality). If it does, skip.

Launch a Task subagent (subagent_type: "general-purpose") for each finding. Process findings in parallel batches of up to 8 to improve throughput.

Task prompt for each finding:

Read ${CLAUDE_SKILL_ROOT}/references/verify-prompt.md for the prompt template. Substitute the finding's values into the ${...} placeholders.

Process results:

Parse the JSON from the subagent response and:

  • Write result to data/verify/<finding-id>.json
  • Append to data/verified.jsonl or data/rejected.jsonl
  • For verified findings, generate findings/<finding-id>.md:
# ${TITLE}

**ID**: ${FINDING_ID} | **Severity**: ${SEVERITY} | **Confidence**: ${CONFIDENCE}
**Skill**: ${SKILL} | **File**: ${FILE_PATH}:${START_LINE}

## Description
${DESCRIPTION}

## Verification
**Verdict**: Verified (${VERIFICATION_CONFIDENCE})
**Reasoning**: ${REASONING}
**Code trace**: ${TRACE_NOTES}

## Suggested Fix
${FIX_DESCRIPTION}

${FIX_DIFF}

Update manifest: set phases.verify to "complete".

Report to user after all verifications:

## Verification Complete

**{verified}** verified, **{rejected}** rejected.

### Verified Findings

| # | Severity | Confidence | File | Title | Reasoning |
|---|----------|------------|------|-------|-----------|
| 1 | **HIGH** | high | `src/db/query.ts:42` | SQL injection in query builder | User input flows directly into... |
...

### Rejected ({rejected_count})

- `{findingId}` {file}: {reasoning}
...

---

Phase 3: Issue

Create a tracking issue that ties all PRs together and gives reviewers a single overview.

Run (1 tool call):

uv run ${CLAUDE_SKILL_ROOT}/scripts/create_issue.py ${SWEEP_DIR}

Parse the JSON stdout. Save issueUrl and issueNumber for Phase 4.

Report to user:

## Tracking Issue Created

{issueUrl}

On failure: Show the error. Continue to Phase 4 (PRs can still be created without a tracking issue).

---

Phase 4: Patch

For each verified finding, create a worktree, fix the code, and open a draft PR. Process findings sequentially (one at a time) since parallel subagents cross-contaminate worktrees.

Severity triage: Patch HIGH and above. For MEDIUM, only patch findings from bug-detection skills (e.g., code-review, security-review). Skip LOW and INFO findings.

Step 0: Setup (run once before the loop):

uv run ${CLAUDE_SKILL_ROOT}/scripts/index_prs.py ${SWEEP_DIR}

Parse the JSON stdout. Use fileIndex for dedup checks.

Determine the default branch and fetch latest so worktrees branch from current upstream:

DEFAULT_BRANCH=$(gh repo view --json defaultBranchRef --jq '.defaultBranchRef.name')
git fetch origin "${DEFAULT_BRANCH}"

For each finding in `data/verified.jsonl`:

Check if finding ID already exists in data/patches.jsonl (incrementality). If it does, skip.

Dedup check: Use the file index from index_prs.py output to determine if an existing open PR already addresses the same issue.

1. File match: Look up the finding's file path in the fileIndex. If no PR touches that file, no conflict; proceed to Step 1. 2. Chunk overlap: If a PR does touch the same file, read its cached diff from data/pr-diffs/<number>.diff and check whether the PR's changed hunks overlap with the finding's line range (startLine-endLine). Overlapping or adjacent hunks (within ~10 lines) indicate the same code region. 3. Same concern: If the hunks overlap, compare the PR title and the finding title/description. Are they fixing the same kind of defect? A PR fixing an off-by-one error and a finding about a null check in the same function are different issues; both should proceed.

Skip the finding only when there is both chunk overlap AND the PR addresses the same concern. Record it in data/patches.jsonl with "status": "existing" and "prUrl" pointing to the matching PR, then continue to the next finding.

Step 1: Create worktree

BRANCH="warden-sweep/${RUN_ID}/${FINDING_ID}"
WORKTREE="${SWEEP_DIR}/worktrees/${FINDING_ID}"
git worktree add "${WORKTREE}" -b "${BRANCH}" "origin/${DEFAULT_BRANCH}"

Each finding branches from the repo's default branch so PRs contain only the fix commit.

Step 2: Generate fix

Launch a Task subagent (subagent_type: "general-purpose") to apply the fix in the worktree. Read ${CLAUDE_SKILL_ROOT}/references/patch-prompt.md for the prompt template. Substitute the finding's values and worktree path into the ${...} placeholders.

Step 2b: Handle skipped findings

If the subagent returned "status": "skipped" (not "applied"), do NOT proceed to Steps 3-4. Instead: 1. Record the finding in data/patches.jsonl with "status": "error" and "error": "Subagent skipped: ${skipReason}" 2. Clean up the worktree 3. Continue to the next finding

Step 3: Find reviewers

uv run ${CLAUDE_SKILL_ROOT}/scripts/find_reviewers.py "${FILE_PATH}"

Step 4: Create draft PR

cd "${WORKTREE}" && git push -u origin HEAD:"${BRANCH}"

Create the PR with a 1-2 sentence "What" summary based on the finding and fix, followed by the finding description and verification reasoning:

REVIEWERS=""
# If find_reviewers.py returned reviewers, build the flags
# e.g., REVIEWERS="--reviewer user1 --reviewer user2"

gh pr create --draft \
  --label "warden" \
  --title "fix: ${TITLE}" \
  --body "$(cat <<'EOF'
${FIX_WHAT_DESCRIPTION}

${DESCRIPTION}

${REASONING}

Automated fix for Warden finding ${FINDING_ID} (${SEVERITY}, detected by ${SKILL}).

<!-- Only include the next line if Phase 3 succeeded and ISSUE_NUMBER is available -->
Ref #${ISSUE_NUMBER}

> This PR was auto-generated by a Warden Sweep (run ${RUN_ID}).
> The finding has been validated through automated deep tracing,
> but human confirmation is requested as this is batch work.
EOF
)" ${REVIEWERS}

Save the PR URL.

Step 5: Record and cleanup

Append to data/patches.jsonl (use "created" as status for successful PRs, not the subagent's "applied"):

{"findingId": "...", "prUrl": "https://...", "branch": "...", "reviewers": ["user1", "user2"], "filesChanged": ["..."], "status": "created|existing|error"}

Remove the worktree:

cd "$(git rev-parse --show-toplevel)"
git worktree remove "${WORKTREE}" --force

Error handling: On failure at any step, write to data/patches.jsonl with "status": "error" and "error": "...", clean up the worktree, and continue to the next finding.

Update manifest: set phases.patch to "complete".

Report to user after all patches:

## PRs Created

**{created}** created, **{skipped}** skipped (existing), **{failed}** failed.

| # | Finding | PR | Status |
|---|---------|-----|--------|
| 1 | `security-review-a1b2c3d4` SQL injection in query builder | #142 | created |
| 2 | `code-review-e5f6g7h8` Null pointer in handler | - | existing (#138) |
...

---

Phase 5: Organize

Run (1 tool call):

uv run ${CLAUDE_SKILL_ROOT}/scripts/organize.py ${SWEEP_DIR}

Parse the JSON stdout.

Report to user:

## Sweep Complete

| Metric | Count |
|--------|-------|
| Files scanned | {filesScanned} |
| Findings verified | {verified} |
| PRs created | {prsCreated} |
| Security findings | {securityFindings} |

Full report: `{summaryPath}`

On failure: Show the error and note which steps completed.

---

Resuming a Sweep

Each phase is incremental. To resume from where you left off:

1. Check data/manifest.json to see which phases are complete 2. For scan: pass --sweep-dir to scan.py 3. For verify: existing data/verify/<id>.json files are skipped 4. For issue: create_issue.py is idempotent (skips if issueUrl in manifest) 5. For patch: existing entries in data/patches.jsonl are skipped 6. For organize: safe to re-run (idempotent)

Output Directory Structure

.warden/sweeps/<run-id>/
  summary.md                        # Stats, key findings, PR links
  findings/                         # One markdown per verified finding
    <finding-id>.md
  security/                         # Security-specific view
    index.jsonl                     # Security findings index
    <finding-id>.md                 # Copies of security findings
  data/                             # Structured data for tooling
    manifest.json                   # Run metadata, phase state
    scan-index.jsonl                # Per-file scan tracking
    all-findings.jsonl              # Every finding from scan
    verified.jsonl                  # Findings that passed verification
    rejected.jsonl                  # Findings that failed verification
    patches.jsonl                   # Finding -> PR URL -> reviewers
    existing-prs.json               # Cached open warden PRs
    report.json                     # Machine-readable summary
    verify/                         # Individual verification results
      <finding-id>.json
    logs/                           # Warden JSONL logs per file
      <hash>.jsonl
    pr-diffs/                       # Cached PR diffs for dedup
      <number>.diff

Related skills

FAQ

What does warden-sweep do?

warden-sweep scans the full repo, verifies issues, and opens draft PRs.

When should I use warden-sweep?

User requests full repo sweep, scan everything, or batch analysis.

Is this skill safe to install?

Review the Security Audits panel on this page before installing in production.

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