issue-fix

name

issue-fix

description

Fix bugs in SkiaSharp C# bindings. Structured workflow for investigating, fixing, and testing bug reports. Triggers: Crash, exception, AccessViolationException, incorrect output, wrong behavior, memory leak, disposal issues, "fails", "broken", "doesn't work", "investigate issue", "fix issue", "look at #NNNN", any GitHub issue number referencing a bug. For adding new APIs, use `api-add-review` skill instead.

Bug Fix Skill

Issue pipeline: Step 3 of 3 (Fix). See documentation/dev/issue-pipeline.md.

Fix bugs in SkiaSharp with minimal, surgical changes.

⛔ CRITICAL: SEQUENTIAL EXECUTION REQUIRED

🛑 PHASES MUST BE EXECUTED IN STRICT ORDER. NO PARALLELIZATION. NO REORDERING.

Phase 1 → Phase 2 → Phase 3 → Phase 4 → Phase 5 → Phase 6 → Phase 7 → Phase 8 → Phase 9

STOP at each phase gate. Do not proceed until gate criteria are met. Phases may be abbreviated when ai-triage/{n}.json and/or ai-repro/{n}.json exist — but you must explicitly consume them and meet the gate with evidence (don’t redo the work). NEVER say "in parallel" — phases are strictly sequential. NEVER start new research (Phase 3) before PR exists (Phase 2). NEVER use store_memory — fixes produce JSON artifacts and PRs, not memories.

---

Workflow Overview

1. Understand   → Fetch issue, consume ai-triage/ai-repro if present
2. Create PR    → 🛑 STOP: Create PR before ANY *new* research
3. Research     → Delta research (triage already did first-pass)
4. Reproduce    → Prefer ai-repro project; Docker only if needed
5. Investigate  → Root cause (guided by repro version matrix + triage codeInvestigation)
6. Fix          → Minimal change
7. Test         → Regression test + existing tests
8. Finalize     → Rewrite PR description, link all fixed issues
9. Fix JSON     → Generate, validate, and persist ai-fix/{n}.json

---

Prerequisites

• GitHub API access (fetch issues, search issues, read comments)
• Git with push access
• Local data cache worktree (docs-data-cache) for ai-triage/ai-repro handoff
• Docker for cross-platform testing (optional; check: docker --version)

---

Phase 1: Understand the Issue (pipeline intake)

1. Prefer the data cache (handoff)

pwsh --version    # Requires 7.5+

# Cache worktree
[ -d ".data-cache" ] || git worktree add .data-cache docs-data-cache
git -C .data-cache pull --rebase origin docs-data-cache
CACHE=".data-cache/repos/mono-SkiaSharp"

TRIAGE="$CACHE/ai-triage/NNNN.json"
REPRO="$CACHE/ai-repro/NNNN.json"

• If TRIAGE exists: treat it as the authoritative classification + codeInvestigation. Extract key details and uncertainties.
• If REPRO exists: treat it as the authoritative factual reproduction record (versions tested + minimal repro source).

If cache is missing the issue/JSONs, fall back to gh.

2. Extract only what you need to open the PR

Extract (from issue + triage/repro if present):

• Symptoms, error messages, stack traces
• Platform (OS, arch, .NET version, SkiaSharp version)
• Version status (reproduces on latest? on main?)
• Minimal reproduction steps / code (prefer ai-repro)

Do not redo triage’s work here. No deep code investigation and no broad related-issue search yet.

✅ GATE: Do not proceed until you have:

• Issue title, symptoms, and error message (if any)
• Target platform identified
• Noted whether ai-triage/NNNN.json exists
• Noted whether ai-repro/NNNN.json exists

⛔ AFTER PHASE 1: STOP AND CREATE PR

🛑 DO NOT search for related issues yet. DO NOT investigate yet. 🛑 Your ONLY next action is Phase 2: Create the Draft PR.

The PR must exist BEFORE any research or investigation begins.

---

Phase 2: Create Draft PR

🛑 THIS PHASE IS BLOCKING. Complete it before ANY other work.

Do NOT:

• Search for related issues (that's Phase 3)
• Read comments on other issues (that's Phase 3)
• Look at code (that's Phase 5)
• Try to reproduce (that's Phase 4)

Do ONLY:

• Create branch
• Push empty commit
• Create draft PR with template
• Add "copilot" label

git checkout -b dev/issue-NNNN-short-description
git commit --allow-empty -m "Investigating #NNNN: [description]"
git push -u origin dev/issue-NNNN-short-description
gh pr create --draft --title "Investigating #NNNN: [description]" --body "[template]"
gh pr edit --add-label "copilot"

Create PR using investigation template from references/pr-templates.md.

The PR description is your living document:

• All collected info, links, and related issues (added as you find them)
• WHY each related issue is similar (same platform? same error? same root cause?)
• Your investigation plan with checkboxes
• Progress log (add rows as you work)
• Alternatives tried (add when something doesn't work)

Update the PR description OFTEN — after every significant step.

✅ GATE: Do not proceed until you have:

• Feature branch created and pushed
• Draft PR opened with investigation template
• "copilot" label added to PR

⛔ AFTER PHASE 2: Verify PR exists before continuing

🛑 STOP. Verify the PR URL exists before proceeding to Phase 3.

Only after confirming the PR is created should you begin research.

---

Phase 3: Research Related Issues (delta)

🛑 PREREQUISITE: Phase 2 must be complete. PR must exist.

If you have not created the draft PR yet, STOP and go back to Phase 2.

If ai-triage/NNNN.json exists, it already contains:

• related issues discovered during workaround/duplicate search
• code investigation entry points
• workaround proposals and missing info

Your job in Phase 3 is delta research only:

• confirm/expand on the most relevant related issues (especially ones with diagnosis in comments)
• run additional searches only if triage confidence is low, triage is stale, or repro contradicts triage

🛑 CRITICAL: This phase often SOLVES the bug.

The community may have already diagnosed the root cause in issue comments. READ ALL COMMENTS on the most relevant related issues before investigating yourself.

Search GitHub issues for:

• Same error message (e.g., undefined symbol: uuid_generate_random)
• Same platform (e.g., Linux ARM64)
• Same SkiaSharp version
• Keywords from title

For EACH related issue found:

1. Read ALL comments (not just the issue body) — diagnosis is often in comments!
2. Note: issue number, title, WHY it's related
3. Extract: workarounds mentioned, root cause analysis, resolution if closed
4. Check for links to external issues (other projects that use SkiaSharp)

Update PR with all related issues and extracted information.

✅ GATE: Do not proceed until you have:

• Searched for related issues (at least 2-3 search queries)
• Read ALL comments on the most relevant related issues
• Updated PR with related issues and any diagnosis found

If a related issue already contains the root cause diagnosis, document it in the PR, but you MUST still proceed to Phase 4 (Reproduce) to validate the hypothesis.

---

Phase 4: Reproduce (prefer ai-repro)

⛔ REPRODUCTION IS MANDATORY.

This phase is satisfied either by:

• Re-running the minimal repro locally, OR
• Consuming an existing ai-repro/NNNN.json that already reproduced the issue on the relevant version/platform and includes the minimal repro source.

Even if you think you know the root cause from Phase 3:

• Community diagnosis could be a workaround, not the real fix
• The hypothesis could be wrong or incomplete
• You need evidence, not assumptions

If ai-repro/NNNN.json exists and conclusion is reproduced:

• Rehydrate the repro source from reproductionSteps[].filesCreated[].content into a local folder (e.g., /tmp/skiasharp/repro/NNNN/) and run it.
• Prefer this NuGet-based repro as the baseline; use Docker only if the host cannot exercise the target platform.

If no ai-repro exists:

• Reproduce using the same approach as issue-repro (standalone NuGet project first; Docker only when needed).

4.1 Target Platform Requirements

Attribute	Must Match
OS (macOS/Windows/Linux)	✅
Architecture (x64/ARM64)	✅
.NET version	✅
SkiaSharp version	✅

4.2 Docker Testing

For cross-platform testing, see references/docker-testing.md.

Example (adapt platform to match the issue):

# Replace with the platform from the issue
docker run --platform linux/arm64 -it <dotnet-sdk-image> bash

4.3 Document Results in PR

Add to PR description:

Environment	Version	Result
[Platform from issue]	[version]	❌ Crashes
[Different platform]	[version]	✅ Works

4.4 If Reproduction Fails

Try hard and exhaust all options before giving up:

1. Try different Docker base images (different Linux distros)
2. Try older/newer .NET versions
3. Try the exact SkiaSharp version AND the last known working version
4. Check if issue mentions specific hardware or configurations
5. Download and use any reproduction project attached to the issue
6. Try minimal reproduction code from the issue verbatim

Document each attempt in PR. After exhausting ALL options: ask user for details, but still proceed with code review while waiting.

✅ GATE: Do not proceed until you have:

• Either (a) re-ran the minimal repro, or (b) consumed ai-repro/NNNN.json as the baseline reproduction record
• Documented the reproduction evidence/results in the PR (including version matrix)
• If reproduction failed: documented what was tried and asked user for help

---

Phase 5: Investigate Root Cause

💡 Often already done! If Phase 3 found a diagnosis in related issue comments, this phase is just confirmation. Don't re-investigate what's already known.

For detailed debugging methodology, see documentation/dev/debugging-methodology.md.

5.1 Start with the Key Question

"Why does this work on [other platform/version] but fail here?"

The answer to this question IS the root cause. Focus your investigation on finding the difference.

5.2 For Platform-Specific Issues: Build and Compare

When a bug affects one platform but not another, build both platforms locally and compare:

# Build x64 native (in Docker)
bash ./scripts/infra/native/linux/docker/glibc/build-local.sh x64

# Build ARM64 cross-compile (in Docker)
bash ./scripts/infra/native/linux/docker/glibc/build-local.sh arm64

# Compare DT_NEEDED entries (the linked libraries)
docker run --rm -v $(pwd):/work debian:bookworm-slim bash -c \
  "apt-get update -qq && apt-get install -y -qq binutils >/dev/null && \
   echo '=== x64 ===' && readelf -d /work/externals/skia/out/linux/x64/libSkiaSharp.so.* | grep NEEDED && \
   echo && echo '=== ARM64 ===' && readelf -d /work/externals/skia/out/linux/arm64/libSkiaSharp.so.* | grep NEEDED"

If a library appears in one but not the other, investigate:

1. Does the ninja file have -lfoo for both? → Check externals/skia/out/linux/{arch}/obj/SkiaSharp.ninja
2. If yes, the linker is silently failing → Check if library exists in sysroot
3. If no, the GN configuration differs → Check native/linux/build.cake and externals/skia/gn/skia.gni

5.3 For C# Issues: Locate the Code

grep -rn "MethodName" binding/SkiaSharp/
grep -r "sk_.*methodname" binding/SkiaSharp/

5.4 Workaround vs Root Cause

⚠️ CRITICAL: Don't mistake a workaround for the root cause fix.

Example from #3369:

• Symptom: undefined symbol: uuid_generate_random on ARM64
• Wrong fix (workaround): Add -luuid to linker flags
• Root cause: fontconfig wasn't being linked at all (broken symlink in sysroot)
• Correct fix: Add the fontconfig runtime library to the cross-compile sysroot

How to tell the difference:

• If your fix adds something NEW to compensate → probably a workaround
• If your fix restores something that SHOULD have been there → probably root cause
• If x64 doesn't need it but ARM64 does → ask WHY the difference exists

5.5 Exit Criteria

Stop investigating when you can answer:

• What exact code/config causes the bug?
• Why does it fail?
• Why doesn't it fail elsewhere?
• What single change fixes it?

✅ GATE: Do not proceed until you have:

• Identified root cause
• Updated PR with root cause analysis

---

Phase 6: Fix

Principle: Minimal change. Fix only what's broken.

For guidance on specific types of fixes, see:

• Memory management - disposal patterns, ownership
• API design - same-instance returns, null checks
• Debugging methodology - native linking issues

✅ GATE: Do not proceed until you have:

• Made the fix
• Committed with descriptive message referencing issue number

---

Phase 7: Build & Test

If Modified Native Code

dotnet cake --target=externals-macos --arch=arm64   # macOS ARM64
dotnet cake --target=externals-linux --arch=arm64   # Linux ARM64 (in Docker)

If Modified C# Only

dotnet cake --target=externals-download  # If output/native/ empty

Write Regression Test

Affected Class	Test File
SKCanvas	tests/Tests/SKCanvasTest.cs
SKBitmap	tests/Tests/SKBitmapTest.cs
SKImage	tests/Tests/SKImageTest.cs
Other	Find matching *Test.cs

Name: Issue_NNNN_BriefDescription()

Run Tests

dotnet test tests/SkiaSharp.Tests.Console.sln

Tests MUST pass. Verify fix on original platform.

✅ GATE: Do not proceed until you have:

• Built successfully
• All tests pass
• Verified fix resolves the original issue (if possible)

---

Phase 8: Finalize

Rewrite PR description using final template from references/pr-templates.md.

Link ALL fixed issues (including related issues that have the same root cause):

Fixes #3369
Fixes #3272

Mark PR as ready for review (remove draft status).

✅ GATE: Complete when:

• PR description rewritten with final template
• All related issues linked with "Fixes #NNNN"
• PR marked ready for review

---

Phase 9: Generate Fix JSON

Generate structured output for the pipeline. Schema: references/fix-schema.json Examples: references/fix-examples.md

1. Generate JSON

Write to /tmp/skiasharp/fix/{timestamp}/{number}.json — use this exact literal path, do NOT substitute $TMPDIR or any other variable. {timestamp} is the current UTC time in yyyyMMdd-HHmmss format. Create the directory first with mkdir -p.

• meta: schemaVersion "1.0", number, repo, analyzedAt (ISO 8601 UTC)
• inputs: { triageFile, reproFile } — paths to upstream files consumed (if any)
• status: { value, reason } — value is one of in-progress, fixed, cannot-fix, needs-info, duplicate. reason is a required one-sentence explanation.
• summary: one-paragraph description of what was fixed and how (include root cause, fix approach, and verification outcome; minLength 20)
• rootCause: { category, area, description, confidence?, affectedFiles? } — what was wrong and why
  • category: one of logic-error, memory-safety, threading, api-misuse, dependency, upstream-skia, missing-feature, other
  • area: one of managed, binding, native, build, packaging, tests, docs
  • confidence: 0.0–1.0 (0.95+=verified, 0.80+=strong evidence, <0.80=hypothesis)
• changes: { files: [{ path, changeType, summary }], breakingChange, risk }
  • changeType: one of added, modified, removed
  • risk: one of low, medium, high
• tests: { regressionTestAdded, testsAdded?, command?, result }
  • result: one of passed, failed, not-run
  • testsAdded: [{ file, name, description? }]
• verification: { reproScenario, method, notes? } — did the repro scenario pass after the fix?
  • reproScenario: one of passed, failed, not-run, not-applicable
  • method: one of automated-test, manual-repro, visual-inspection, code-review (required)
• blockers: string array — required when status.value is cannot-fix or needs-info. Each item is one actionable blocker.
• pr: { number?, url, status } — required when status.value is fixed
• feedback: corrections to triage/repro findings (optional, see below)
• relatedIssues: other issue numbers fixed or related — required (minItems 1) when status.value is duplicate

2. Record upstream corrections

If the fix discovered that triage or repro got something wrong, record it:

"feedback": {
  "corrections": [
    {
      "source": "triage",
      "topic": "root-cause",
      "upstream": "Triage suggested native Skia bug",
      "corrected": "Actually a missing managed-side validation"
    }
  ]
}

3. Validate

# Try pwsh first, fall back to python3
pwsh .agents/skills/issue-fix/scripts/validate-fix.ps1 /tmp/skiasharp/fix/{timestamp}/{number}.json \
  || python3 .agents/skills/issue-fix/scripts/validate-fix.py /tmp/skiasharp/fix/{timestamp}/{number}.json

⚠️ NEVER use hand-rolled validation. Always use the scripts above.

4. Persist

Copy the validated JSON to output/ai/ for collection.

pwsh .agents/skills/issue-fix/scripts/persist-fix.ps1 /tmp/skiasharp/fix/{timestamp}/{number}.json

This copies the JSON to output/ai/ mirroring the data-cache structure.

---

Error Recovery

Issue	Recovery
Can't reproduce	Ask user for exact environment details
Fix causes test failures	Revert, re-analyze root cause
EntryPointNotFoundException	Rebuild natives after C API changes
Can't test on required platform	Use Docker or ask user to verify
Proposed fix is a workaround	Stop — find why it works on other platforms
Docker build uses cached layers	Use docker build --no-cache to rebuild
Linker silently skips library	See debugging-methodology.md

---

Final Checklist

Before marking complete, verify ALL gates were passed:

• Phase 1: Issue understood, key details extracted
• Phase 2: Draft PR created and used as living document
• Phase 3: Related issues searched, ALL comments read, diagnosis collected
• Phase 4: Baseline reproduction established (re-ran repro or consumed ai-repro/NNNN.json)
• Phase 5: Root cause identified and documented
• Phase 6: Minimal fix implemented
• Phase 7: Build passes, tests pass
• Phase 8: PR finalized with "Fixes #NNNN"
• Phase 9: Fix JSON generated, validated with validate-fix.ps1/.py (saw ✅), and persisted

---

Anti-Patterns

See the triage and repro anti-patterns references for the full lists. Critical rules for fix:

#0 (CRITICAL): NEVER use store_memory. Fixes produce JSON artifacts and PRs, not memories.

#1 (CRITICAL): NEVER skip the validation script. You MUST run validate-fix.ps1 (or .py fallback) and see ✅ before persisting. Mentally checking fields is not validation. If the script isn't run, the fix JSON is invalid.

#2 (CRITICAL): NEVER skip phases or reorder them. Sequential execution is required — see the ⛔ block at the top.