skia-analyst

name

skia-analyst

description

Analyze Skia features for SkiaSharp - produces a unified analysis of what shipped (upstream engine benefits, PR links, migration guides) and what's missing (impact/priority/effort scoring, hidden APIs). Given any combination of git refs, milestones, or no input at all, it scans upstream Skia release notes, diffs bindings, and checks C++ headers to produce a unified report. Use whenever the user asks to "write release notes", "generate changelog", "what changed between versions", "what are we missing", "feature gap analysis", "what should we bind next", "what's new in Skia", "scout features", "diff two tags", "what shipped in this release", "summarize changes since v3.x", "prepare release announcement", or any request to analyze SkiaSharp versions or Skia features. Also use proactively when the user mentions a Skia milestone bump, finishes a release cycle, or asks what went into a specific version.

Skia Analyst

You analyze Skia features for SkiaSharp from two angles simultaneously:

1. What shipped — upstream engine benefits, PR links, migration guides
2. What's missing — gap analysis with impact/priority/effort scoring, hidden API scan, action items

Every run produces both. Output is structured JSON and rendered GitHub-flavored Markdown.

This skill always runs in a SkiaSharp checkout. It uses:

• externals/skia/ submodule for the C API (our fork at mono/skia)
• binding/SkiaSharp/SkiaApi.generated.cs for the C API reflected as P/Invoke externs
• binding/SkiaSharp/*.cs for the C# wrappers
• Upstream google/skia headers fetched via GitHub for hidden API comparison

Key References

• references/schema-cheatsheet.md — Human-readable schema
• references/skia-analyst-schema.json — JSON Schema (Draft 2020-12)
• references/analysis-instructions.md — Classification criteria

Input Flexibility

The user may specify anything — infer the scan mode:

User says	Mode	What to do
Nothing, "scan everything"	full	All milestones, full gap analysis
"what's new since m133"	windowed	milestoneFrom=133, milestoneTo=current
"between m133 and m147"	windowed	milestoneFrom=133, milestoneTo=147
"diff v3.119.4..origin/main"	diff	Git diff + milestone analysis
"what changed in v4.147.0"	diff	Auto-detect previous tag, diff

If unclear, ask. But try to infer first.

Workflow

Phase 1: Setup (determine scan range, prepare sources)
Phase 2: Launch dual-model agents (Opus + GPT in parallel)
Phase 3: Synthesize — merge findings, dedupe, both lenses
Phase 4: Generate outputs (JSON → validate → Markdown)
Phase 5: Present results

Phase 1: Setup

1a. Determine scan range

Based on user input, establish:

• scanMode: full, windowed, or diff
• For windowed: milestoneFrom and milestoneTo
• For diff: refFrom and refTo (resolve SHAs, dates, commit count)

1b. Determine current milestone

cat externals/skia/include/core/SkMilestone.h

Or check commit messages for the latest Skia bump PR.

1c. Fetch Skia release notes

Fetch RELEASE_NOTES.md from google/skia using the GitHub MCP tool or gh api:

gh api repos/google/skia/contents/RELEASE_NOTES.md -H "Accept: application/vnd.github.raw" > skia-release-notes.md

Save to a file in the working directory for agents to reference.

1d. Prepare upstream headers for hidden API scan

Agents will fetch upstream C++ headers directly from GitHub during their scan:

github-mcp-server-get_file_contents owner=google repo=skia path=include/core/SkImage.h

1e. Locate binding sources

The C API is reflected in binding/SkiaSharp/SkiaApi.generated.cs as P/Invoke extern methods. The C# wrappers are in binding/SkiaSharp/*.cs. Both are in the worktree.

For the C API headers (our fork), check externals/skia/include/c/ and externals/skia/src/c/. If the submodule isn't checked out, agents can grep SkiaApi.generated.cs for sk_* and gr_* extern function names — this reflects the full C API surface.

Phase 2: Launch Dual-Model Agents

Launch two background agents simultaneously on different models:

task agent_type=general-purpose mode=background model=claude-opus-4.7 name=analyst-opus:
task agent_type=general-purpose mode=background model=gpt-5.4 name=analyst-gpt:

Each agent does the complete job independently:

1. Release notes scan — read Skia RELEASE_NOTES.md, extract features
2. Hidden API scan — fetch upstream C++ headers from google/skia, compare against binding/SkiaSharp/SkiaApi.generated.cs for P/Invoke externs and binding/SkiaSharp/*.cs for wrappers
3. Binding verification — grep the actual code to set bindingStatus
4. Git diff (if diff mode) — analyze API/build/dep changes between refs

For EVERY finding, classify with BOTH lenses:

• Changelog: changeType + importance
• Gap: bindingStatus + impact + priority + effort

Phase 3: Synthesize

When both agents complete, merge their findings:

1. Deduplicate by name/skiaApi — keep richer data from each
2. Resolve conflicts — more cautious bindingStatus wins, higher impact wins
3. Union hidden APIs — combine both agents' header scan discoveries

Phase 4: Generate Outputs

4a. Generate JSON

Save to skia-analyst-report.json in the working directory.

4b. Validate

python3 .agents/skills/skia-analyst/scripts/validate-skia-analyst.py skia-analyst-report.json

4c. Render Markdown

python3 .agents/skills/skia-analyst/scripts/render-skia-analyst.py skia-analyst-report.json skia-analyst-report.md

This produces a GitHub-flavored Markdown file with collapsible details, suitable for pasting into a GitHub issue or sharing as a gist.

Phase 5: Present Results

Show highlights inline:

• Top gaps — transformative and significant items
• Quick wins — partial binding status (C API exists, just needs C# wrapper)
• Breaking changes — if any findings have importance: breaking

Then offer next steps:

1. "Want me to investigate any finding in more detail?"
2. "Should I use the api-add-review skill to start binding a feature?"
3. "Want to upload the markdown to a gist for sharing?"
4. "Should I create a GitHub issue with the gap analysis?"