match-workflow
Drive symbol-to-verification matching work in crashwoc-decomp. Use when Codex needs to take a symbol or unit from lookup through context gathering, narrow local builds, and final regression checks.
Best use case
match-workflow is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Drive symbol-to-verification matching work in crashwoc-decomp. Use when Codex needs to take a symbol or unit from lookup through context gathering, narrow local builds, and final regression checks.
Teams using match-workflow should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/match-workflow/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How match-workflow Compares
| Feature / Agent | match-workflow | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Drive symbol-to-verification matching work in crashwoc-decomp. Use when Codex needs to take a symbol or unit from lookup through context gathering, narrow local builds, and final regression checks.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Match Workflow ## Standard Loop 1. Resolve the target with `python tools/ai_lookup_symbol.py ...`, `python tools/ai_lookup_unit.py ...`, or `python tools/ai_context.py ...`. 2. If the task starts from a file or unit, run `python tools/ai_match_plan.py <unit-or-path>` to list the remaining functions and pick the next target. 3. Build the context target with `ninja build/GCBE7D/src/<unit>.ctx` when you need flattened includes for decomp work. 4. Edit the source file with the smallest viable change. 5. Build the single object with `ninja build/GCBE7D/src/<unit>.o`. 6. Re-run `python tools/ai_lookup_symbol.py <symbol>`, `python tools/ai_context.py <symbol>`, or `python tools/ai_match_plan.py <unit>` to confirm the target still resolves and to inspect updated match data. 7. Run `ninja changes` if the work can affect matching or regression state. If `build/GCBE7D/baseline.json` is missing, run `ninja baseline` first. ## Practical Rules - Prefer the normalized unit name printed by the lookup helpers when duplicate basenames exist. - Use `build/GCBE7D/report.json` through `ai_context.py` when you need function-level fuzzy-match data. - For regression checks in automation, use `ninja changes` and `python tools/changes_fmt.py build/GCBE7D/report_changes.json`; avoid interactive `objdiff-cli diff` sessions. - Use `build/GCBE7D/asm/<unit>.s` when context and current source are not enough to reconstruct the exact function shape. - Keep code edits narrow and reversible. Avoid unrelated cleanup while matching. - If a function stops improving after 4 build/measure cycles, record the blocker and move to the next function instead of stalling. ## References - Read [references/verification.md](references/verification.md) for the default verification sequence and exit criteria. - Read [references/file-campaign.md](references/file-campaign.md) for file-based autonomous matching loops. - Read [references/match-style.md](references/match-style.md) for high-signal style rules that improve true matches.
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