Phase 1: Discover Benchmark-Eligible Modules

A module is eligible if it has a Criterion benchmark in lading_payload/benches/.

1. List benchmarks: Glob lading_payload/benches/*.rs — each filename (minus .rs) is a bench name
2. Resolve sources: For each bench name, find the corresponding source file(s) under lading_payload/src/. Check single-file modules ({name}.rs), directory modules ({name}/), and parent-module patterns (e.g., opentelemetry_log → opentelemetry/log.rs)
3. Match fingerprints: Glob ci/fingerprints/*/lading.yaml — each directory name is a fingerprint. Match fingerprints to modules by reading the variant: key from each config

The result is a set of (bench, source_files, fingerprint_or_none) triples.

Phase 2: Profile Allocation Intensity

Run the profiling script:

.claude/skills/lading-optimize-find-target/scripts/profile-modules

Output is TSV: module, allocations, total_bytes, peak_live_bytes. Record per-module.

Phase 3: Learn from Past Optimizations

Read the optimization history to understand what techniques work and what's already done:

Read .claude/skills/lading-optimize-hunt/assets/db.yaml

For each entry, read its detail file (file: field, relative to .claude/skills/lading-optimize-hunt/) to extract:

• Technique and measurements — which techniques yielded what % improvements
• Lessons — what patterns were optimized and what the before/after looked like
• Targets already covered — so you skip them

This history teaches you what to look for. Successful past techniques are strong signals for where to look next. The lessons field often suggests next targets explicitly.

Phase 4: Find Opportunities

Scan every benchmark-eligible source module for every known pattern below. This is an exhaustive cross-product — do not short-circuit after finding one hit.

Known Patterns

Procedure (must follow exactly)

Step 1 — Read source files. For each module's source file(s), Read the full file (excluding #[cfg(test)] blocks). Understand the data flow: what structs exist, how serialization works, where the hot path is, and what allocations occur.

Step 2 — Identify patterns. For each module, check whether any of the Known Patterns above apply. Record a hit matrix:

module × pattern → match count (0 = no hit)

Show the full matrix as a table. Every cell must have a value. Do NOT skip any combination.

Step 3 — Record opportunities. Each verified hot-path hit becomes an opportunity: (pattern, technique, target_function, file, module, allocations_from_profiling)

Phase 5: Filter

Remove any opportunity that:

1. Already in .claude/skills/lading-optimize-hunt/assets/db.yaml — same function + semantically equivalent technique already exists
2. No benchmark — module has no matching bench file in lading_payload/benches/

If zero survive, STOP: "No valid optimization targets found."

Phase 6: Rank

Sort by two dimensions:

1. Technique impact — techniques with measured history in .claude/skills/lading-optimize-hunt/assets/db.yaml rank higher (compute avg % improvement from measurements.benchmarks.macro). Unknown techniques rank last.
2. Allocation intensity — modules with higher allocation counts from profiling rank higher.

Sort by technique impact first, allocation intensity second. Tiebreaker: Prefer modules with no prior .claude/skills/lading-optimize-hunt/assets/db.yaml entries, then alphabetical file name.

Show sorted results in a table.

Phase 7: Return Result

Pick the top-ranked opportunity. Return as a fenced YAML code block. Do NOT write to disk. Do not include intermediate tables, matrices, or analysis.

pattern: "<description of the code pattern found>"
technique: "<pattern name / optimization technique to apply>"
target: "<Module::function>"
file: "<relative path to source file>"
bench: "<relative path to Criterion benchmark .rs file>"
fingerprint: "<relative path to fingerprint lading.yaml config, or null>"