lading-optimize-find-target

name	lading-optimize-find-target
description	Finds a valid optimization target in lading. Returns a filled target.yaml template with pattern, technique, target, file, bench, and fingerprint. Use before /lading-optimize-hunt or when selecting a new optimization target. Use when this capability is needed.
metadata	{"author":"DataDog"}

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.

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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.

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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.

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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

Name	Pattern	Technique
vec-with-capacity	Vec::new() + repeated push	Vec::with_capacity(n)
string-with-capacity	String::new() + repeated push	String::with_capacity(n)
map-with-capacity	FxHashMap::default() hot insert	FxHashMap::with_capacity(n)
buffer-reuse	format!() in hot loop	write!() to reused buffer
slice-params	&Vec<T> or &String parameter	&[T] or &str slice
hoist-allocation	Allocation in hot loop	Move allocation outside loop
object-pool	Repeated temp allocations	Object pool / buffer reuse
borrow-not-clone	Clone where borrow works	Use reference
inline	Hot cross-crate fn call	#[inline] attribute
lazy-iterators	Intermediate .collect() calls	Iterator chains without collect
box-large-structs	Large struct by value	Box or reference
bounded-buffer	Unbounded growth	Bounded buffer with .clear()
scratch-buffer	encode_to_vec() per call	Reusable BytesMut scratch buffer
on-demand-serialization	Deep clone of template in loop	Incremental mutation / COW

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)

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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."

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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.

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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>"

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Source: DataDog/lading — distributed by TomeVault.