lading-optimize-find-target

lading-optimize-find-target is a skill for Claude Code, Codex from DataDog/lading. It costs 54 tokens per session (1,377 once invoked), scanned A, original, MIT.

A workflow guide for finding a suitable performance target in the Lading project. It looks for code with Criterion benchmarks, allocation data, and matching performance records.

In plain words
What is it for?
Use it before an optimization attempt to identify the source file, benchmark, technique, and performance fingerprint to work on.
Why use it?
It helps choose an optimization target based on measurable workload and past results instead of guesswork.

Skill for Claude CodeCodex

About the project

lading is a data-generation and load-testing tool that measures the performance and resource behavior of long-running programs by sending them repeatable synthetic workloads. Developers and performance engineers use it to test daemons and other complex programs across different protocols, including in Datadog Agent regression testing. The catalogue skills and instruction support working with lading.

DataDog/lading · 98 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/datadog/lading/lading-optimize-find-target
Any agent
npx skills add DataDog/lading --skill lading-optimize-find-target
Clone the repo
git clone --depth 1 https://github.com/DataDog/lading

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for lading-optimize-find-target

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadog/lading/lading-optimize-find-target.svg)](https://agentmods.dev/skills/datadog/lading/lading-optimize-find-target)
Your own site
<a href="https://agentmods.dev/skills/datadog/lading/lading-optimize-find-target"><img src="https://agentmods.dev/badge/skills/datadog/lading/lading-optimize-find-target.svg" alt="Measured on agentmods" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,377 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00054 $0.01377
Opus 5 $0.00027 $0.00688
Sonnet 5 $0.00011 $0.00275
Haiku 4.5 $0.00005 $0.00138

Measured 6d ago against content hash 6b15f4715fb8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

lading-optimize-find-target scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/lading-optimize-find-target/SKILL.md · 129 lines

How it starts

The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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_logopentelemetry/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

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

Read the full file on GitHub · 129 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 129 lines · 54 tokens per session scan A 6b15f4715fb8

Subscribe to this mod's changes

lading-optimize-find-target is a skill published in the GitHub repository DataDog/lading (98 stars, last pushed 3d ago), licensed MIT. It adds 54 tokens to every session and 1,377 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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