lading: Skill for Claude Code

.claude/skills/lading-optimize-hunt/SKILL.md

lading-optimize-hunt is a skill for Claude Code from DataDog/lading. It costs 26 tokens per session (1,037 once invoked), scanned D, original, MIT.

A workflow guide that coordinates and records performance optimization attempts in the Lading project. It captures a baseline, applies changes, sends them for review, and records the result.

In plain words
What is it for?
Use it to run the project’s optimization process, from preparation and target selection through review and updating the optimization history.
Why use it?
It keeps optimization work comparable and documented by measuring the original state before code changes and storing each outcome.

Skill for Claude Code ✓ vendor

Written for Claude Code: allowed-tools in frontmatter. Also seen: reads .claude/ paths.

This is DataDog/lading's own configuration. It tells Claude Code how to work on lading itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything lading configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is ./target/release/payloadtool "$PAYLOADTOOL_CONFIG" --memory-stats 2>&1 | tee /tmp/baseline-mem.txt.

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 · 101 stars · on GitHub

Reuse

Borrowing it

Nothing to install: this file belongs to DataDog/lading. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/DataDog/lading/main/.claude/skills/lading-optimize-hunt/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/DataDog/lading

Made for: Claude Code.

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-hunt

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadog/lading/lading-optimize-hunt/github.svg)](https://agentmods.dev/skills/datadog/lading/lading-optimize-hunt)
Your own site
<a href="https://agentmods.dev/skills/datadog/lading/lading-optimize-hunt"><img src="https://agentmods.dev/badge/skills/datadog/lading/lading-optimize-hunt/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for lading-optimize-hunt

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadog/lading/lading-optimize-hunt"><img src="https://agentmods.dev/badge/skills/datadog/lading/lading-optimize-hunt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,037 The whole file, excluding the scripts and references it only reads on demand.
Security scan D 2 findings. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00026 $0.01037
Opus 5.5 $0.00010 $0.00415
Sonnet 5.5 $0.00005 $0.00207
Haiku 4.5 $0.00003 $0.00104

Measured 2mo ago against content hash 026256f992fc, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade D, and why

lading-optimize-hunt scanned grade D with 2 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 2mo 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.

Unrestricted tool accessmediumExcessive agency

A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.

allowed-tools: Bash(cat:*) Bash(cargo:*) Bash(ci/*:*) Bash(hyperfine:*) Bash(*/payloadtool:*) Bash(tee:*) Read Write Edit Glob Grep Skill

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

rm -rf target/criterion
.claude/skills/lading-optimize-hunt/SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Optimization Hunt

Coordinates optimization attempts: captures baselines, implements changes, invokes review, and records all outcomes.

Role: Coordinator and Recorder

Hunt is the coordinator and recorder — it captures baselines, implements changes, hands off to review, and records all outcomes.

Hunt does NOT:

  • Run post-change benchmarks (review does this)
  • Make pass/fail decisions on optimizations (review does this)

Hunt DOES:

  • Record all verdicts and outcomes in .claude/skills/lading-optimize-hunt/assets/db.yaml after review returns

Phase 0: Pre-flight

Run /lading-preflight.


Phase 1: Find Target

Run /lading-optimize-find-target.

It returns a YAML block with 6 fields: pattern, technique, target, file, bench, fingerprint - Print it out.


Phase 2: Establish Baseline

CRITICAL: Capture baseline metrics BEFORE making any code changes.

Identify the Benchmark Target

Use the bench and fingerprint fields from find-target's output — they are repo-relative paths ready to use:

BENCH=<bench field without extension>   # e.g. from "lading_payload/benches/syslog.rs" use "--bench syslog"
PAYLOADTOOL_CONFIG=<fingerprint field>  # e.g. "ci/fingerprints/syslog/lading.yaml"

Stage 1: Clear previous benchmarks

Clear any previously captured baselines so stale data cannot contaminate this run.

rm -f /tmp/criterion-baseline.log /tmp/baseline.json /tmp/baseline-mem.txt
rm -rf target/criterion

Stage 2: Micro-benchmark Baseline

Run only the benchmark for your target:

cargo criterion --bench "$BENCH" 2>&1 | tee /tmp/criterion-baseline.log

Stage 3: Macro-benchmark Baseline

Use the matching fingerprint config:

cargo build --release --bin payloadtool
hyperfine --warmup 3 --runs 30 --export-json /tmp/baseline.json \
  "./target/release/payloadtool $PAYLOADTOOL_CONFIG"

./target/release/payloadtool "$PAYLOADTOOL_CONFIG" --memory-stats 2>&1 | tee /tmp/baseline-mem.txt

Read the full file on GitHub · 137 lines

Files

What ships with it

8 files 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. 2mo ago First seen · 137 lines · 26 tokens per session scan D 026256f992fc

Subscribe to this mod's changes

lading-optimize-hunt is a skill published in the GitHub repository DataDog/lading (101 stars, last pushed 2d ago), licensed MIT. It adds 26 tokens to every session and 1,037 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it D with 2 findings (unrestricted tool access, recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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