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Official agent skill

Lading Optimize Hunt

by DataDog in DataDog/lading

Coordinates optimization attempts. An agent skill from DataDog/lading.

OfficialMITAuto-check passedDevelopment

Install Lading Optimize Hunt

skills CLI
$ npx skills add DataDog/lading --skill lading-optimize-hunt -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install DataDog/lading lading-optimize-hunt --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/DataDog/lading.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/lading-optimize-hunt .claude/skills/lading-optimize-hunt && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
lading-optimize-hunt
GitHub stars
101
Token cost
~1k tokens
SKILL.md length
361 words
Files
9 (incl. assets)
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

Coordinates optimization attempts. An agent skill from DataDog/lading.

  • Works in 6 steps: Pre-flight → Find Target → Establish Baseline → …
  • Development work in your project
  • SKILL.md covers Role: Coordinator and Recorder, Phase 0: Pre-flight, Phase 1: Find Target and Phase 2: Establish Baseline, plus 3 more sections
  • Calls cargo

What it does

Lading Optimize Hunt is an agent skill from DataDog/lading, published by the product's own GitHub organization. Coordinates optimization attempts. Captures baselines, implements changes, invokes review, and records outcomes.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including assets (for example `README.md`, `assets/db.yaml` and `assets/db/cache-inline.yaml`).

It sits in Development. The repository describes itself as: A suite of data generation and load testing tools. The licence is MIT.

When your agent uses it

  • Development work in your project

Example prompts

  • “Use the lading-optimize-hunt skill to coordinate optimization attempts. An agent skill from DataDog/lading”
  • “/lading-optimize-hunt”

Requirements

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

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Pre-flight
  2. Find Target
  3. Establish Baseline
  4. Implement
  5. Hand Off to Review
  6. Recording

What it can do on your machine

Read from SKILL.md and the folder at commit a7bb0ea. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(cat:*)
    • Bash(cargo:*)
    • Bash(ci/*:*)
    • Bash(hyperfine:*)
    • Bash(*/payloadtool:*)
    • Bash(tee:*)
    • Read
    • Write
    • Edit
    • Glob

    …and 2 more on the same allowed-tools line.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • cargo

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Lading Optimize Hunt loads about 1k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 361 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from DataDog/lading at commit a7bb0ea, republished under its MIT licence (© DataDog). 361 words, ~1,031 tokens.

Download SKILL.mdSave it as .claude/skills/lading-optimize-hunt/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
lading-optimize-hunt
description
Coordinates optimization attempts. Captures baselines, implements changes, invokes review, and records outcomes.
allowed-tools
Bash(cat:*), Bash(cargo:*), Bash(ci/*:*), Bash(hyperfine:*), Bash(*/payloadtool:*), Bash(tee:*), Read, Write, Edit, Glob, Grep, Skill

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:

bash
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.

bash
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:

bash
cargo criterion --bench "$BENCH" 2>&1 | tee /tmp/criterion-baseline.log
Stage 3: Macro-benchmark Baseline

Use the matching fingerprint config:

bash
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

Baseline captured. These files will be consumed by review:

  • /tmp/criterion-baseline.log — micro-benchmark baseline
  • /tmp/baseline.json — macro-benchmark timing baseline
  • /tmp/baseline-mem.txt — macro-benchmark memory baseline

CRITICAL: All benchmarks must complete before continuing.

Phase 3: Implement

Make ONE change. Keep it focused and minimal.

Before proceeding, ALL changes must pass:

bash
ci/validate

No exceptions. If ci/validate fails, fix the issue before continuing.

If ci/validate repeatedly fails on a pre-existing bug (not caused by your change), document it and stop.

Show full SKILL.md (125 more words)Show less

Phase 4: Hand Off to Review

Run /lading-optimize-review with the target fields as positional arguments:

/lading-optimize-review <bench> <fingerprint> <file> <target> <technique>

Where:

  • <bench> — benchmark name from find-target's bench field, without path or extension (e.g. trace_agent)
  • <fingerprint> — repo-relative path from find-target's fingerprint field
  • <file> — repo-relative path from find-target's file field
  • <target> — function name from find-target's target field
  • <technique> — technique from find-target's technique field

It returns a YAML report. Print it out.

Phase 5: Recording

After review returns its YAML report, record the result. Every outcome MUST be recorded.

Step 1: Write the Report

Write review's YAML report verbatim to .claude/skills/lading-optimize-hunt/assets/db/<id>.yaml. Do not modify, reformat, or add to the report content — it is the authoritative record from review.

Step 2: Update the Index

Add an entry to .claude/skills/lading-optimize-hunt/assets/db.yaml following the format in .claude/skills/lading-optimize-hunt/assets/index.template.yaml.

© DataDog, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (assets) in .claude/skills/lading-optimize-hunt of DataDog/lading.

  • SKILL.md
  • README.md
  • assets/db.yaml
  • assets/db/cache-inline.yaml
  • assets/db/datadog-logs-buffer-reuse.yaml
  • assets/db/dogstatsd-buffer-reuse.yaml
  • assets/db/fluent-on-demand-serialization.yaml
  • assets/db/syslog-to_bytes-reusable-buffer.yaml
  • assets/index.template.yaml

Open the folder on GitHubat commit a7bb0ea

Compare with similar skills

Lading Optimize Hunt next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Lading Optimize Hunt compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Lading Optimize Hunt this skillDataDog/lading101—~1kAutomated safety check: PassMIT
Vercel Composition Patternssupabase/supabase111k58 repos~726Automated safety check: PassMIT
Finishing a Development Branchobra/superpowers297k5 repos~1.9kAutomated safety check: PassMIT
Typescript Advanced Typesrolling-scopes/rsschool-app10k25 repos~4.2kAutomated safety check: PassMPL-2.0
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT

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More from DataDog/lading

  • Lading Optimize Find Target

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Categories

Questions about Lading Optimize Hunt

What does Lading Optimize Hunt do?

Coordinates optimization attempts. An agent skill from DataDog/lading. Lading Optimize Hunt is an agent skill from DataDog/lading, published by the product's own GitHub organization. Coordinates optimization attempts.

When should I use Lading Optimize Hunt?

Lading Optimize Hunt fits situations like: development work in your project.

How do I install Lading Optimize Hunt in Claude Code?

Run `npx skills add DataDog/lading --skill lading-optimize-hunt -a claude-code`. Or copy the skill folder (.claude/skills/lading-optimize-hunt in DataDog/lading) into .claude/skills/lading-optimize-hunt in your project. Claude Code loads it when a task matches its description.

How do I install Lading Optimize Hunt in Codex?

Run `npx skills add DataDog/lading --skill lading-optimize-hunt -a codex`. Or copy the skill folder (.claude/skills/lading-optimize-hunt in DataDog/lading) into .agents/skills/lading-optimize-hunt in your project. Codex loads it when a task matches its description.

Can I use Lading Optimize Hunt in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add DataDog/lading --skill lading-optimize-hunt -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lading-optimize-hunt, .gemini/skills/lading-optimize-hunt, .github/skills/lading-optimize-hunt and .opencode/skills/lading-optimize-hunt in your project.

What does Lading Optimize Hunt need to run?

Going by SKILL.md and its folder, Lading Optimize Hunt needs the command-line tools its instructions call (cargo). Its frontmatter pre-approves these tools: Bash(cat:*), Bash(cargo:*), Bash(ci/*:*), Bash(hyperfine:*), Bash(*/payloadtool:*), Bash(tee:*), Read, Write, Edit, Glob, Grep, Skill.

Does Lading Optimize Hunt access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Lading Optimize Hunt safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Lading Optimize Hunt use?

Lading Optimize Hunt is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Lading Optimize Hunt use?

About 1k tokens (SKILL.md is roughly 4.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Lading Optimize Hunt?

Skills that share tags, products or a category with Lading Optimize Hunt: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 297k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Lading Optimize Hunt?

DataDog (a GitHub organization, an official publisher) maintains it in DataDog/lading, which has 101 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on September 30, 2026.

Source: DataDog/lading on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.