Vercel Composition Patterns
supabase/supabase
React composition patterns that scale. An agent skill from supabase/supabase.
Finds a valid optimization target in lading. An agent skill from DataDog/lading.
Install Lading Optimize Find Target
$ npx skills add DataDog/lading --skill lading-optimize-find-target -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install DataDog/lading lading-optimize-find-target --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/DataDog/lading.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/lading-optimize-find-target .claude/skills/lading-optimize-find-target && rm -rf skills-srcUse ~/.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/
Install the "lading-optimize-find-target" agent skill from https://github.com/DataDog/lading/tree/main/.claude/skills/lading-optimize-find-target into .claude/skills/lading-optimize-find-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lading-optimize-find-target", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/DataDog/lading/tree/main/.claude/skills/lading-optimize-find-targetType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add DataDog/lading --skill lading-optimize-find-target -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install DataDog/lading lading-optimize-find-target --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DataDog/lading.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/lading-optimize-find-target .agents/skills/lading-optimize-find-target && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "lading-optimize-find-target" agent skill from https://github.com/DataDog/lading/tree/main/.claude/skills/lading-optimize-find-target into .agents/skills/lading-optimize-find-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lading-optimize-find-target", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add DataDog/lading --skill lading-optimize-find-target -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install DataDog/lading lading-optimize-find-target --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DataDog/lading.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/lading-optimize-find-target .cursor/skills/lading-optimize-find-target && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "lading-optimize-find-target" agent skill from https://github.com/DataDog/lading/tree/main/.claude/skills/lading-optimize-find-target into .cursor/skills/lading-optimize-find-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lading-optimize-find-target", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/DataDog/lading.git --path .claude/skills/lading-optimize-find-target--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add DataDog/lading --skill lading-optimize-find-target -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install DataDog/lading lading-optimize-find-target --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DataDog/lading.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/lading-optimize-find-target .gemini/skills/lading-optimize-find-target && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "lading-optimize-find-target" agent skill from https://github.com/DataDog/lading/tree/main/.claude/skills/lading-optimize-find-target into .gemini/skills/lading-optimize-find-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lading-optimize-find-target", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install DataDog/lading lading-optimize-find-targetInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add DataDog/lading --skill lading-optimize-find-target -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/DataDog/lading.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/lading-optimize-find-target .github/skills/lading-optimize-find-target && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "lading-optimize-find-target" agent skill from https://github.com/DataDog/lading/tree/main/.claude/skills/lading-optimize-find-target into .github/skills/lading-optimize-find-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lading-optimize-find-target", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add DataDog/lading --skill lading-optimize-find-target -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install DataDog/lading lading-optimize-find-target --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DataDog/lading.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/lading-optimize-find-target .opencode/skills/lading-optimize-find-target && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "lading-optimize-find-target" agent skill from https://github.com/DataDog/lading/tree/main/.claude/skills/lading-optimize-find-target into .opencode/skills/lading-optimize-find-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lading-optimize-find-target", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
lading-optimize-find-target- GitHub stars
- 101
- Token cost
- ~1.5k tokens
- SKILL.md length
- 556 words
- Files
- 2 (incl. scripts)
- Skills in repo
- 6
- Repo updated
- First seen
- Licence
- MIT
At a glance
Finds a valid optimization target in lading. An agent skill from DataDog/lading.
- Works in 7 steps: Discover Benchmark-Eligible Modules → Profile Allocation Intensity → Learn from Past Optimizations → …
- Development work in your project
- SKILL.md covers Phase 1: Discover…, Phase 2: Profile Allocation…, Phase 3: Learn from Past… and Phase 4: Find Opportunities, plus 3 more sections
What it does
Lading Optimize Find Target is an agent skill from DataDog/lading, published by the product's own GitHub organization. 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.
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts.
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-find-target skill to find a valid optimization target in lading. An agent skill from DataDog/lading”
- “/lading-optimize-find-target”
Requirements
- Pre-approved tools (allowed-tools): Bash, Read, Glob, Grep
Workflow steps
7 steps, taken from the step headings in SKILL.md.
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:
BashReadGlobGrep
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/, which the agent can run.
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 Find Target loads about 1.5k tokens when it runs. Until then it costs about 63 tokens; SKILL.md has 556 words of instructions outside code blocks.
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: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
allowed-tools: Bash, Read, Glob, Grep
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); the scripts in this folder are not scanned.
SKILL.md
The full file from DataDog/lading at commit a7bb0ea, republished under its MIT licence (© DataDog). 556 words, ~1,524 tokens.
Download SKILL.mdSave it as
.claude/skills/lading-optimize-find-target/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- 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.
- allowed-tools
- Bash, Read, Glob, Grep
- context
- fork
Phase 1: Discover Benchmark-Eligible Modules
A module is eligible if it has a Criterion benchmark in lading_payload/benches/.
- List benchmarks: Glob
lading_payload/benches/*.rs— each filename (minus.rs) is a bench name - 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) - Match fingerprints: Glob
ci/fingerprints/*/lading.yaml— each directory name is a fingerprint. Match fingerprints to modules by reading thevariant: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-modulesOutput 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.yamlFor 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 |
Show full SKILL.md (226 more words)Show less
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:
- Already in
.claude/skills/lading-optimize-hunt/assets/db.yaml— same function + semantically equivalent technique already exists - 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:
- Technique impact — techniques with measured history in
.claude/skills/lading-optimize-hunt/assets/db.yamlrank higher (compute avg % improvement frommeasurements.benchmarks.macro). Unknown techniques rank last. - 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>"© 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 1 other file (scripts) in .claude/skills/lading-optimize-find-target of DataDog/lading.
- SKILL.md
- scripts/profile-modules
Open the folder on GitHubat commit a7bb0ea
Compare with similar skills
Lading Optimize Find Target 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Lading Optimize Find Target this skillDataDog/lading | 101 | — | ~1.5k | Automated safety check: Notes | MIT | |
| Vercel Composition Patternssupabase/supabase | 111k | 59 repos | ~726 | Automated safety check: Pass | MIT | |
| Finishing a Development Branchobra/superpowers | 296k | 5 repos | ~1.9k | Automated safety check: Pass | MIT | |
| Typescript Advanced Typesrolling-scopes/rsschool-app | 10k | 25 repos | ~4.2k | Automated safety check: Pass | MPL-2.0 | |
| PR Babysitteropeninterpreter/openinterpreter | 69k | 3 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 5 repos | ~1.1k | Automated safety check: Pass | MIT |
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Categories
Questions about Lading Optimize Find Target
What does Lading Optimize Find Target do?
Finds a valid optimization target in lading. An agent skill from DataDog/lading. Lading Optimize Find Target is an agent skill from DataDog/lading, published by the product's own GitHub organization. Finds a valid optimization target in lading.
When should I use Lading Optimize Find Target?
Lading Optimize Find Target fits situations like: development work in your project.
How do I install Lading Optimize Find Target in Claude Code?
Run `npx skills add DataDog/lading --skill lading-optimize-find-target -a claude-code`. Or copy the skill folder (.claude/skills/lading-optimize-find-target in DataDog/lading) into .claude/skills/lading-optimize-find-target in your project. Claude Code loads it when a task matches its description.
How do I install Lading Optimize Find Target in Codex?
Run `npx skills add DataDog/lading --skill lading-optimize-find-target -a codex`. Or copy the skill folder (.claude/skills/lading-optimize-find-target in DataDog/lading) into .agents/skills/lading-optimize-find-target in your project. Codex loads it when a task matches its description.
Can I use Lading Optimize Find Target 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-find-target -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-find-target, .gemini/skills/lading-optimize-find-target, .github/skills/lading-optimize-find-target and .opencode/skills/lading-optimize-find-target in your project.
What does Lading Optimize Find Target need to run?
SKILL.md names no scripts, command-line tools or credentials: Lading Optimize Find Target is instructions for the agent only. Its frontmatter pre-approves these tools: Bash, Read, Glob, Grep.
Does Lading Optimize Find Target 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 Find Target safe to install?
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Lading Optimize Find Target use?
Lading Optimize Find Target 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 Find Target use?
About 1.5k tokens (SKILL.md is roughly 6.1k 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 Find Target?
Skills that share tags, products or a category with Lading Optimize Find Target: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 296k 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 Find Target?
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.