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.
npx agentmods add skills/datadog/lading/lading-optimize-submitnpx skills add DataDog/lading --skill lading-optimize-submitgit clone --depth 1 https://github.com/DataDog/ladingWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00033 | $0.00922 |
| Opus 5 | $0.00016 | $0.00461 |
| Sonnet 5 | $0.00007 | $0.00184 |
| Haiku 4.5 | $0.00003 | $0.00092 |
Grade A, and why
lading-optimize-submit 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimization Submit Workflow
Complete optimization workflow with git automation. This skill wraps /lading-optimize-hunt and handles:
- Git branch creation
- Git commit with formatted results
- Optional push and PR creation
Phase 0: Pre-flight
Run /lading-preflight first to ensure environment is ready.
Phase 1: Prepare Git Environment
# Ensure clean state on main
git checkout main && git pull
# Verify clean working directory
git status
STOP if working directory is dirty. Commit or stash changes before proceeding.
Phase 2: Hunt
Run /lading-optimize-hunt.
CRITICAL: After /lading-optimize-hunt completes, you MUST return here to Phase 3.
The hunt workflow will:
- Select and analyze optimization targets
- Capture baseline benchmarks
- Implement optimization
- Run basic ci/validate check
- Invoke /lading-optimize-review (which runs post-change benchmarks and judges)
The hunt will:
- Record the verdict in
.claude/skills/lading-optimize-hunt/assets/db.yamlafter review returns
BUT the hunt does NOT:
- Run post-change benchmarks (review does this)
- Make pass/fail decisions (review does this)
- Create git branches
- Commit changes
- Push to remote
- Create PRs
Those are the responsibility of THIS skill (lading-optimize-submit).
Phase 3: Create Optimization Branch
Create a new branch and add the changes.
# Create descriptive branch name
# Format: opt/<crate>-<technique>
# Examples:
# opt/payload-cache-prealloc
# opt/throttle-avoid-clone
# opt/syslog-buffer-reuse
git checkout -b opt/<crate>-<technique>
git add .
Using the template in .claude/skills/lading-optimize-submit/assets/commit-template.txt, commit the changes:
# Example:
git commit -m "opt: buffer reuse in syslog serialization
Replaced per-iteration format!() with reusable Vec<u8> buffer.
Target: lading_payload/src/syslog.rs::Syslog5424::to_bytes
Technique: buffer-reuse
Micro-benchmarks:
syslog_100MiB: +42.0% throughput (481 -> 683 MiB/s)
Macro-benchmarks (payloadtool):
Time: -14.5% (8.3 ms -> 7.1 ms)
Memory: -35.8% (6.17 MiB -> 3.96 MiB)
Allocations: -49.3% (67,688 -> 34,331)
Co-Authored-By: Claude Sonnet 4.5 <[email protected]>
"
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.
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.
- 2d ago First seen · 141 lines · 33 tokens per session scan A 4fbebf2794e0
lading-optimize-submit is a skill published in the GitHub repository DataDog/lading (98 stars, last pushed 5d ago), licensed MIT. It adds 33 tokens to every session and 922 once invoked, about $0.0002 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…