lading-optimize-submit

An automated workflow for finding and applying code optimizations, then recording the changes in Git. Git is a system for tracking code changes; a pull request is a review request for those changes.

In plain words
What is it for?
Use it to prepare an optimization branch, run the optimization hunt and review, commit the results, and optionally push them or open a pull request.
Why use it?
It organizes benchmarking, optimization, review, commits, and optional sharing of the work, while stopping if the working directory is not clean.

Skill for Claude CodeCodex

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

Made for: Claude Code, Codex.

Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 922 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 $0.00033 $0.00922
Opus 5 $0.00016 $0.00461
Sonnet 5 $0.00007 $0.00184
Haiku 4.5 $0.00003 $0.00092

Measured 2d ago against content hash 4fbebf2794e0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

.claude/skills/lading-optimize-submit/SKILL.md · 141 lines

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.yaml after 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]>
"

Read the full file on GitHub · 141 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. 2d ago First seen · 141 lines · 33 tokens per session scan A 4fbebf2794e0

Subscribe to this mod's changes

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.

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