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.
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.
curl -O https://raw.githubusercontent.com/DataDog/lading/main/.claude/skills/lading-optimize-review/SKILL.mdgit clone --depth 1 https://github.com/DataDog/ladingWrote 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.
[](https://agentmods.dev/skills/datadog/lading/lading-optimize-review)<a href="https://agentmods.dev/skills/datadog/lading/lading-optimize-review"><img src="https://agentmods.dev/badge/skills/datadog/lading/lading-optimize-review/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.
<a href="https://agentmods.dev/skills/datadog/lading/lading-optimize-review"><img src="https://agentmods.dev/badge/skills/datadog/lading/lading-optimize-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 5 Skill grants unrestricted tool access without appropriate constraints. An agent with unfettered tool access can perform arbitrary actions including file modification, network requests, and code execution.Fix: Restrict tool access to only the tools required for the skill's stated purpose. Use an explicit allowlist rather than granting blanket access.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.01909 |
| Opus 5.5 | $0.00010 | $0.00764 |
| Sonnet 5.5 | $0.00005 | $0.00382 |
| Haiku 4.5 | $0.00003 | $0.00191 |
Grade B, and why
lading-optimize-review scanned grade B with 1 finding 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(sample:*) Bash(samply:*) Bash(cargo:*) Bash(ci/*:*) Bash(hyperfine:*) Bash(*/payloadtool:*) Bash(tee:*) Read Glob Grep How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimization Patch Review
A rigorous 5-persona peer review system for optimization patches in lading. Requires unanimous approval backed by concrete benchmark data. Duplicate Hunter persona prevents redundant work.
Role: Judge
Review is the decision-maker. It does NOT record results.
Review judges using benchmarks and 5-persona review, then returns a structured report.
Outcomes
| Outcome | Votes | Action |
|---|---|---|
| APPROVED | 5/5 APPROVE | Return APPROVED report |
| REJECTED | Any REJECT | Return REJECTED report |
Arguments
This skill requires 5 positional arguments passed by the caller:
| Arg | Field | Example | Used for |
|---|---|---|---|
$ARGUMENTS[0] |
bench | trace_agent |
cargo criterion --bench flag |
$ARGUMENTS[1] |
fingerprint | ci/fingerprints/trace_agent_v04/lading.yaml |
payloadtool config path |
$ARGUMENTS[2] |
file | lading_payload/src/trace_agent/v04.rs |
report + duplicate check |
$ARGUMENTS[3] |
target | V04::to_bytes |
report |
$ARGUMENTS[4] |
technique | buffer-reuse |
report + duplicate check |
If any argument is missing -> REJECT. All 5 are required.
Generate Report ID
Derive the id from the file and technique arguments:
- Take the filename stem from
$ARGUMENTS[2](e.g.lading_payload/src/trace_agent/v04.rs→trace-agent-v04) - Append the technique
$ARGUMENTS[4](e.g.buffer-reuse) - Join with
-→trace-agent-v04-buffer-reuse
Use this id in the report.
Phase 1: Benchmark Execution
Step 1: Read Baseline Data
Read the baseline benchmark files captured:
/tmp/criterion-baseline.log— micro-benchmark baseline/tmp/baseline.json— macro-benchmark timing baseline/tmp/baseline-mem.txt— macro-benchmark memory baseline
If baseline data is missing -> REJECT. Baselines must be captured before any code change and before this gets invoked.
Step 2: Run Post-Change Micro-benchmarks
What ships with it
2 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.
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.
- 2mo ago First seen · 203 lines · 26 tokens per session scan B 6a24944dc121
lading-optimize-review 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 1,909 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 1 finding (unrestricted tool access). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
autoreview
Structured code review through the shared OpenClaw agent-skills installation.
omh-code-review
This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
re0-merge
Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…