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.
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-find-targetnpx skills add DataDog/lading --skill lading-optimize-find-targetgit 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-find-target)<a href="https://agentmods.dev/skills/datadog/lading/lading-optimize-find-target"><img src="https://agentmods.dev/badge/skills/datadog/lading/lading-optimize-find-target.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00054 | $0.01377 |
| Opus 5 | $0.00027 | $0.00688 |
| Sonnet 5 | $0.00011 | $0.00275 |
| Haiku 4.5 | $0.00005 | $0.00138 |
Grade A, and why
lading-optimize-find-target 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 6d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-modules
Output 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.yaml
For 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 |
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.
- 6d ago First seen · 129 lines · 54 tokens per session scan A 6b15f4715fb8
lading-optimize-find-target is a skill published in the GitHub repository DataDog/lading (98 stars, last pushed 3d ago), licensed MIT. It adds 54 tokens to every session and 1,377 once invoked, about $0.0003 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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