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-huntnpx skills add DataDog/lading --skill lading-optimize-huntgit 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-hunt)<a href="https://agentmods.dev/skills/datadog/lading/lading-optimize-hunt"><img src="https://agentmods.dev/badge/skills/datadog/lading/lading-optimize-hunt.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 | $0.00026 | $0.01037 |
| Opus 5 | $0.00013 | $0.00518 |
| Sonnet 5 | $0.00005 | $0.00207 |
| Haiku 4.5 | $0.00003 | $0.00104 |
Grade D, and why
lading-optimize-hunt scanned grade D with 2 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 4d 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(cargo:*) Bash(ci/*:*) Bash(hyperfine:*) Bash(*/payloadtool:*) Bash(tee:*) Read Write Edit Glob Grep Skill Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf target/criterion How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimization Hunt
Coordinates optimization attempts: captures baselines, implements changes, invokes review, and records all outcomes.
Role: Coordinator and Recorder
Hunt is the coordinator and recorder — it captures baselines, implements changes, hands off to review, and records all outcomes.
Hunt does NOT:
- Run post-change benchmarks (review does this)
- Make pass/fail decisions on optimizations (review does this)
Hunt DOES:
- Record all verdicts and outcomes in
.claude/skills/lading-optimize-hunt/assets/db.yamlafter review returns
Phase 0: Pre-flight
Run /lading-preflight.
Phase 1: Find Target
Run /lading-optimize-find-target.
It returns a YAML block with 6 fields: pattern, technique, target, file, bench, fingerprint - Print it out.
Phase 2: Establish Baseline
CRITICAL: Capture baseline metrics BEFORE making any code changes.
Identify the Benchmark Target
Use the bench and fingerprint fields from find-target's output — they are repo-relative paths ready to use:
BENCH=<bench field without extension> # e.g. from "lading_payload/benches/syslog.rs" use "--bench syslog"
PAYLOADTOOL_CONFIG=<fingerprint field> # e.g. "ci/fingerprints/syslog/lading.yaml"
Stage 1: Clear previous benchmarks
Clear any previously captured baselines so stale data cannot contaminate this run.
rm -f /tmp/criterion-baseline.log /tmp/baseline.json /tmp/baseline-mem.txt
rm -rf target/criterion
Stage 2: Micro-benchmark Baseline
Run only the benchmark for your target:
cargo criterion --bench "$BENCH" 2>&1 | tee /tmp/criterion-baseline.log
Stage 3: Macro-benchmark Baseline
Use the matching fingerprint config:
cargo build --release --bin payloadtool
hyperfine --warmup 3 --runs 30 --export-json /tmp/baseline.json \
"./target/release/payloadtool $PAYLOADTOOL_CONFIG"
./target/release/payloadtool "$PAYLOADTOOL_CONFIG" --memory-stats 2>&1 | tee /tmp/baseline-mem.txt
What ships with it
8 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.
- assets/db.yaml 1.1 KB
- assets/db/cache-inline.yaml 2.1 KB
- assets/db/datadog-logs-buffer-reuse.yaml 1.2 KB
- assets/db/dogstatsd-buffer-reuse.yaml 1.5 KB
- assets/db/fluent-on-demand-serialization.yaml 3.6 KB
- assets/db/syslog-to_bytes-reusable-buffer.yaml 2.4 KB
- assets/index.template.yaml 186 B
- README.md 15 KB
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.
- 4d ago First seen · 137 lines · 26 tokens per session scan D 026256f992fc
lading-optimize-hunt is a skill published in the GitHub repository DataDog/lading (98 stars, last pushed 6d ago), licensed MIT. It adds 26 tokens to every session and 1,037 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it D with 2 findings (unrestricted tool access, recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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