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H Mmer/Cost — measured AI agent skill | DecimalAI

h-mmer/cost

NewSkillScoreMaintained

Show cost tracking and ROI for this engagement.

by h-mmer

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

Security & AppSec pack

decimalai skills pull h-mmer-cost

Get this skill — curl · raw file

curl -fsSL https://app.decimal.ai/s/h-mmer-cost/SKILL.md

Raw file: /s/h-mmer-cost/SKILL.md ↗ · machine-readable summary at /s/h-mmer-cost.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

23 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 7d ago

—+30% tokens0% turns39% of whole cases pass with skill (9/23)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-04	✗→✓	▲ Improved	-1%	0%
case-05	✗→✓	▲ Improved	14%	0%
case-07	✗→✓	▲ Improved	80%	0%
case-18	✗→✓	▲ Improved	49%	0%
case-19	✗→✓	▲ Improved	145%	0%

Showing 5 of 23 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/1/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

H-mmer / pentest-agents

providers/codex/.agents/skills/cost/SKILL.md

View on GitHub ↗

Imported 8/1/2026Version v1

Skill contents

SKILL.md

Show engagement cost data.

Run these in sequence:

uv run python3 ../../tools/cost.py summary
uv run python3 ../../tools/cost.py roi
Provide analysis: which agents consumed the most, whether the spend is justified by findings.

Top-Tier ROI Review

Cost review is a hunting control, not accounting trivia.

Segment spend by phase: recon, ranking, hunting, validation, reporting, rework.
Flag any agent class with high spend and low durable output: no new surface, no killed hypotheses, no confirmed evidence.
Compare spend to expected value: bounty range, report probability, duplicate risk, and remaining proof work.
Recommend one concrete budget move: continue, pivot class, reduce parallelism, raise min-score, skip noisy targets, or run a focused chain pass.
Treat repeated false positives as a training signal. Link the spend spike to /learn, /brain, or rules updates.

Want to see if h-mmer/cost would activate on your phrasing? Try it before you install — no LLM call, no signup.

Other measured skills in the registry, with their headline benchmark lift.