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H Mmer/Status — measured AI agent skill | DecimalAI

h-mmer/status

NewSkillScoreMaintained

Show engagement dashboard with program info, scope, brain state, findings, agent activity, and cost estimate.

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-status

Get this skill — curl · raw file

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

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

22 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

—+13% tokens0% turns23% of whole cases pass with skill (5/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-06	✗→✓	▲ Improved	-18%	0%
case-07	✗→✓	▲ Improved	-51%	0%
case-17	✗→✓	▲ Improved	-38%	0%
case-08	✓→✗	▼ Worse	-79%	0%
case-11	✓→✗	▼ Worse	-77%	0%

Showing 5 of 22 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/status/SKILL.md

View on GitHub ↗

Imported 8/1/2026Version v1

Skill contents

SKILL.md

Show the engagement status dashboard.

Run uv run python3 ../../tools/statusline.py to display the full dashboard.

After displaying, offer relevant next actions based on the state:

If no scope: suggest /sync
If no brain: suggest /brain init
If no findings: suggest /quickscan or /fullscan
If findings but no reports: suggest /report
If exhausted vectors are high: suggest focusing on untested areas

Top-Tier Dashboard Bar

Status should drive the next decision.

Always surface:

highest-value untested target
strongest confirmed finding not yet reported
chain-pending feeders
stale recon or scope data
blockers requiring user action
cost burn versus validated output
evidence gaps before submission

End with exactly three recommended next actions ordered by expected value. Avoid generic "continue hunting" unless the target and vuln class are named.

Want to see if h-mmer/status 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.