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Get Started Free →Run a force-robust Stormbreaker loop — route to real agents, execute a verified pipeline to completion.
.claude/skills/agentlas-ai-hep-storm/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | 118% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 105% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 151% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 125% | 0% |
Update fallback: 자동 업데이트가 안 되면 hephaestus update를 한 번 실행하세요. 업데이트하지 않아도 현재 버전 명령은 그대로 동작합니다.
Raw arguments: everything the user typed after /skill:hep-storm.
Codex plugins cannot register slash commands, so this custom prompt is the explicit entrypoint (/prompts:hep-storm). The same contract is also available implicitly via the hep-storm skill. Also triggered by @Hephaestus storm <goal>.
Drive a goal through the Stormbreaker Loop — Hephaestus' force-robust, verifier-first execution loop. Unlike a one-shot answer or a generic parallel fan-out, Stormbreaker routes the goal to real Agentlas specialists, structures the work as a dependency-ordered pipeline fabric, drives each work packet as a hardened goal loop (it does not stall, run away, or claim false success), and refuses to report success without evidence. In an agentic runtime you are the executor — the engine gives you the verified plan; you carry it out with your own tools.
Use it for loop-worthy work: apps, sites, agents, automations, debugging, multi-step research, data/report generation — anything with files, tools, tests, or external verification. Trivial questions should be answered directly, not stormed.
Every result includes execution_harness. Apply execution_harness.system_prompt verbatim before planning or executing any packet, and retain its prompt_sha256 in the goal ledger. Do not redefine, summarize, or replace Goal mode or UltraCode mode in this Codex adapter. If live session JSON is available, expose it as AGENTLAS_SESSION_INVENTORY; otherwise use Core's explicit host:primary fallback and do not invent workers or models. With no external executor, status: materialized plus final_gate.can_report_success: false is the expected handoff to Codex's native tools, never a completed run.
Resolve the runner — first executable wins; runtime cache fallback:
bashRUNNER="" for c in \ "$HOME/.agentlas/runtime/current/bin/hephaestus" \ ./bin/hephaestus do [ -x "$c" ] && RUNNER="$c" && break; done if [ -z "$RUNNER" ]; then for cache in \ "${CODEX_HOME:-$HOME/.codex}/plugins/cache/agentlas-core-engine/hephaestus" \ "$HOME/.claude/plugins/cache/agentlas-core-engine/hephaestus"; do newest="$(ls -d "$cache"/*/bin/hephaestus 2>/dev/null | sort -V | tail -1)" [ -n "$newest" ] && [ -x "$newest" ] && RUNNER="$newest" && break done fi [ -n "$RUNNER" ] || { echo "Hephaestus runtime not found. Run the installer first." >&2; exit 1; } if [ "${HEPHAESTUS_AUTH_AUTOPOPUP:-1}" != "0" ]; then "$RUNNER" auth ensure --timeout 180 >/dev/null 2>&1 || true fi # Route + materialize the pipeline fabric for THIS goal. No --executor-command: # the host model (you) executes each packet natively. --research-evidence grounds # plan/research packets with Research Engine receipts. FABRIC="$("$RUNNER" hep-storm "$ARGUMENTS" --research-evidence --runtime codex)" printf '%s\n' "$FABRIC"
Read route_decision.action (or route_action) and branch — Stormbreaker only auto-materializes a full fabric for a pipeline; other actions still start a storm, just with the workforce the router chose:
pipeline — the result carries the execution_fabric (packets,parallel_groups, sessions, resume_policy), per-packet write_scope and goal/verifier, a pipeline_id, a journal path, and final_gate criteria. Run the full loop in §2.
clarify — the goal is ambiguous. Ask clarify_question with the candidatelist as ONE batch, then re-run "$RUNNER" hep-storm "<refined goal>". This is the scope-lock ambiguity gate; do not guess past it.
route (single card) — a one-agent storm: borrow and run that card attachedto this project, then still apply the verify → repair → final-gate steps.
hub_fallback / hub_candidates — Hub lookup used redacted keywords only.If an execution block lists recommended_agents, borrow each in stage order via "$RUNNER" hep-call "<agent>" "<goal>" --project . and run them attached to this repo; otherwise report candidates and offer /skill:hep-build.
propose_new — no fit exists; offer to build one via /skill:hep-build.refuse — explain reasons (e.g. loop guard) and stop. Do not retry aroundit.
Execute the goal to completion under this protocol. Do not stop to ask for confirmation — this is a force-robust run. Only halt when the goal is verified, or you are genuinely blocked by auth, payment, policy, a missing secret/tool, or a required user approval.
route decision's failure-memory. If (and only if) the goal is too ambiguous to decompose safely, ask ONE batch of 3–5 questions (what NOT to do / smallest version / done signal / dependencies), then proceed. If it is already specific, ask nothing.
done-signal for each packet and for the whole goal. These become the loop's stop criteria.
parallel_groups and depends_on orderingas the plan. Open a visible goal ledger: packet, owner, verification gate, status, resume point.
group concurrently where this runtime supports delegation. When a packet's card names an Agentlas specialist, borrow and run it attached to this project via "$RUNNER" hep-call "<card>" "<goal>" --project . rather than role-playing it. Write artifacts to each packet's write_scope.
verification. A loop.goal_command exiting 0 may end the goal loop, but it cannot verify the executor's own output or self-attest success. Require an independent verifier and hash-validated artifact evidence. "It ran" is never success.
that packet — bounded. Honor the goal-loop budget: tolerate transient failures with backoff, stop a packet as stalled after consecutive no-progress iterations, and never exceed its iteration ceiling. A resumed run continues from the journal, it does not restart.
(can_report_success). Run the evidence/security gate on produced artifacts.
Keep visible progress concise: what was attempted, what was verified, and exactly where to resume if blocked. Never expose hidden reasoning — show progress, evidence, decisions, and final status only.
with backoff, not fatal. Only a genuine streak of hard failures stops the run.
no-progress stops the loop as stalled instead of spinning.
reached_goal onlywhen the verifier proves it. No bare "it ran" ever counts.
its numbering from the journal instead of colliding or restarting from zero.
session is missing, or a gate did not run, report the run as blocked or unverified with the exact next step — never as complete. A scheduled or materialized run is not proof that an external action succeeded.
follows this runtime's own safety and permission model.
receipt_id, pipeline_id, and journal path in your final messageso the run is auditable and resumable.
text/prompts:hep-storm ship a working waitlist landing page with a verified signup flow /prompts:hep-storm 이 리포 결제 버그를 재현 PoC까지 만들어서 고치고 회귀 테스트로 검증해줘 @Hephaestus storm turn this research question into a cited report with evidence
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 13,934 | 19,406 | +39% | 1 | 1 | 0% | 519 | 3,100 | +497% | 0 | 0 | — |
case-02 | fail→fail | 14,839 | 17,314 | +17% | 1 | 1 | 0% | 171 | 2,984 | +1645% | 0 | 0 | — |
case-03 | fail→fail | 55,980 | 14,976 | -73% | 1 | 1 | 0% | 9,040 | 3,098 | -66% | 0 | 0 | — |
case-04 | pass→pass | 9,312 | 4,722 | -49% | 1 | 1 | 0% | 1,392 | 3,067 | +120% | 0 | 0 | — |
case-05 | pass→pass | 4,268 | 3,713 | -13% | 1 | 1 | 0% | 525 | 2,687 | +412% | 0 | 0 | — |
case-06 | pass→pass | 10,468 | 7,054 | -33% | 1 | 1 | 0% | 1,687 | 3,174 | +88% | 0 | 0 | — |
case-07 | fail→fail | 13,432 | 7,012 | -48% | 1 | 1 | 0% | 1,870 | 3,200 | +71% | 0 | 0 | — |
case-08 | fail→pass | 11,074 | 6,834 | -38% | 1 | 1 | 0% | 1,483 | 3,235 | +118% | 0 | 0 | — |
case-09 | fail→pass | 11,462 | 8,273 | -28% | 1 | 1 | 0% | 1,672 | 3,425 | +105% | 0 | 0 | — |
case-10 | fail→pass | 8,236 | 6,503 | -21% | 1 | 1 | 0% | 1,251 | 3,145 | +151% | 0 | 0 | — |
case-11 | fail→pass | 37,098 | 9,389 | -75% | 1 | 1 | 0% | 3,282 | 3,846 | +17% | 0 | 0 | — |
case-12 | fail→pass | 8,486 | 4,212 | -50% | 1 | 1 | 0% | 1,310 | 2,943 | +125% | 0 | 0 | — |
case-13 | fail→pass | 13,322 | 4,137 | -69% | 1 | 1 | 0% | 1,908 | 2,679 | +40% | 0 | 0 | — |
case-14 | fail→pass | 10,766 | 3,078 | -71% | 1 | 1 | 0% | 1,429 | 2,580 | +81% | 0 | 0 | — |
case-15 | fail→fail | 13,840 | 5,670 | -59% | 1 | 1 | 0% | 1,219 | 3,135 | +157% | 0 | 0 | — |
case-16 | pass→pass | 17,044 | 12,789 | -25% | 1 | 1 | 0% | 2,416 | 3,843 | +59% | 0 | 0 | — |
case-17 | fail→pass | 9,871 | 12,592 | +28% | 1 | 1 | 0% | 1,347 | 3,154 | +134% | 0 | 0 | — |
case-18 | pass→pass | 16,014 | 4,827 | -70% | 1 | 1 | 0% | 1,857 | 2,904 | +56% | 0 | 0 | — |
case-19 | pass→pass | 13,639 | 5,336 | -61% | 1 | 1 | 0% | 1,582 | 3,096 | +96% | 0 | 0 | — |
case-20 | fail→pass | 15,300 | 6,865 | -55% | 1 | 1 | 0% | 2,156 | 3,181 | +48% | 0 | 0 | — |
case-21 | pass→fail | 11,658 | 6,105 | -48% | 1 | 1 | 0% | 1,541 | 3,222 | +109% | 0 | 0 | — |
case-22 | fail→pass | 15,079 | 4,473 | -70% | 1 | 1 | 0% | 2,438 | 2,816 | +16% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 19 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +41 percentage points is the difference between those two pass rates over the 19 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.