Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Staff a task from registered Local, owner Cloud, and public Hub agents.
.claude/skills/agentlas-ai-hep-network/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | 198% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -20% | 0% |
Update fallback: 자동 업데이트가 안 되면 hephaestus update를 한 번 실행하세요. 업데이트하지 않아도 현재 버전 명령은 그대로 동작합니다.
Raw request: $ARGUMENTS
Act as the active top-level workforce orchestrator. Use MCP server hephaestus-network, the local Agentlas OS Core and only host-visible Workforce MCP. Core reaches Cloud and Hub through its internal upstream client. Network means registered Local + signed-in owner Cloud + public Hub.
The user does not need to say goal. First read workforce.goal_context(projectDir) and reuse an active binding for the same ongoing work before considering recruitment.
Before the first Cloud or Hub source call, reuse the installed Agentlas sign-in. Resolve the runner only for authentication; staffing remains in the Workforce MCP tools:
bashRUNNER="" for candidate in \ "$HOME/.agentlas/runtime/current/bin/hephaestus" \ "./bin/hephaestus" do if [ -n "$candidate" ] && [ -x "$candidate" ]; then RUNNER="$candidate"; break; fi done [ -n "$RUNNER" ] && "$RUNNER" auth ensure --timeout 180 >/dev/null 2>&1 || true
agentlas.workforce-work-order.v1 with substantive roleslots. Fill a slot with task/cardinality/criticality plus only the communities/skills/knowledge, runtimes, and languages that genuinely constrain the hire; omit every other list field (absent = empty — the wire normalizes) and never fill requiredToolCapabilities, requiredAuthorities, forbiddenAuthorities, consumes, produces, requiredRoles, or modalities: tools, authorities, and modalities attach to the executing runtime, not the agent card, so those gates only exclude real candidates — put ordinary inputs/outputs in the task text and handoffs in edges. Private grounding stays local. Write every discovery-facing field in English, faithfully translating a non-English request (the candidate corpus is English and cross-lingual matching buries the correct agent — measured 1st vs 144th for one query); keep an untranslatable term with a short English gloss. languages is the delivery language, not the search language — keep it as the required output language even though the order is authored in English.
workforce.search_candidates with{workOrder, sourceScope: "network"} and preserve source receipts plus selectionSessionId. The default response is a projected menu, not a complete federationResult; do not echo it as one. Unavailable sources remain explicit.
agentlas.workforce-selection.v1 yourself. Call workforce.validate_selection with {workOrder, selection} and keep its response as federatedSelection. Revise on rejection. Deterministic code may enforce governance but may not pick, rerank, or silently substitute.
workforce.prepare_execution with{workOrder, selection, federatedSelection, projectDir, goalId?} and require exact source, release, package/content, runtime-bundle, permission, and context pins for every selected row. projectDir is mandatory; pass the incumbent goalId when continuing. Otherwise Core derives it from the WorkOrder id and automatically binds the successful plan before execution.
workforce.goal_context, reuses the incumbent rosterplus local skills when sufficient, and recruits only a real additive gap using the same goalId. Record the turn posture through workforce.record_goal_turn.
model.resolve_allocation with that inventory plus the host-owned stage: planner/manager-plan, worker, manager-synthesis/synthesis, or verifier. Use the receipt's exact provider, model, and effort for that invocation. Model pins and ceilings come only from the MCP server's operator policy, never from the task or tool arguments. A missing worker policy inherits orchestrator; orchestrator never falls through to worker.
invocations with explicit artifact handoffs; preserve authoritative Team graphs. Allocation receipts have usage: null before execution, so record actual usage on the later invocation/run receipt instead of inventing zero.
Keep the roster bound across turns, sessions, runtime restarts, and context compaction until explicit whole-goal completion/cancellation via workforce.complete_goal(explicitCompletion=true). Lease expiry affects only the next Hub charge; it never dismisses the roster. Standby is durable availability, not a continuously running model. Memory/Experience accrue on actual invocations.
Report executed only from a receipt proving every child invocation, handoff, synthesis, and a passing independent verifier. Otherwise report the last truthful state. Do not call legacy hephaestus_route, bypass Core with direct remote search, or use popularity/history/price/availability as semantic fit. Exact duplicate releases collapse Local > Cloud > Hub only with verified identical lineage.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 5,944 | 7,745 | +30% | 1 | 1 | 0% | 220 | 1,493 | +579% | 0 | 0 | — |
case-02 | fail→fail | 10,437 | 7,456 | -29% | 1 | 1 | 0% | 830 | 1,513 | +82% | 0 | 0 | — |
case-03 | fail→fail | 18,017 | 21,951 | +22% | 1 | 1 | 0% | 990 | 2,396 | +142% | 0 | 0 | — |
case-04 | fail→fail | 6,101 | 8,069 | +32% | 1 | 1 | 0% | 226 | 1,579 | +599% | 0 | 0 | — |
case-05 | pass→fail | 5,844 | 16,441 | +181% | 1 | 1 | 0% | 1,018 | 1,683 | +65% | 0 | 0 | — |
case-06 | pass→fail | 8,294 | 36,592 | +341% | 1 | 1 | 0% | 1,505 | 9,620 | +539% | 0 | 0 | — |
case-07 | fail→fail | 9,115 | 43,453 | +377% | 1 | 1 | 0% | 1,654 | 9,811 | +493% | 0 | 0 | — |
case-08 | fail→fail | 11,031 | 12,745 | +16% | 1 | 1 | 0% | 1,779 | 2,238 | +26% | 0 | 0 | — |
case-09 | fail→pass | 11,427 | 24,718 | +116% | 1 | 1 | 0% | 1,775 | 5,287 | +198% | 0 | 0 | — |
case-10 | fail→pass | 11,055 | 5,202 | -53% | 1 | 1 | 0% | 1,516 | 2,055 | +36% | 0 | 0 | — |
case-11 | fail→fail | 25,289 | 7,384 | -71% | 1 | 1 | 0% | 3,758 | 1,697 | -55% | 0 | 0 | — |
case-12 | fail→fail | 12,574 | 7,132 | -43% | 1 | 1 | 0% | 1,771 | 1,462 | -17% | 0 | 0 | — |
case-13 | fail→fail | 10,978 | 7,767 | -29% | 1 | 1 | 0% | 1,535 | 1,529 | -0% | 0 | 0 | — |
case-14 | fail→pass | 12,479 | 6,205 | -50% | 1 | 1 | 0% | 2,076 | 2,276 | +10% | 0 | 0 | — |
case-15 | fail→pass | 10,851 | 3,877 | -64% | 1 | 1 | 0% | 1,543 | 1,771 | +15% | 0 | 0 | — |
case-16 | pass→fail | 5,773 | 9,049 | +57% | 1 | 1 | 0% | 690 | 1,698 | +146% | 0 | 0 | — |
case-17 | fail→fail | 17,315 | 9,044 | -48% | 1 | 1 | 0% | 2,430 | 1,770 | -27% | 0 | 0 | — |
case-18 | fail→pass | 15,378 | 3,734 | -76% | 1 | 1 | 0% | 2,218 | 1,780 | -20% | 0 | 0 | — |
case-19 | fail→pass | 44,381 | 9,759 | -78% | 1 | 1 | 0% | 1,967 | 1,685 | -14% | 0 | 0 | — |
case-20 | fail→fail | 10,949 | 8,797 | -20% | 1 | 1 | 0% | 1,379 | 1,710 | +24% | 0 | 0 | — |
case-21 | fail→pass | 12,183 | 15,216 | +25% | 1 | 1 | 0% | 1,687 | 2,544 | +51% | 0 | 0 | — |
case-22 | fail→pass | 5,860 | 2,621 | -55% | 1 | 1 | 0% | 721 | 1,517 | +110% | 0 | 0 | — |
case-23 | fail→pass | 11,188 | 3,473 | -69% | 1 | 1 | 0% | 1,619 | 1,625 | +0% | 0 | 0 | — |
case-24 | fail→pass | 14,061 | 12,123 | -14% | 1 | 1 | 0% | 1,923 | 2,145 | +12% | 0 | 0 | — |
case-25 | fail→fail | 8,229 | 10,101 | +23% | 1 | 1 | 0% | 1,039 | 1,882 | +81% | 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. 25 cases were attempted, and 11 counted toward the lift figure. The other 14 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 +28 percentage points is the difference between those two pass rates over the 11 comparable cases. 6 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.