mcpbeat

Hephaestus Network

agentlas-ai/agentlas-os-openclaw-hephaestus-network

Use when the user asks OpenClaw to staff a durable goal from Agentlas Hub agents or teams. The active host LLM chooses the exact roster, which remains goal-bound until explicit completion.

718 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
1165
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/agentlas-ai/Agentlas-OS --skill hephaestus-network

The instruction itself

1 sections, as written by the author

Hephaestus Agent Workforce Network

The active host LLM is the per-turn orchestrator. Hub supplies content and

qualification evidence, exact immutable releases, and BYOM directives; it does

not select the final team or run a server-side LLM.

  • Create a redacted agentlas.workforce-work-order.v1 with substantive role

slots, skills/knowledge, MCP tools, artifacts, runtime/language/authority,

cardinality, and handoff/review edges. Keep private context local.

  • Call Hub MCP workforce.search_candidates.
  • As the host LLM, author agentlas.workforce-selection.v1 from exact content

and eval evidence. Do not use lexical top-1, popularity, ratings, history,

revenue, or local callability as semantic fit.

  • Call workforce.validate_selection, revise on rejection, then call

workforce.prepare_execution. Require exact release version, package hash,

content digest, and directive bundle; never silently substitute.

  • Bind the prepared plan to the stable current goal/task id with

workforce.bind_goal. On every later turn read workforce.goal_context,

reuse the incumbent roster plus local skills when sufficient, recruit only

a real additive gap, and record the posture with

workforce.record_goal_turn.

  • Before each bound planner/manager, worker, synthesis, or verifier call,

advertise the host's real sessions and call model.resolve_allocation with

the host-owned stage. Use the exact provider/model/effort receipt. Pins and

ceilings come only from AGENTLAS_MODEL_ALLOCATION_POLICY_JSON; missing

worker policy inherits orchestrator. usage: null before invocation is not

worker-execution proof.

  • Run only useful bound manager/planner, workers, synthesis, and verifier as

distinct model invocations with explicit artifact handoffs and nested Team

graphs.

Keep the roster across turns, sessions, restarts, compaction, and lease expiry.

Release it only with workforce.complete_goal(explicitCompletion=true) after

explicit whole-goal completion/cancellation. A 24-hour lease controls only the

next Hub charge; standby is durable availability, not a continuously running

model. Memory/Experience accrue on actual invocations.

If this OpenClaw host cannot call the Workforce MCP tools or create separate child

invocations, report the last truthful state instead of calling the legacy

router. Execution success requires planner parse success without fallback,

every child/handoff receipt, synthesis, and a passing verifier.

How to use it

Copy the folder

Take agentlas-ai/agentlas-os-openclaw-hephaestus-network from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.