agentsclimarketplace

Hephaestus build

Skill agentlas-ai/Agentlas-OS/codex/plugins/agentlas-core-engine-meta-agent/skills/hephaestus-build

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Install
npx -y skills add agentlas-ai/Agentlas-OS --skill hephaestus-build

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Use when the user types /prompts:hep-build, mentions @Hephaestus for build work, asks to create a single Agentlas agent, create a multi-agent team, or package an existing local/external agent into Agentlas architecture.

SKILL.md

5.0 KB, as published. Nobody here has run it

Hephaestus Build

Procedure

  1. Treat this as the public Codex build surface. Do not expose or ask the user to invoke the older internal support skill names.
  2. Read AGENTS.md and .agentlas/mode-map.json when they exist in the current workspace.
  3. Run the public mode classifier by independent ownership boundaries, not by keywords such as "team":
    • package or repair existing material -> 30-agentlas-packager;
    • one independently owned context/tools/success standard -> 10-single-agent-builder;
    • two or more roles with separate context, permissions, success standards, handoff, or synthesis needs -> 20-multi-agent-team-builder. If the shape is unclear, ask before generating. The user-facing question must be plain language, for example: "이 일을 한 명의 전문가가 처음부터 끝까지 맡으면 되나요, 아니면 조사/분석/검토처럼 여러 전문가가 나눠 맡고 마지막에 합쳐야 하나요?" Do not expose internal labels such as single-agent, team-builder, ownership boundary, memory/context, synthesis, or produces/consumes.
  4. Run the Builder Interview and Research Gate from docs/builder-interview-research-gate.md before writing substantial package files:
    • ask an 8-12 question first batch when the request is vague;
    • continue follow-ups until target user, tasks, inputs, outputs, examples, role count, separated tools or permissions, final merge needs, execution order, memory, failure modes, and evals are clear;
    • phrase shape questions in everyday language. Ask who handles which part, whether each role needs different files/accounts/tools, whether someone must merge the result, and whether work can run at the same time or must pass from one person to the next;
    • research official or primary docs, similar agent repositories or comparables, GitHub examples, academic/professional theory, and tool/plugin docs;
    • compare selected and rejected tools/plugins with permission, secret, fallback, and smoke-test notes;
    • synthesize domain-expert behavior from interview answers, comparable agents/repos, theory, and tool choices;
    • write docs/builder-interview.md, docs/research-sources.md, docs/tool-selection.md, docs/domain-expert-synthesis.md, docs/prompt-performance-contract.md, and .agentlas/capability-eval-plan.json.
  5. If missing narrow details still change files, adapters, or public/private boundaries, ask one to five clarify questions before generating.
  6. Pick one:
    • 10-single-agent-builder;
    • 20-multi-agent-team-builder;
    • 30-agentlas-packager.
  7. Load matching support skills.
  8. Write all generated or repaired runtime agent instructions in English: AGENTS.md, CLAUDE.md, GEMINI.md, agent.md, skills, workflow/command adapters, runtime prompts, handoff contracts, return contracts, and operating docs. Translate Korean or other-language source material into English agent behavior. Localized public copy, routing trigger examples, and sample user inputs may use the target user language.
  9. Emit or repair Agentlas contracts, including .agentlas activation seed files and .agentlas/global-commands.json when local continuity is part of the output.
  10. Add the generated command to Claude Code, Codex, Gemini CLI, generic AGENTS.md, and terminal adapters. For teams, expose the orchestrator/HQ command and route workers through HQ unless direct worker commands were requested.
  11. Run scripts/verify-team-package.sh <generated-package-root> for generated or repaired packages. If it fails, do not report completion; collapse the output to a single-agent package or add the required orchestrator/HQ and team contracts.
  12. Verify with scripts/verify-package.sh.
  13. Once verification and local registration have succeeded, ask one final two-choice storage question using structured controls when available: Cloud에 올리기 or 로컬에만 저장. Cloud means owner-private Agent Cloud storage, restorable on another signed-in Desktop. Mobile can use the package only after a paired Desktop restores/installs it; Cloud is not a hosted LLM runtime. Local-only performs no network mutation.
  14. Never upload by default. Missing input and non-interactive execution are local-only. Only after explicit Cloud consent, run the trusted Hephaestus runner with upload <exact-verified-package-root> --visibility private-link. Keep the local package on every auth/offline/CAS/quota/scan failure and report an exact retry command. Public Hub publication remains a separate explicit action.

Output

Return status, evidence, output, global_commands, interview_research, and blockers.

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