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Execute a sprint phase-by-phase with validator-in-the-loop retry. For each phase: implement (code, deliverables, research, etc.) → AI validators review → retry on NEEDS_FIX → advance on PASS. Works with any domain (engineering, business, research).
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | lope-execute |
| description | Execute a sprint phase-by-phase with validator-in-the-loop retry. For each phase: implement (code, deliverables, research, etc.) → AI validators review → retry on NEEDS_FIX → advance on PASS. Works with any domain (engineering, business, research). |
For each phase: implement → validators review → retry on NEEDS_FIX → advance on PASS.
This skill is invoked two ways:
/lope-execute <sprint_doc> and expects you to run that docSPRINT-*.md in the working directory is usually a safe bet — confirm with the user if ambiguous).Each phase gets validated twice back-to-back:
Set LOPE_SINGLE_STAGE=1 to revert to legacy single-pass validation.
PYTHONPATH=~/.lope python3 -m lope execute "$SPRINT_PATH"
c. If spec NEEDS_FIX: apply the spec-level fixes, re-validate. Quality pass won't run until spec passes.
d. If quality NEEDS_FIX: apply the quality-level fixes, re-validate.
e. On both PASS: mark phase complete, move to next
3. After all phases: run /lope-audit for the scorecard
Lope auto-detects the sprint's domain from the doc header:
The validator review task adapts automatically. Same /lope-execute command for every domain.
Use when configuring, installing, verifying, or troubleshooting optional Headroom MCP compression with Lope or Lope-installed agent hosts. Covers opt-in Headroom MCP registration, large validator/tool-output compression, Claude/Codex/Cursor MCP status checks, and the `headroom-ai[mcp]` dependency when the user asks for it.
Multi-CLI validator ensemble for AI work. Use for multi-phase sprints, single-shot cross-model checks, autonomous flow graphs, evidence gates, team management, persistent finding memory, council deliberation, and self-update. Any AI CLI implements, any AI CLI validates. 15 built-in CLI adapters plus infinite custom providers via JSON config.
Use Lope when cross-model perspective helps: multi-phase sprints, flow graphs, ask/review/vote/compare/pipe, team, lope memory, lope deliberate, gate/check, update, or Headroom setup. Trigger on 3+ phases, consequential multi-file work, second opinions, artifact review, consensus/SARIF, ADR/PRD/RFC/build-vs-buy/migration, A/B or yes/no choices, pipes, roster changes, updates, or Headroom. Skip trivial single edits, pure conversation, urgent firefighting.
Run a declarative GRAPH workflow where AI agents negotiate autonomously — no human gates. A flow is a DOT graph: nodes are agent turns, ensemble reviews, shell verify-steps, or judge/routers; edges carry conditions and loops. Each node dispatches into lope's existing multi-CLI executors (any CLI implements, the ensemble votes). Bounded by per-node and graph-wide visit caps so an unsupervised run can never loop forever. Use when the user wants to shape HOW agents collaborate (fan-out proposers, consensus, fix-loops) as editable, reviewable, version-controlled process — not a fixed linear sprint.
Manage the validator team (add, list, remove, smoke-test) via CLI flags. Trigger on any user request about adding, removing, configuring, enabling, disabling, or testing a validator/teammate/CLI on lope — including 'add openclaw to lope', 'remove ollama from the team', 'list validators', 'is my new provider working', 'hook up my mistral pod', 'what CLIs are configured', OR the user pasting a curl command and saying 'add this'. Works for subprocess binaries (any local CLI), OpenAI-compatible HTTP endpoints (pods, gateways, cloud APIs), and pasted curl commands via `--from-curl`. The LLM translates natural language into the right invocation — the end user never edits JSON.
Draft a sprint doc via multi-round negotiation with AI validators. Lope sends your plan to other AI CLIs (Claude, Gemini, OpenCode, Codex, Mistral Vibe, Aider, Ollama, Goose, llama.cpp, Open Interpreter, Copilot, Amazon Q, or any HTTP API) for independent review. Majority vote. No single-model blindspot. Works for engineering, business (marketing, finance, ops), and research domains.