菜单
Generate a scorecard from sprint execution results — per-phase verdicts, confidence scores, duration, overall status. Optionally writes to the lope journal for historical tracking.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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.
| name | lope-audit |
| description | Generate a scorecard from sprint execution results — per-phase verdicts, confidence scores, duration, overall status. Optionally writes to the lope journal for historical tracking. |
Generate a detailed scorecard showing per-phase verdicts, confidence scores, durations, and overall status.
This skill is invoked two ways:
/lope-audit <sprint_doc>SPRINT-*.md in the working directory as your default target if the user doesn't name one explicitly.PYTHONPATH=~/.lope python3 -m lope audit "$SPRINT_PATH"
--no-journal — skip writing to the lope journal at ~/.lope/journal/YYYY_MM_DD.mdPYTHONPATH=~/.lope python3 -m lope audit "$SPRINT_PATH" --no-journal
Sprint: SPRINT-AUTH-MIDDLEWARE
Phases: 4 (PASS=4, NEEDS_FIX=0, FAIL=0, INFRA=0)
Total duration: 93.4s
Avg confidence: 0.92
---
P1 scaffold: PASS conf=0.95 dur=12s
P2 core-middleware: PASS conf=0.91 dur=34s
P3 refresh-rotation: PASS conf=0.88 dur=28s
P4 integration-tests: PASS conf=0.94 dur=19s
---
Overall: OK
Works for any domain — the scorecard format is domain-neutral.