一键导入
eval-harness
Select and run Roboclaws validation, product, eval-suite, and live-agent eval rows from a plan, diff, or explicit capability request.
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。
菜单
Select and run Roboclaws validation, product, eval-suite, and live-agent eval rows from a plan, diff, or explicit capability request.
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
Complete open-ended household robot goals through public household MCP tools.
Drive the ADR-0003 MolmoSpaces real-world cleanup MCP contract.
Label visible cleanup-relevant movable objects from grouped RAW-FPV frames without creating executable cleanup handles.
Align scene Gaussian/splat, USD/mesh, and robot map assets into an honest digital-twin evidence workflow. Use when a new Gaussian scene arrives, when B1/Map12-style assets need to be connected, when map anchors are being projected into a 3D scene, or when an agent must decide whether an alignment is candidate, verified, runtime-proven, or planner-backed.
Create blog, README, social, or demo-review visual showcases from completed Roboclaws run artifacts, especially chaptered GIFs/contact sheets for household cleanup runs. Use when asked to turn report output, robot_views, traces, or run_result.json into polished visual result evidence without rerunning the robot task.
Use this skill whenever the user asks whether a Roboclaws live-agent report run is faster, wants to compare Agent SDK/Codex/Claude cleanup reports, asks about model-call work, residual latency, speedup validity, or report performance under provider/network variance. The skill extracts sanitized JSON metrics first and answers only from generated metric/comparison artifacts, not from report.html scraping or raw prompts.
| name | eval-harness |
| description | Select and run Roboclaws validation, product, eval-suite, and live-agent eval rows from a plan, diff, or explicit capability request. |
Use this skill when a Roboclaws plan, diff, PR, or agent-facing change needs one maintainer proof surface. The skill answers:
It is an orchestration skill, not a robot behavior skill. Keep task strategy in
task skills such as molmo-realworld-cleanup; keep reusable robot capability
semantics in MCP tools and capability profiles.
Recommend rows without running them:
just agent::eval recommend plan=docs/plans/example.md budget=focused
Execute relevant rows for a plan or diff:
just agent::eval execute plan=docs/plans/example.md budget=focused
just agent::eval execute since=origin/main budget=focused
Run one versioned suite directly as a lower-level row/debugging path:
just agent::eval suite=cleanup_capability budget=smoke
Promote failed, blocked, or inconclusive eval evidence:
just agent::eval promote-regression \
eval_results=output/evals/<suite>/<stamp>/eval_results.json \
source_sample_id=<sample-id> \
regression_sample_id=regression.<name>
recommend: never executes rows; it lists commands and preflight needs.execute budget=smoke: deterministic confidence only; selected expensive or
live rows are recorded as skipped by user budget.execute budget=focused: default maintainer mode; selected required live
rows must run or record explicit blocked evidence.execute budget=full: run required and recommended selected rows unless
environment, network, provider, hardware, or guard preflight blocks them.Never downgrade a selected live-agent eval into deterministic-only success.
Missing provider keys, provider 5xx/429, and model-service failures are
model_or_provider_unavailable. Missing Docker, simulator/runtime, DINO
sidecar, Python env, or live-session capacity is environment_blocked.
Each run writes:
output/eval-harness/<stamp>/
eval_harness.json
eval_harness.md
eval_harness.html
rows/<row-id>/
evals/<suite-id>/<stamp>/
The manifest schema is roboclaws_eval_harness_manifest_v1. Rows use
roboclaws_eval_harness_row_v1 and may be deterministic_gate,
product_run, eval_suite, live_agent_eval, regression_promotion, or
manual_review.
The selector is deterministic and rule-table based over plan text, git diff paths, and explicit overrides. It does not use an LLM classifier.
Important signals:
smoke_regression.map_build_consumer.cleanup_capability, and focused/full live eval rows.Do not use or recreate a user-facing agent-validation-matrix route. Historical
plans may mention it, but maintained guidance should point at @eval-harness
and just agent::eval recommend|execute|suite|promote-regression.