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MiaoDX

@miaodx source repo

8 published skills

  1. Eval Harness · miaodx bundle
    Select and run Roboclaws validation, product, eval-suite, and live-agent eval rows from a plan, diff, or explicit capability request.
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  2. Eval Evolution · miaodx bundle
    Prepare, run, review, and explicitly promote bounded Skill or existing-MCP Eval Evolution campaigns through the canonical eval facade.
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  3. Household World · miaodx bundle
    Complete household-world goals through public household MCP tools.
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  4. Cloudml Eval Ops · miaodx bundle
    Run frozen Roboclaws Eval Harness rows on CloudML with bounded parallelism, official cml lifecycle commands, executor-backed JuiceFS transfer, durable task receipts, verified collection, and explicit retry/preemption evidence. Use when a user asks to run, refresh, resume, monitor, collect, or debug a Roboclaws baseline/eval on CloudML, especially parallel CPU, MuJoCo, Grounding DINO, provider, or hybrid local/cloud runs.
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  5. Raw Fpv Visual Labeler · miaodx bundle
    Label visible cleanup-relevant movable objects from grouped RAW-FPV frames without creating executable cleanup handles.
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  6. Visual Result Showcase · miaodx bundle
    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.
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  7. Report Performance Analysis · miaodx bundle
    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.
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  8. Scene Gaussian Map Alignment · miaodx bundle
    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.
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