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Cad sim quality harness

Skill rolson24/cad-sim-agent-skills/skills/cad-sim-quality-harness

Evidence-led agent skills for CAD, engineering artifacts, independent review, and bounded revision

Install
npx -y skills add rolson24/cad-sim-agent-skills --skill cad-sim-quality-harness

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

2 things to look at

  • 11 days oldThe repository was created 11 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Internal reset CAD helper for bootstrapping and auditing project-specific Quality Harness rows before serious CAD, simulation, optimization, prototype handoff, A+ review, or repeated fuzzy complaints. Use when intake, design orchestration, review/revise, reviewer lenses, or tooling scout need a small falsifiable evidence contract in quality/quality_harness.yaml, quality_harness_results.json, quality_harness_trace.md, or a Quality Reset seed.

SKILL.md

4.8 KB, as published. Nobody here has run it

CAD Sim Quality Harness

Create the smallest falsifiable evidence contract that prevents polished packets from hiding weak engineering. This is an internal helper, not a user-facing workflow entrypoint.

Contract

Write or refresh these files under the active run root:

quality/quality_harness.yaml
quality/quality_harness_results.json
quality/quality_harness_trace.md
quality/quality_reset_seed.yaml   # only for Quality Reset / clean_restart

quality_harness.yaml is the source of truth. Keep rows compact and current:

schema_version: 2
harness_id: "<stable run-local id>"
harness_version: "<date-or-counter>"
status: active
required: true
selected_profile: "physical_prototype | simulation_optimization | robotics_electromechanical | software_connected_hardware | design_review_only | hybrid"
acceptance_profile: "generic_physical | simulation_optimization | robotics_electromechanical | canoe_design_study | garmin_style_mount"
rows:
  - id: "<stable row id>"
    decision_or_risk: "<decision or failure mode>"
    intended_behavior: "<observable behavior the design claims>"
    verifier_check:
      kind: "command | structured_artifact | visual_review | boundary_review"
      target: "<command, structured file, primary-surface state, or boundary>"
      expected: "<falsifiable expected result>"
      reviewer_command: "<required for command kind; side-effect-free check/dry-run command>"
      reviewer_execution_mode: "isolated_material_copy"
      reviewer_side_effects: "none"
    current_result: "pass | fail | missing | boundary | not_relevant"
    not_relevant_reason: "<required only for not_relevant>"
    evidence_artifacts: []
    required_in_primary_surface: true
    primary_surface_location: "<path#section, or empty until promoted>"
    boundary: "none | physical_boundary | user_boundary | stage_approval"
    next_action: "<smallest next move if this row does not pass>"
    route_if_failed: "continue_autonomous | needs_user | ready_for_bench_test | ready_for_review_with_boundary"

Workflow

  1. Read only the active run's allowed inputs: intake/project_brief.yaml, intake/active_review_rubric.yaml when present, current review_loop.yaml, current handoff.json, canonical annotation_view/, and latest review or user complaint. Preserve any source-boundary preflight.

  2. Select one primary acceptance_profile. Read references/profile_contracts.yaml for the mandatory row IDs and allowed verifier kinds, then references/profile_templates.md for authoring guidance. Every mandatory row must be present. Add at most three run-specific risk rows.

  3. Prefer generated checks, plots, CAD/spatial screenshots, simulator exports, or compact calculations over prose. A pass must use an allowed verifier; boundary_review is only a pass basis when the row itself is bounded. Every command verifier must provide a side-effect-free reviewer_command, declare reviewer_execution_mode: isolated_material_copy and reviewer_side_effects: none, and list its local helper/input files in the verifier mapping. Authoring may run a normal result-producing command before freeze; reviewers never run that command directly against the run root.

  4. Generate quality_harness_results.json from the YAML source of truth:

    python scripts/harness_results.py <run-root>
    python scripts/harness_results.py <run-root> --check
    

    Never hand-edit the JSON mirror. Write quality_harness_trace.md with the short human trace from row to evidence and boundary.

  5. Add pointers, not duplicates: intake/project_brief.yaml.review_rubric.quality_harness.path and intake/active_review_rubric.yaml.quality_harness.path should point to quality/quality_harness.yaml.

  6. If the current packet is patchwork, stale-proof driven, or still bad after repeated fuzzy complaints, write quality_reset_seed.yaml and route through existing clean_restart state values. Old artifacts may be benchmarks, anti-patterns, or requirements evidence, never current proof.

Completion

Stop when every required row is pass, not_relevant, or explicitly boundary with physical_boundary, user_boundary, or stage_approval; each readiness-critical row has a primary-surface location when required; and route preflight can enforce the same facts without reading prose.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.