Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/w1ne/kernelcad-web/image-replicatornpx skills add w1ne/kernelCAD-web --skill image-replicatorgit clone --depth 1 https://github.com/w1ne/kernelCAD-webWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00043 | $0.01548 |
| Opus 5 | $0.00022 | $0.00774 |
| Sonnet 5 | $0.00009 | $0.00310 |
| Haiku 4.5 | $0.00004 | $0.00155 |
Grade A, and why
image-replicator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
image-replicator
Purpose
After the blockout is plausible, this sub-skill drives the detail pass. Each iteration produces a render, runs the scorer, and returns a concrete decision: which gate is weakest, what model change closes it, whether to continue or stop.
Inputs
- The
.kcad.tsfile with a clean blockout. - The reference photo path.
- Current scorer output (or "not yet run" on the first pass).
The iteration loop
Step 1 — Render
Always render all four canonical views at the correct resolution:
kernelcad render build.kcad.ts \
--width 1920 --height 1080 \
--views front,right,top,iso \
-o /tmp/render.png
Read the PNG back. Filenames are not evidence — you must observe the image.
Step 2 — Score
kernelcad score build.kcad.ts \
--reference ./reference.jpg \
--view front \
--json
The scorer returns a JSON object with per-gate values:
{
"silhouette": 0.71,
"ssim": 0.28,
"phash": 0.62,
"overall": 0.54
}
Score all views that have a reference. If the task harness provides a
harness.ts, run it directly — it wraps scoreAgainstReference with the
correct thresholds:
npx ts-node eval/tasks/taskNN/harness.ts
Step 3 — Diagnose which gate to chase next
Read the per-gate numbers and decide:
| Gate | Low score means |
|---|---|
silhouette |
The 2D silhouette outline does not match: wrong overall shape, missing brow bumps, wrong arm angles, wrong aspect ratio. Fix: adjust params, fix the path outline. |
ssim |
The rendered image is structurally different from the reference: wrong depth, wrong material (flat vs. glossy), missing lens geometry, wrong body proportion. Fix: apply PBR material (Rule 6), add depth (Rule 3), fix booleans. |
phash |
Perceptual hash mismatch: high-level appearance differs. Usually a proxy for "the object reads as a different product category." Fix: re-read the brief, re-check primary mass arrangement. |
Chase the lowest gate first. A 0.20 silhouette score means the silhouette is wrong — fixing the SSIM first will not help because the silhouette mismatch dominates overall score.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 157 lines · 43 tokens per session scan A c7004cec110c
image-replicator is a skill published in the GitHub repository w1ne/kernelCAD-web (17 stars, last pushed 4d ago), licensed MIT. It adds 43 tokens to every session and 1,548 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
simplecadapi
Thin SimpleCAD SDK reference skill focused on the public API surface, core types, and current modeling workflows.
maya-scripting
Bootstrap stage — escape hatch for Maya work that has no packaged skill yet. Agents should prefer searchskills / dcccapabilitymanifest → loadskill → typed tools (inputSchema + annotations) from domain skills; use executepython or executemel only when no skill matches, for bulk in-process loops, or for API…
cad
Use when doing parametric 3D modeling, 2D drafting, geometric kernel development, BIM/IFC processing, PCB design, or AutoCAD .NET development. Index of 19 skills: FreeCAD, OpenSCAD, OCCT, CadQuery, KiCad, SolveSpace, QCAD, xBIM, Clipper2, TongWen and more.
maya-asset-source
Pipeline stage — asset discovery and resolution. Search local asset libraries, resolve paths to structured AssetDescriptor records, and surface candidate assets for downstream import. Use before maya-import-to-scene to locate what to import.
cadquery-modeling
Generate CAD models with CadQuery using an adaptive CAD Compiler workflow: plan-mode skill constraint handoff, focused iteration scopes, fail-first challenge review, compact review packets, real-product reference fidelity, visual/functional audits, source-first repair, and STEP/STL export. Use for CadQuery, CQ, 建模…
maya-geometry
Interchange stage — FBX / OBJ geometry interchange. Round-trip geometry through FBX or OBJ; scene save is owned by maya-scene. The FBX export tool drives every FBXExport option through the FBX plugin's MEL globals, bakes animation by default, and verifies the output file. Use for cross-DCC handoff. Not for primitive…