Borrowing it
Nothing to install: this file belongs to andrewbartels1/SolidworksMCP-python. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/andrewbartels1/SolidworksMCP-python/main/.github/skills/feature-tree-reconstruction/SKILL.mdgit clone --depth 1 https://github.com/andrewbartels1/SolidworksMCP-pythonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/andrewbartels1/solidworksmcp-python/feature-tree-reconstruction)<a href="https://agentmods.dev/skills/andrewbartels1/solidworksmcp-python/feature-tree-reconstruction"><img src="https://agentmods.dev/badge/skills/andrewbartels1/solidworksmcp-python/feature-tree-reconstruction/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/andrewbartels1/solidworksmcp-python/feature-tree-reconstruction"><img src="https://agentmods.dev/badge/skills/andrewbartels1/solidworksmcp-python/feature-tree-reconstruction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00066 | $0.00564 |
| Opus 5.5 | $0.00026 | $0.00226 |
| Sonnet 5.5 | $0.00013 | $0.00113 |
| Haiku 4.5 | $0.00007 | $0.00056 |
Grade A, and why
feature-tree-reconstruction 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 1mo 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature-Tree Reconstruction
Use this skill when the original model is available and appearance-first guessing would be risky.
Objective
Produce a safe, evidence-backed reconstruction workflow that starts from model inspection and only executes after the family and first-feature strategy are approved.
Workflow
- Inspect before planning:
open_modelget_model_infolist_features(include_suppressed=True)get_mass_propertiesclassify_feature_tree
- Classify feature family and confidence:
revolve,extrude,sheet_metal,advanced_solid,assembly,drawing,unknown
- Delegate by family:
sheet_metaland unsupported advanced families: VBA-aware reconstruction path- simple part families: direct MCP checkpoint plan
- assembly: component-first decomposition, part-level reconstruction per component
- Retrieve supporting evidence before execution:
- local worked examples
- tool-catalog pages
- recent error/remediation history
- Execute conservatively:
- propose 3-6 checkpoint steps only
- require human approval before each irreversible step
- Verify and store:
- capture resulting feature-family alignment
- compare mass properties and key dimensions
- log failures and remediation for future runs
Output Contract
Always return:
familyconfidence(high/medium/low)evidence(top items used)warnings(contradictions, missing evidence)recommended_workflowcheckpoint_plan(3-6 steps)requires_human_confirmation(true/false)
Guardrails
- Never reconstruct from silhouette only when the source model is available.
- Never produce a monolithic 20-step build plan before family acceptance.
- Do not continue execution when confidence is low and contradictory evidence exists.
- If family is
unknown, force additional inspection and user clarification before build. - For sheet metal and unsupported operations, route to VBA-aware planning instead of guessing direct tool calls.
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.
- 1mo ago First seen · 68 lines · 66 tokens per session scan A fee86e87cb4e
feature-tree-reconstruction is a skill published in the GitHub repository andrewbartels1/SolidworksMCP-python (82 stars, last pushed 4d ago), licensed MIT. It adds 66 tokens to every session and 564 once invoked, about $0.0003 per session on Opus 5.5. 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.
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