feature-tree-reconstruction

feature-tree-reconstruction is a skill for Claude Code, Codex from andrewbartels1/SolidworksMCP-python. It costs 66 tokens per session (564 once invoked), scanned A, original, MIT.

A workflow for rebuilding an existing SolidWorks model by inspecting its feature tree, which is the ordered list of operations that created the model. It classifies the model type before choosing a reconstruction method.

In plain words
What is it for?
Use it to inspect parts, sheet-metal models, assemblies, drawings, and other SolidWorks files; choose a reconstruction path; and verify the rebuilt model.
Why use it?
It reduces the risk of guessing the model's structure from its appearance. Evidence, confidence checks, and approval points help prevent incorrect or irreversible changes.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/andrewbartels1/solidworksmcp-python/feature-tree-reconstruction
Any agent
npx skills add andrewbartels1/SolidworksMCP-python --skill feature-tree-reconstruction
Clone the repo
git clone --depth 1 https://github.com/andrewbartels1/SolidworksMCP-python

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for feature-tree-reconstruction

README.md
[![agentmods](https://agentmods.dev/badge/skills/andrewbartels1/solidworksmcp-python/feature-tree-reconstruction.svg)](https://agentmods.dev/skills/andrewbartels1/solidworksmcp-python/feature-tree-reconstruction)
Your own site
<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="Measured on agentmods" height="20"></a>
Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 564 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00066 $0.00564
Opus 5 $0.00033 $0.00282
Sonnet 5 $0.00013 $0.00113
Haiku 4.5 $0.00007 $0.00056

Measured 4d ago against content hash fee86e87cb4e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 4d 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.

.github/skills/feature-tree-reconstruction/SKILL.md · 68 lines

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

  1. Inspect before planning:
    • open_model
    • get_model_info
    • list_features(include_suppressed=True)
    • get_mass_properties
    • classify_feature_tree
  2. Classify feature family and confidence:
    • revolve, extrude, sheet_metal, advanced_solid, assembly, drawing, unknown
  3. Delegate by family:
    • sheet_metal and unsupported advanced families: VBA-aware reconstruction path
    • simple part families: direct MCP checkpoint plan
    • assembly: component-first decomposition, part-level reconstruction per component
  4. Retrieve supporting evidence before execution:
    • local worked examples
    • tool-catalog pages
    • recent error/remediation history
  5. Execute conservatively:
    • propose 3-6 checkpoint steps only
    • require human approval before each irreversible step
  6. 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:

  • family
  • confidence (high/medium/low)
  • evidence (top items used)
  • warnings (contradictions, missing evidence)
  • recommended_workflow
  • checkpoint_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.

Read the full file on GitHub · 68 lines

Changes

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.

  1. 4d ago First seen · 68 lines · 66 tokens per session scan A fee86e87cb4e

Subscribe to this mod's changes

feature-tree-reconstruction is a skill published in the GitHub repository andrewbartels1/SolidworksMCP-python (68 stars, last pushed today), licensed MIT. It adds 66 tokens to every session and 564 once invoked, about $0.0003 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.

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