run-tradeoff-study

run-tradeoff-study is a skill for Claude Code from JMMonte/agentic-digital-twin. It costs 99 tokens per session (949 once invoked), scanned A, original, MIT.

A method for comparing competing hardware designs with calculated measurements and several weighting schemes. A sensitivity check tests whether the preferred choice changes when priorities such as performance or serviceability change.

In plain words
What is it for?
Use it when choosing between hardware layouts or structures, such as battery placement, sponsons, or strut geometry, before building the selected design.
Why use it?
It replaces an intuition-based choice with evidence about factors such as balance, space, weight, clearance, and containment. The sensitivity check helps reveal choices that only win under one set of priorities.

Skill for Claude Code

Written for Claude Code: ${CLAUDE_PLUGIN_ROOT} variable. Also seen: mentions AGENTS.md.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the agentic-digital-twin plugin — 8 skills, 1 agent shipped together

Good fit Use it when choosing between hardware layouts or structures, such as battery placement, sponsons, or strut geometry, before building the selected design.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add JMMonte/agentic-digital-twin
Claude Code
/plugin install agentic-digital-twin

Made for: Claude Code.

Or install agentic-digital-twin, the plugin that ships this one along with the rest of its 8 skills, 1 agent.

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 run-tradeoff-study

README.md
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Your own site
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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.

agentmods 80×15 button for run-tradeoff-study

Your own site · 80×15
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Per session 99 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 949 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00099 $0.00949
Opus 5 $0.00049 $0.00475
Sonnet 5 $0.00020 $0.00190
Haiku 4.5 $0.00010 $0.00095

Measured 9d ago against content hash dd86163ceec9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

run-tradeoff-study 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 9d 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.

skills/run-tradeoff-study/SKILL.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Run a scored tradeoff study

Gut picks lose. A scored study with sensitivity earns its keep — it has overturned the "obvious" choice repeatedly. This skill runs one correctly.

The method (four rules)

  1. Cheapest fidelity that discriminates. Evaluate candidates at the PLACEMENT level — box layouts over the exported sections/rings — NOT a full build per candidate. Build only the winner.
  2. Score on COMPUTED physics. CG, inertia, margin, density, containment, clearance — computed from the candidate parameters, not asserted.
  3. Sensitivity, then ROBUST winner. Sweep several weight schemes (equal / performance-leaning / serviceability-leaning / ...). Pick the candidate that wins the MOST schemes, not the one that wins a single weighting. A lone-weighting winner hides ties and fragile leads.
  4. Side by side. One rerun Spatial3DView PER candidate, tiled in a grid (a rrb.Grid of views). Never overwrite one recording per candidate — the whole point is to see them together.

The gotchas (each cost a wrong answer once — see GUARDRAILS.md §4)

  • np.interp requires ASCENDING xp. For "lower is better" use ascending xp with REVERSED fp (np.interp(x, [lo, hi], [5, 2])). A descending xp does not raise — it SILENTLY INVERTS the metric and makes the worst candidate look best.
  • Score what actually constrains, not a proxy that looks placed. E.g. pack DENSITY (kg/L) catches a box too small to hold its cells; a "placed" box can still be infeasible.
  • Containment vs the REAL ring, not an assumed beam. A V-bottom + tumblehome hull means a side box pokes out the TOP corner as well as the bottom. Check against the exported section, not an envelope you imagined.
  • Drop metrics that don't discriminate. If a metric is ~constant across candidates (e.g. CG_x when payload sits at the CG), it earns no column — find one that separates them.

Procedure

  1. If the project already has tradeoff.py (from scaffold-hw-project), start there. Otherwise copy the template: cp "${CLAUDE_PLUGIN_ROOT}/skills/scaffold-hw-project/templates/tradeoff.py" ./tradeoff.py

Read the full file on GitHub · 76 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. 9d ago First seen · 76 lines · 99 tokens per session scan A dd86163ceec9

Subscribe to this mod's changes

run-tradeoff-study is a skill published in the GitHub repository JMMonte/agentic-digital-twin (2 stars, last pushed 2mo ago), licensed MIT. It adds 99 tokens to every session and 949 once invoked, about $0.0005 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 2026-08-31.

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