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 skills add wkzMagician/newton-mcp --skill apply-fields-and-constraintsgit clone --depth 1 https://github.com/wkzMagician/newton-mcpWrote 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/wkzmagician/newton-mcp/apply-fields-and-constraints)<a href="https://agentmods.dev/skills/wkzmagician/newton-mcp/apply-fields-and-constraints"><img src="https://agentmods.dev/badge/skills/wkzmagician/newton-mcp/apply-fields-and-constraints/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/wkzmagician/newton-mcp/apply-fields-and-constraints"><img src="https://agentmods.dev/badge/skills/wkzmagician/newton-mcp/apply-fields-and-constraints.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00051 | $0.00228 |
| Opus 5 | $0.00026 | $0.00114 |
| Sonnet 5 | $0.00010 | $0.00046 |
| Haiku 4.5 | $0.00005 | $0.00023 |
Grade A, and why
apply-fields-and-constraints 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 11d 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.
What it actually says
Apply fields and constraints
- Decide whether the prompt describes a kinematic relationship (constraint) or an environmental influence (field).
- Use
fixed-pointto anchor one object point in world space. - Use
distanceto maintain separation between two object-local points; specify distance in meters and dimensionless normalized stiffness. - Use a
uniformfield for constant force [N] or acceleration [m/s^2]. - Use a
radialfield for attraction or repulsion around a center; use the sign ofstrengthconsistently. - Set
object_idsonly when the influence is selective; omit it for a global field. - Inspect object references before deletion and remove dependent constraints or fields first.
Do not emulate a constraint with an extremely strong force unless the requested motion is intentionally compliant.
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.
- 11d ago First seen · 17 lines · 51 tokens per session scan A ae78a874cfa1
apply-fields-and-constraints is a skill published in the GitHub repository wkzMagician/newton-mcp (0 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 51 tokens to every session and 228 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 2026-08-31.
Other skills, from other repositories
i4h-workflow-train-rl
Use when training, evaluating, or exporting Workflow policies with online RSL-RL or RLinf, including RL checkpoint and Workflow handoff.
godot-optimization
Use when optimizing Godot games — profiler, draw calls, physics tuning, memory management, and common bottlenecks.
robosuite/training-law/SKILL
Master reference for ASPIRE Robosuite experiments. Covers system overview, 7 tasks, setup, running experiments, debugging, full API reference, and all pipeline modes (Fix Loop, Baseline).
systemic-emergent-design
Use when designing, auditing, or generating the systemic substrate of a game — the small set of consistent, interacting rules (a "chemistry engine") that produce emergent play, rather than scripted set-pieces. Covers immersive-sim principles (intention & perceivable consequence), multiplicative vs. additive design…
metashape-reconstruction
Guide dense reconstruction, mesh building, and texturing in Metashape MCP. Covers depth maps, point cloud, mesh, texture, DEM, and orthomosaic generation with correct GPU/CPU settings and quality parameters. Works through the Metashape MCP server.
digital-twin-builder
Enriches a Universal Assembly Graph (UAG, produced by the game-designer skill) with simulation-grade semantics -- physical units, material/mass properties, and physics/logic constraints -- so it is ready for a physically meaningful digital twin, not just a visual scene. Use this skill whenever the user wants a UAG to…