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 hanhuark/mechanical-engineering-research-skill --skill research-schematic-designgit clone --depth 1 https://github.com/hanhuark/mechanical-engineering-research-skillWrote 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/hanhuark/mechanical-engineering-research-skill/research-schematic-design)<a href="https://agentmods.dev/skills/hanhuark/mechanical-engineering-research-skill/research-schematic-design"><img src="https://agentmods.dev/badge/skills/hanhuark/mechanical-engineering-research-skill/research-schematic-design/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/hanhuark/mechanical-engineering-research-skill/research-schematic-design"><img src="https://agentmods.dev/badge/skills/hanhuark/mechanical-engineering-research-skill/research-schematic-design.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 | $0.00050 | $0.00686 |
| Opus 5 | $0.00025 | $0.00343 |
| Sonnet 5 | $0.00010 | $0.00137 |
| Haiku 4.5 | $0.00005 | $0.00069 |
Grade A, and why
research-schematic-design 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 today.
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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Schematic Design
Purpose
Create figures that clarify a scientific claim without inventing evidence. A publication schematic is an engineering artifact: its geometry, flow directions, symbols, labels, color semantics, and stated scale must agree with the manuscript, data, and method.
Route To References
- Read
references/schematic-brief.mdto turn the request into a complete technical brief. - Read
references/schematic-workflow.mdto choose native-vector, hybrid, or image-editing production. - Read
references/scientific-visual-qa.mdbefore delivering or inserting a figure into a manuscript, slide deck, proposal, or poster. - Start from
assets/templates/schematic-brief.yamlwhen the user needs a reusable figure specification.
Core Rules
- Default to an editable, native-vector source for system diagrams, facility schematics, workflows, labels, arrows, dimensions, equations, and legends. Use SVG, PowerPoint objects, or another requested editable format.
- Use image generation only for visual concepts, non-semantic textures, or a reference composition. Never trust generated in-image text, arrow direction, numerical values, symbols, dimensions, or geometry as the final scientific content.
- Recreate every final label, arrow, line, icon, legend, and quantitative annotation deterministically after a generated concept is selected.
- Distinguish measured apparatus, simulated domains, proposed systems, conceptual mechanisms, and illustrative elements. Do not depict unmeasured mechanisms as observed facts.
- Preserve source and rights boundaries. Do not trace or reproduce a third party's figure without permission; use supplied references for inspiration and create an original composition.
Workflow
- Identify the figure's single job: explain a facility, mechanism, workflow, comparison, or overall research story.
- Build the schematic brief: audience, target size, required components, exact labels and units, connections, evidence class, visual hierarchy, and exclusions.
- Choose the production lane:
- Native vector: default for facilities, flow paths, process diagrams, methods, system architectures, and graphical abstracts with exact labels.
- Hybrid: generate one or more concept frames for composition or material cues, then reconstruct the accepted concept as editable vector objects.
- Controlled image edit: use only when a supplied image must retain visual information; overlay all technical annotations as deterministic vector elements.
- Build the source artifact and a rendered preview. Keep the figure uncluttered, with one visual grammar for arrows, line weights, colors, labels, and callouts.
- Run the scientific visual QA before delivery. Verify every label against the source material and inspect the figure at final use size.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today First seen · 48 lines · 50 tokens per session scan A ba4783cae284
research-schematic-design is a skill published in the GitHub repository hanhuark/mechanical-engineering-research-skill (16 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 686 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 2026-09-10.
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