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hanhuark/mechanical-engineering-research-skill
A Codex/Claude skill for thermal-fluid mechanical engineering research, proposal development, technical writing, data analysis, presentations, and AI-assisted workflows.
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Verified commit 3980282b717e36f032bf3b571c9b0136bd10ca3e
git clone --filter=blob:none --no-checkout https://github.com/hanhuark/mechanical-engineering-research-skill.git .hotskills/hanhuark-mechanical-engineering-research-skill && git -C .hotskills/hanhuark-mechanical-engineering-research-skill checkout --detach 3980282b717e36f032bf3b571c9b0136bd10ca3e
# Copy each selected skill directory from .hotskills/hanhuark-mechanical-engineering-research-skill into .claude/skills/<skill-name>/Heat
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Individual skills in this repository
- mechanical-engineering-research
skills/mechanical-engineering-research - research-data-analysis
skills/research-data-analysis - research-mentor-review
skills/research-mentor-review - research-proposal-development
skills/research-proposal-development - research-schematic-design
skills/research-schematic-design - research-slide-design
skills/research-slide-design - research-writing-literature
skills/research-writing-literature - reviewer-author-loop
skills/reviewer-author-loop - thermal-fluid-analysis
skills/thermal-fluid-analysis
How it works
mechanical-engineering-research
When your agent uses it
Apply source-aware mechanical-engineering judgment to research, analysis, coding, writing, teaching, research identity, and release work. Use for thermal-fluid systems, heat transfer, fluid mechanics, thermodynamics, HVAC, energy systems, turbomachinery, piping, multiphase flow, experiments, correlations, CFD, reduced-order models, AI/ML, uncertainty, engineering datasets, literature reviews, citations, manuscripts, reviewer revisions, Overleaf packages, figures, proposals, research software, reproducibility, public releases, engineering teaching materials, or a research-project logo and visual identity.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
2,411 tokensWhen the skill is activated
Reference documents
48,815 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Skill Suite
- Core Workflow
- Integrity Gates
- Task Router
- Output Contract
Scripts executed separately
scripts/audit_data_manifest.pyscripts/audit_latex_project.pyscripts/audit_style_calibration.pyscripts/audit_technical_writing.py
Files
../../.claude-plugin/plugin.jsonasset562 bytesSKILL.mdcore12,052 bytesSKILL.mdcore12,052 bytesSKILL.mdcore12,052 bytesSKILL.mdcore12,052 bytesSKILL.mdcore12,052 bytesagents/openai.yamlasset371 bytesassets/templates/benchmark-dataset-readme.mddoc1,420 bytesassets/templates/claim-evidence-ledger.csvasset146 bytesassets/templates/data-rights-manifest.csvasset202 bytesassets/templates/reproducibility-manifest.jsonasset353 bytesassets/templates/response-to-reviewers.mddoc1,559 bytesassets/templates/reviewer-response-matrix.csvasset146 bytesassets/templates/symbol-unit-convention-ledger.csvasset156 bytesreferences/ai-tools-thermal-fluids.mddoc7,300 bytesreferences/anti-formulaic-writing.mddoc8,450 bytesreferences/author-guided-ai-writing.mddoc9,535 bytesreferences/brief-template.mddoc1,466 bytesreferences/budget-and-justification.mddoc7,318 bytesreferences/citation-integrity.mddoc3,846 bytesreferences/data-provenance-and-release.mddoc4,002 bytesreferences/dataset-software-review.mddoc3,083 bytesreferences/experimental-design-and-uncertainty.mddoc3,923 bytesreferences/han-hu-research-style.mddoc16,943 bytesreferences/han-hu-style-calibration-protocol.mddoc2,151 bytesreferences/innovation-commercialization.mddoc13,823 bytesreferences/literature-review.mddoc13,381 bytesreferences/manuscript-revision-submission.mddoc6,957 bytesreferences/model-verification-and-ml-credibility.mddoc3,826 bytesreferences/paper-writing-style.mddoc14,238 bytesreferences/presentation-slides.mddoc14,263 bytesreferences/proposal-development.mddoc21,783 bytesreferences/research-coding.mddoc8,268 bytesreferences/research-logo-and-identity.mddoc5,328 bytesreferences/research-toolchain.mddoc6,926 bytesreferences/research-workflow-and-revision.mddoc7,606 bytesreferences/result-change-and-construct-audit.mddoc2,238 bytesreferences/review-article-workflow.mddoc8,306 bytesreferences/scientific-figure-and-artifact-qa.mddoc6,000 bytesreferences/teaching-mechanical-engineering.mddoc1,595 bytesreferences/technical-writing-analysis.mddoc8,659 bytesscripts/audit_data_manifest.pyruns3,634 bytesscripts/audit_latex_project.pyruns9,341 bytesscripts/audit_style_calibration.pyruns3,640 bytesscripts/audit_technical_writing.pyruns8,231 bytes../research-data-analysis/SKILL.mddoc2,256 bytes../research-mentor-review/SKILL.mddoc3,063 bytes../research-proposal-development/SKILL.mddoc2,437 bytes../research-schematic-design/SKILL.mddoc3,645 bytes../research-slide-design/SKILL.mddoc2,194 bytes../research-writing-literature/SKILL.mddoc4,360 bytes../research-writing-literature/references/technical-argument-audit.mddoc8,765 bytes../research-writing-literature/references/technical-prose-clarity.mddoc6,984 bytes../reviewer-author-loop/SKILL.mddoc8,567 bytes../thermal-fluid-analysis/SKILL.mddoc2,773 bytes
Read SKILL.md
# Mechanical Engineering Research
## Skill Suite
Use this skill as the cross-cutting coordinator when a request spans multiple research activities. For a focused deliverable, use the specialist skill that owns it:
- [thermal-fluid-analysis](../thermal-fluid-analysis/SKILL.md): thermal-fluid physics, experiments, CFD, correlations, uncertainty, scaling, and design tradeoffs.
- [research-writing-literature](../research-writing-literature/SKILL.md): literature synthesis, citations, introductions, manuscript sections, and figure-led discussion.
- [research-proposal-development](../research-proposal-development/SKILL.md): solicitation-aligned proposals, review criteria, preliminary results, milestones, risks, and proposal figures.
- [research-data-analysis](../research-data-analysis/SKILL.md): baseline cases, hypothesis-driven DOE, reproducible analysis, plots, CFD/experimental data, and ML evaluation.
- [research-slide-design](../research-slide-design/SKILL.md): research talks, posters, visual logic, speaker notes, and presentation QA.
- [research-schematic-design](../research-schematic-design/SKILL.md): publication-quality editable facility, mechanism, workflow, and graphical-abstract schematics.
- [research-mentor-review](../research-mentor-review/SKILL.md): constructive student-facing feedback with prioritized, actionable next steps.
- [reviewer-author-loop](../reviewer-author-loop/SKILL.md): iterative reviewer critique, author revision, verification, and re-review.View the complete source on GitHubresearch-data-analysis
When your agent uses it
Design and analyze engineering experiments or simulations using baseline cases, hypothesis-driven DOE, traceable data processing, defensible plots, uncertainty, and validation. Use for CFD, laboratory data, ML workflows, parameter studies, or research code.
What it loads into context
Frontmatter
54 tokensAlways available to the agent
SKILL.md
399 tokensWhen the skill is activated
Reference documents
4,454 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- Route To References
- Hypothesis-Driven DOE
- Data And Plots
Files
SKILL.mdcore2,256 bytesagents/openai.yamlasset322 bytesreferences/ai-tools-thermal-fluids.mddoc7,877 bytesreferences/research-coding.mddoc7,026 bytesreferences/technical-writing-analysis.mddoc8,764 bytes
Read SKILL.md
# Research Data Analysis
## Purpose
Produce understanding, not an exhaustive parameter sweep. Use a detailed baseline case to demonstrate the measurement/model chain, then choose experiments or simulations that can prove or disprove a stated hypothesis.
## Route To References
- For baseline-first DOE, figure discussion, and technical analysis structure, read `references/technical-writing-analysis.md`.
- For reproducible research code and data pipelines, read `references/research-coding.md`.
- For AI/ML-assisted thermal-fluid analysis, read `references/ai-tools-thermal-fluids.md`.
## Hypothesis-Driven DOE
View the complete source on GitHubresearch-mentor-review
When your agent uses it
Provide constructive, evidence-based research mentoring feedback on student manuscripts, proposals, analyses, code, figures, and presentations. Use for advisor-style review that teaches the next improvement without impersonating an advisor or making unsupported scientific judgments.
What it loads into context
Frontmatter
53 tokensAlways available to the agent
SKILL.md
579 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- Review Workflow
- Feedback Standard
- Formulaic Writing Check
- Coordination
Files
SKILL.mdcore3,063 bytesagents/openai.yamlasset320 bytes
Read SKILL.md
# Research Mentor Review
## Purpose
Give the kind of feedback that helps a researcher improve independently: specific, prioritized, explainable, and tied to evidence, logic, and next actions. This is a mentoring workflow, not an impersonation of any advisor and not a substitute for authorship, supervisory judgment, or safety review.
## Review Workflow
1. Identify the deliverable, audience, decision it must support, available evidence, and the research stage.
2. Start with what the work currently establishes and what remains unsupported.
3. Review the highest-risk issues first: research question, novelty/gap logic, methods, assumptions, evidence quality, analysis, physical interpretation, and claim strength.
4. Then address communication: paragraph/slide logic, citation practice, figures, tables, reproducibility, code, and presentation clarity.
5. Convert each important critique into an actionable revision, validation, or question. Explain why it matters.
View the complete source on GitHubresearch-proposal-development
When your agent uses it
Develop research proposals with solicitation alignment, reviewer-focused logic, evidence-bounded preliminary results, milestones, risks, and compelling figures. Use for federal, foundation, equipment, partnership, or internal research proposals.
What it loads into context
Frontmatter
49 tokensAlways available to the agent
SKILL.md
425 tokensWhen the skill is activated
Reference documents
5,622 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- Route To Reference
- Workflow
- Writing Rules
- Final Review
Files
SKILL.mdcore2,437 bytesagents/openai.yamlasset331 bytesreferences/budget-and-justification.mddoc7,318 bytesreferences/proposal-development.mddoc22,807 bytes
Read SKILL.md
# Research Proposal Development
## Purpose
Turn an important research question into a reviewer-decision-ready proposal. Preserve the investigator's scientific direction and distinguish verified preliminary evidence, proposed work, assumptions, and collaborator-dependent commitments.
## Route To Reference
Read `references/proposal-development.md` before drafting a full narrative, responding to review criteria, planning proposal figures, integrating preliminary results, or polishing a submission.
Read `references/budget-and-justification.md` before building a budget, writing a budget justification, preparing a subaward budget, or reconciling the budget with the narrative.
## Workflow
View the complete source on GitHubresearch-schematic-design
When your agent uses it
Create publication-quality, editable scientific and engineering schematics with explicit physical meaning, exact labels, visual hierarchy, and provenance. Use for experimental facilities, thermal-fluid mechanisms, workflows, graphical abstracts, system architectures, or research diagrams.
What it loads into context
Frontmatter
54 tokensAlways available to the agent
SKILL.md
686 tokensWhen the skill is activated
Reference documents
1,244 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- Route To References
- Core Rules
- Workflow
- Required Output
Files
SKILL.mdcore3,645 bytesSKILL.mdcore3,645 bytesSKILL.mdcore3,645 bytesSKILL.mdcore3,645 bytesSKILL.mdcore3,645 bytesSKILL.mdcore3,645 bytesSKILL.mdcore3,645 bytesagents/openai.yamlasset352 bytesassets/templates/schematic-brief.yamlasset892 bytesreferences/schematic-brief.mddoc1,845 bytesreferences/schematic-workflow.mddoc3,155 bytesreferences/scientific-visual-qa.mddoc1,534 bytes
Read SKILL.md
# 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.md` to turn the request into a complete technical brief.
- Read `references/schematic-workflow.md` to choose native-vector, hybrid, or image-editing production.
- Read `references/scientific-visual-qa.md` before delivering or inserting a figure into a manuscript, slide deck, proposal, or poster.
- Start from `assets/templates/schematic-brief.yaml` when the user needs a reusable figure specification.
## Core RulesView the complete source on GitHubresearch-slide-design
When your agent uses it
Design clean, graphics-first technical presentations and posters with logical slide progression, visual hierarchy, purposeful motion, and complementary speaker notes. Use for research talks, conference slides, seminars, defenses, posters, or figure-focused presentations.
What it loads into context
Frontmatter
52 tokensAlways available to the agent
SKILL.md
403 tokensWhen the skill is activated
Reference documents
3,233 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- Route To Reference
- Design Rules
- Research Figure Handling
- Final Check
Files
SKILL.mdcore2,194 bytesSKILL.mdcore2,194 bytesagents/openai.yamlasset307 bytesreferences/presentation-slides.mddoc16,814 bytes
Read SKILL.md
# Research Slide Design
## Purpose
Make technical presentations easy to follow and visually calm. A listener should be able to anticipate the reason for slide N+1 from slide N, while the presenter supplies the interpretation that the slide does not repeat.
## Route To Reference
Read `references/presentation-slides.md` before building an outline, editing slides, creating speaker notes, designing a poster, or planning animations and videos.
## Design Rules
- Establish one claim, question, or transition per slide; use section transitions when the logic changes.
- Treat every visual group as having its own rectangle of space. Maintain clear separation between title, figure, annotation, legend, takeaway, and navigation elements.View the complete source on GitHubresearch-writing-literature
When your agent uses it
Write and revise rigorous research narratives, literature reviews, manuscript sections, citations, and figure discussions. Use for technical introductions, methods, results, discussions, review articles, abstracts, or related-work sections.
What it loads into context
Frontmatter
49 tokensAlways available to the agent
SKILL.md
821 tokensWhen the skill is activated
Reference documents
14,674 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- Route To References
- Introduction And Background
- Citations And Synthesis
- Methods, Models, And Results
- Editorial Safeguards
Files
../mechanical-engineering-research/references/author-guided-ai-writing.mddoc9,535 bytesSKILL.mdcore4,360 bytesSKILL.mdcore4,360 bytesagents/openai.yamlasset309 bytesreferences/anti-formulaic-writing.mddoc7,705 bytesreferences/han-hu-research-style.mddoc11,845 bytesreferences/literature-review.mddoc12,330 bytesreferences/paper-writing-style.mddoc11,181 bytesreferences/technical-argument-audit.mddoc8,765 bytesreferences/technical-prose-clarity.mddoc6,984 bytesreferences/technical-writing-analysis.mddoc8,826 bytes
Read SKILL.md
# Research Writing And Literature
## Purpose
Build a research story rather than a sequence of paper summaries. Every paragraph needs a central topic, normally in its first sentence; each later sentence must develop, support, qualify, or transition from it.
When AI is used to draft or substantially revise the document, first use [author-guided-ai-writing.md](../mechanical-engineering-research/references/author-guided-ai-writing.md) to obtain an author-approved story brief and claim-evidence map. Drafting and line editing do not replace those author decisions.
Before a substantial draft or structural revision, identify the reader, the decision or scientific understanding the section must support, the evidence that carries the central claim, and what the reader should know after the section. Write the structure from that reader-to-evidence path instead of filling a generic manuscript template.
## Route To References
- For critical literature synthesis, read `references/literature-review.md`.
- For journal-paper structure and results-led storytelling, read `references/paper-writing-style.md`.View the complete source on GitHubreviewer-author-loop
When your agent uses it
Iterative manuscript improvement workflow where a reviewer agent critiques, an author agent revises, a verifier checks the revision, and the loop repeats until acceptance or a human-pause condition is reached. Use for virtual peer review, reviewer-author revision loops, rebuttal preparation, resubmission polishing, journal paper improvement, response-to-reviewers drafting, and deciding when additional data, experiments, theory, modeling, or author judgment is required before further revision.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,684 tokensWhen the skill is activated
Reference documents
1,452 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- Confidentiality
- When To Use
- Inputs To Identify
- Loop Protocol
- Stop And Pause Rules
- Output Format
- Coordination With Other Skills
- Minimal Flow
Files
SKILL.mdcore8,567 bytesSKILL.mdcore8,567 bytesagents/openai.yamlasset435 bytesreferences/author-revision-protocol.mddoc2,331 bytesreferences/pause-criteria.mddoc1,966 bytesreferences/reviewer-rubric.mddoc3,012 bytes
Read SKILL.md
# Reviewer-Author Loop
## Purpose
Use this skill to run a closed-loop manuscript improvement process:
1. A **Reviewer Agent** evaluates the manuscript as a skeptical but constructive peer reviewer.
2. An **Author Agent** revises the manuscript and response plan to address the review.
3. A **Verifier Agent** checks that the revision actually resolves the comments and does not introduce new problems.
4. The loop repeats until the reviewer recommends acceptance or the author/verifier reaches a pause condition requiring human input.
This skill is distinct from one-pass peer review. Its value is the revision loop, traceable comment resolution, and explicit stop/pause decision.
## ConfidentialityView the complete source on GitHubthermal-fluid-analysis
When your agent uses it
Analyze thermal-fluid engineering systems, experiments, CFD, and correlations with explicit assumptions, validity limits, uncertainty, and mechanism-based interpretation. Use for heat transfer, fluid mechanics, phase change, HVAC, energy systems, turbomachinery, piping, or thermal management.
What it loads into context
Frontmatter
60 tokensAlways available to the agent
SKILL.md
496 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- Analysis Workflow
- Experiments
- CFD And Modeling
- Results Discussion
- Final Check
Files
SKILL.mdcore2,773 bytesagents/openai.yamlasset306 bytes
Read SKILL.md
# Thermal-Fluid Analysis
## Purpose
Develop decision-ready thermal-fluid analysis without separating numerical output from physical meaning. State what is measured, simulated, derived, assumed, or inferred; retain units, sign conventions, property states, system boundaries, and applicability limits.
## Analysis Workflow
1. Define the system: geometry, working fluid, operating regime, boundary conditions, performance metrics, and constraints.
2. Establish the physical model: governing balances, material properties and their evaluation state, dimensionless groups, competing mechanisms, and expected limiting behavior.
3. Select evidence: standards, handbooks, peer-reviewed work, calibrated measurements, validated CFD, or correlations inside their stated range.
4. Test credibility: dimensional consistency, conservation, order of magnitude, limiting cases, uncertainty/sensitivity, and an independent analytical or empirical check where possible.
5. Conclude by mechanism and tradeoff: performance, pressure drop or pumping power, manufacturability, reliability, cost, safety, scaling, and residual risk.
View the complete source on GitHubSkills included
- mechanical-engineering-research
- research-data-analysis
- research-mentor-review
- research-proposal-development
- research-schematic-design
- research-slide-design
- research-writing-literature
- reviewer-author-loop
- thermal-fluid-analysis
Repository facts
- License
- MIT
- Created
- May 12, 2026
- Last pushed
- Oct 30, 2026
- Forks
- 2
- Open issues
- 0
- Project activity
- Active
Before you install
Local scriptsAutomated signals, not a guarantee
Checked Nov 6, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
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- No broad tool permissions
- No dangerous shell commands
- No hidden or obfuscated text
- No instruction overrides
- No access to secrets
- No requests to vote in Hot
Show all 16 signals by file
.claude-plugin/plugin.json
- Network accessline 6
"name": "Han Hu", "email": "72934230+hanhuark@users.noreply.github.com", "url": "https://github.com/hanhuark" - Network accessline 7
"email": "72934230+hanhuark@users.noreply.github.com", "url": "https://github.com/hanhuark" }, - Network accessline 8
"url": "https://github.com/hanhuark" - Network accessline 9
}, "homepage": "https://github.com/hanhuark/mechanical-engineering-research-skill", "repository": "https://github.com/hanhuark/mechanical-engineering-research-skill", - Network accessline 10
"homepage": "https://github.com/hanhuark/mechanical-engineering-research-skill", - Network accessline 11
"repository": "https://github.com/hanhuark/mechanical-engineering-research-skill",
skills/mechanical-engineering-research/references/innovation-commercialization.md
- Network accessline 7
At the University of Arkansas, route invention and commercialization work through Technology Ventures: https://research.uark.edu/techventures/. Their public page says Technology Ventures helps faculty and research scientists identify, prote
skills/mechanical-engineering-research/scripts/audit_data_manifest.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/mechanical-engineering-research/scripts/audit_latex_project.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/mechanical-engineering-research/scripts/audit_style_calibration.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/mechanical-engineering-research/scripts/audit_technical_writing.py
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#!/usr/bin/env python3
skills/research-data-analysis/references/ai-tools-thermal-fluids.md
- Network accessline 22
- `BubbleID` (https://github.com/cldunlap73/BubbleID): computer-vision framework for pool-boiling images. Use for bubble tracking, segmentation, classification, interface-velocity analysis, bubble statistics, vapor fraction, bubble count, a - Network accessline 23
- `SeqReg` (https://github.com/cldunlap73/SeqReg): sequence-regression framework for boiling heat-flux prediction and general sequence regression from time series, acoustic emission, hydrophone data, and optical/image-derived inputs. - Network accessline 24
- `CFDTwin` (https://github.com/UARK-NED3/CFDTwin): surrogate modeling and digital-twin workflows for CFD simulations, especially when expensive CFD cases need accelerated prediction, interpolation, or design exploration. - Network accessline 25
- `DataDroid-LAM` (https://github.com/spier16/DataDroid-LAM): lab analysis tooling example for automated or AI-assisted processing workflows. - Network accessline 26
- `MEEG-54403` (https://github.com/hanhuark/MEEG-54403): Machine Learning for Mechanical Engineers course reference for supervised learning, unsupervised learning, image classification, clustering, dimensionality reduction, time-series clas
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Signal confidence: 10 facts
- Network access in prose of documentationmechanical-engineering-research/references/innovation-commercialization.md:7Low confidence
- Executable script in code of a file that runsmechanical-engineering-research/scripts/audit_data_manifest.py:1High confidence
- Executable script in code of a file that runsmechanical-engineering-research/scripts/audit_latex_project.py:1High confidence
- Executable script in code of a file that runsmechanical-engineering-research/scripts/audit_style_calibration.py:1High confidence
- Executable script in code of a file that runsmechanical-engineering-research/scripts/audit_technical_writing.py:1High confidence
- Network access in prose of documentationresearch-data-analysis/references/ai-tools-thermal-fluids.md:22Low confidence
- Network access in prose of documentationresearch-data-analysis/references/ai-tools-thermal-fluids.md:23Low confidence
- Network access in prose of documentationresearch-data-analysis/references/ai-tools-thermal-fluids.md:24Low confidence
- Network access in prose of documentationresearch-data-analysis/references/ai-tools-thermal-fluids.md:25Low confidence
- Network access in prose of documentationresearch-data-analysis/references/ai-tools-thermal-fluids.md:26Low confidence
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Native | Manifest .claude-plugin/plugin.json | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md skills/mechanical-engineering-research/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/mechanical-engineering-research/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/mechanical-engineering-research/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/mechanical-engineering-research/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/mechanical-engineering-research/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.