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Use before formal research writing or delivery to package source readiness, compliance or requirement evidence, citation boundaries, and unresolved confirmations for the artifact being moved forward.
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Based on SOC occupation classification
| name | material-passport |
| description | Use before formal research writing or delivery to package source readiness, compliance or requirement evidence, citation boundaries, and unresolved confirmations for the artifact being moved forward. |
Use this skill when a formal research artifact is about to move from planning to drafting, drafting to review, or review to delivery.
Create a compact evidence passport for the artifact so the agent does not treat a polished draft as ready when sources, requirements, compliance, citations, or open confirmations are still weak.
research-wiki/DOCUMENT_PIPELINE.mdshort: internal planning or early draft movement; missing optional evidence becomes WARN.full: reviewer-facing, stakeholder-facing, submission-facing, or final delivery; missing required evidence becomes HOLD.TO CONFIRM items.python3 scripts/material_passport.py --artifact <path> --scope short
python3 scripts/material_passport.py --artifact <path> --scope full --audience reviewer-facing
Add flags when relevant:
--requires-compliance
--requires-requirements
--citation-heavy
--citation-report <path>
--runtime-receipt <path>
--source-map <path>
--integrity-report <path>
--quality-gate <path>
--to-confirm "field or decision still unresolved"
PASS: required evidence is present for the selected scope.WARN: short-scope passport has missing or non-blocking evidence.HOLD: full-scope or required evidence is missing.If status is HOLD, do not call the artifact ready, client-ready, submission-ready, or publication-ready. Resolve the missing evidence, reduce the claim, or keep the artifact as a working draft.
Design and review active learning implementation support for university teachers, including classroom activity structures, preparation workflow, student participation, assessment alignment, and AI-agent support points.
Turn dissertation evidence into an AI agent design specification for supporting active learning implementation, including user scenarios, functions, interaction flow, boundaries, failure modes, and design rationale.
Synthesize confirmed co-design, design-elicitation, prototype-feedback, or participant-generated design outputs into requirements, design principles, prototype changes, participant contribution maps, and design decision logs.
Design specifications for dissertation figures such as conceptual frameworks, concept-card/interview process diagrams, AI-agent workflow diagrams, adoption-condition models, and findings maps.
Maintain a lightweight dissertation research wiki with project memory, literature notes, methodology decisions, supervisor feedback, design rationale, and open questions.
Audit an AI agent prototype or design concept for usability, pedagogical fit, teacher feedback, active learning support, evidence quality, and next design iterations.