skills/reference-reading-summarizer/SKILL.md
Read and summarize project reference sources into structured source cards. Use for skimming papers, PDFs, Word docs, Markdown notes, BibTeX files, scripts, specs, collaborator feedback, or source bundles; extract writing patterns, methods, theory, benchmarks, baselines, implementation hints, risks, constraints, and project seeds without yet deciding project implications.
npx skillsauth add a-green-hand-jack/ml-research-skills reference-reading-summarizerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Turn a reference source into a structured, copyright-safe source card. This skill answers: What does this source say?
Do not force every source into project decisions. Use reference-project-synthesizer after a card exists and the project implication matters.
<installed-skill-dir>/
├── SKILL.md
├── references/
│ ├── model-routing.md
│ └── reading-modes.md
└── templates/
├── source-card.md
├── paper-card.md
└── reading-run.md
paper-or-pdf: paper PDFs, reports, slide exports, or other PDF referencescollaborator-doc: PDF/Word/text feedback or collaborator-authored notesmarkdown / text-note / latex-source: notes, specs, drafts, or design documentsbibliography: BibTeX collections and citation setsscript / notebook / config-or-spec: implementation hints, protocols, task definitionsbundle: folder containing multiple docs/scripts/bib/configs that should be interpreted as a unitskim: relevance, role labels, whether deeper reading is neededextract-writing: intro framing, paragraph moves, contribution wording, captions, figure/table narrationextract-method: algorithm, objective, architecture, inference, implementation detailsextract-theory: assumptions, theorem statements, proof ideas, formal definitionsextract-benchmark: task, dataset, split, metric, protocol, compute, evaluation caveatsextract-baseline: baseline method role, fairness conditions, comparison requirementsextract-risk: closest-work threat, novelty boundary, reviewer attack surfaceextract-feedback: collaborator comments, requested edits, contradictions, decisions, TODOsextract-spec: requirements, constraints, interfaces, acceptance criteria, project assumptionsextract-bundle: bundle inventory, internal relationships, most useful files, missing contextextract-implementation-hints: reusable scripts, configs, APIs, preprocessing, commands, pitfallsextract-project-seed: initial idea, problem, assumptions, resources, open questions, first actionsdeep-read: high-value source where misunderstanding would change project directionRead references/model-routing.md.
Default:
skim, simple card skeleton, writing-pattern extraction, bundle inventoryEscalate when the source is closest work, a core algorithm source, a theory source, a benchmark source, a project seed, a collaborator constraint, or when the card will support a claim, experiment, baseline, implementation, paper revision, or rebuttal.
reference/.agent/source-index.md first; fall back to reference/.agent/reference-index.md.reference/.agent/runs/<run-id>/ when extraction is nontrivial:
input-manifest.mdextraction-notes.mdmodel.jsondecision.mdtemplates/source-card.md into reference/cards/<source-id>.md. For pure papers, templates/paper-card.md remains a compatible subtype.reference/.agent/processing-status.md from unread or skimmed to carded when appropriate; update reading-status.md if present for compatibility. Memory writeback: this skill writes only to reference/cards/ and reference/.agent/; project-level memory writeback (claim/evidence/risk boards) is the job of reference-project-synthesizer after synthesis.reference-project-synthesizerliterature-review-sprintcitation-coverage-auditEvery card should include:
Do not turn cheap skim cards into strong conclusions. Mark confidence: skim or needs-deep-read when appropriate.
testing
Bootstrap project-local ml-research-skills. Use from global installs when creating a new ML research project, enabling this collection in an existing ML research repo, or deciding whether to install the full bundle locally. Route to project-init for new projects; do not handle paper or experiment work directly.
development
Route project operations tasks — git, memory, bootstrap, remote, workspace, code review, timeline, ops — to the correct skill. Use when the task involves commits, pushes, worktrees, project memory, enabling project-local skills, SSH/server coordination, sidecar runners, or audits. Do not solve the ops task directly.
testing
Route ML/AI paper writing tasks to the correct skill — contract planning, prose drafting, section writing, consistency editing, review simulation, rebuttal, submission, or citation work. Use when the task involves writing, revising, reviewing, or submitting a paper instead of guessing between paper-writing-assistant, paper-writing-contract-planner, paper-reviewer-simulator, auto-paper-improvement-loop, or citation skills. Do not draft prose directly.
data-ai
Project-local router for ML research skill selection. Use inside an initialized ML research project, or while maintaining this skill repo, when the user describes an ML research/paper/experiment/discovery/ops/release workflow and may not know the skill; route to a domain router or high-signal leaf. Do not use for generic non-ML projects.