document-knowledge
Distills everything Claude has learned in the current conversation into a structured Markdown knowledge document saved to disk. Use when the user wants to capture findings, decisions, architecture insights, or research from a session — phrases like "document what you know", "save your knowledge", "write up what we found", or "document this".
What this skill does
# Document Knowledge Scan the current conversation, extract what was learned, and write it to a structured Markdown file. ## What to capture - **Findings** — things discovered about the codebase, system, or domain - **Decisions** — choices made and why - **Architecture / structure** — how things fit together - **Gotchas / surprises** — unexpected behavior, edge cases, constraints - **Open questions** — unresolved issues worth flagging - **Next steps** — actionable follow-ups if any were identified Don't summarize the conversation itself — distill the *knowledge* it produced. ## Output ### Filename Generate a slug from the main topic discussed: `knowledge-<topic>-<YYYY-MM-DD>.md` e.g. `knowledge-auth-middleware-2026-04-03.md` If the user passed a name via `$ARGUMENTS`, use that as the filename (add `.md` if missing). ### Save location Save to the current working directory unless the user specifies otherwise. ### Structure ```markdown # [Topic] — Knowledge Document _Generated: YYYY-MM-DD_ ## Summary One short paragraph: what this session was about and the key takeaway. ## Findings - ... ## Decisions | Decision | Rationale | |----------|-----------| | ... | ... | ## Architecture / Structure (Include only if relevant — diagrams, file relationships, data flow, etc.) ## Gotchas & Constraints - ... ## Open Questions - [ ] ... ## Next Steps - [ ] ... ``` Omit any section that has nothing to add — don't leave empty headings. ## Behavior 1. Review the full conversation context. 2. Extract and synthesize knowledge (not a transcript summary). 3. Write the file to disk using the Write tool. 4. Confirm to the user: filename + path + brief description of what was captured. $ARGUMENTS
Related in Writing & Docs
jax-development
IncludedUse this skill when the user is writing, debugging, profiling, refactoring, reviewing, benchmarking, parallelising, exporting, or explaining JAX code, or when they mention JAX, jax.numpy, jit, grad, value_and_grad, vmap, scan, lax, random keys, pytrees, jax.Array, sharding, Mesh, PartitionSpec, NamedSharding, pmap, shard_map, Pallas, XLA, StableHLO, checkify, profiler, or the JAX repo. It helps turn NumPy or PyTorch-style code into pure functional JAX, fix tracer/control-flow/shape/PRNG bugs, remove recompiles and host-device syncs, choose transforms and sharding strategies, inspect jaxpr/lowering/IR, and benchmark compiled code correctly.
nature-article-writer
IncludedDrafts, rewrites, diagnostically critiques, and style-calibrates primary research manuscripts for Nature and Nature Portfolio journals. Use when the user wants a Nature-style title, summary paragraph or abstract, introduction, results, discussion, methods, figure legends, presubmission enquiry, cover letter, reviewer response, or when a scientific draft sounds generic, jargon-heavy, structurally weak, or AI-ish and needs precise, broad-reader-friendly prose without inventing data, analyses, or references. Best for primary research articles and letters rather than reviews or press releases unless explicitly adapting one.
deckrd
IncludedDocument-driven framework that derives requirements, specifications, implementation plans, and executable tasks from goals through structured AI dialogue. Use when user says "write requirements", "create spec", "plan implementation", "derive tasks", "structure this feature", "break down into tasks", or "document this module". Also use for reverse engineering existing code into docs (/deckrd rev). Do NOT use for direct code writing — use /deckrd-coder after tasks are generated. Do NOT use when the user only wants to run or fix existing code without planning.
clinical-decision-support
IncludedGenerate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
handling-sf-data
IncludedSalesforce data operations with 130-point scoring. Use this skill to create, update, delete, bulk import/export, generate test data, and clean up org records using sf CLI and anonymous Apex. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, needs data factory patterns for Apex tests, or needs to seed/clean records in a Salesforce org. DO NOT TRIGGER when: SOQL query writing only (use querying-soql), Apex test execution (use running-apex-tests), or metadata deployment (use deploying-metadata).
accelint-ac-to-playwright
IncludedConvert and validate acceptance criteria for Playwright test automation. Use when user asks to (1) review/evaluate/check if AC are ready for automation, (2) assess if AC can be converted as-is, (3) validate AC quality for Playwright, (4) turn AC into tests, (5) generate tests from acceptance criteria, (6) convert .md bullets or .feature Gherkin files to Playwright specs, (7) create test automation from requirements. Handles both bullet-style markdown and Gherkin syntax with JSON test plan generation and validation.