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Found 628 Skills
Three-layer PII anonymization for session transcripts (therapy, coaching, consulting, mentoring). Runs Natasha (Russian NER), OpenAI Privacy Filter, and local LLM (Ollama) in sequence for maximum coverage. Fully local by default. This skill should be used when anonymizing session transcripts, notes, or any text containing client PII before AI analysis. Triggers on "anonymize", "redact PII", "anonymize session", "protect client data", "strip personal data", "anonymize transcript".
Build a personalised voice profile inside a Cowork project from a short interview plus 3 to 5 sample pieces of writing. Works for any content format: LinkedIn posts, newsletters, essays, emails, blog posts, tweets, or any other published writing. Use this skill at the start of any Cowork project where the user wants Claude to learn who they are and how they write before drafting new content. Trigger whenever the user says "build my voice", "learn my voice", "set up my content system", "onboard me", "train on my writing", "train on my posts", "I want Claude to sound like me", or drops a batch of writing samples into chat at the start of a project. Also trigger for first-time Cowork users who need a voice foundation before writing anything. Always produces two files (about-me.md and voice.md) saved into the project root.
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis with two-column tables. Left column preserves the chapter content; right column maps every idea to the reader's actual life using brain context. Output is a single brain page at media/books/<slug>-personalized.md plus an optional PDF via brain-pdf.
Cognitive memory management — encode, recall, forget, set reminders, and maintain long-term knowledge using personality-modulated memory.
This skill should be used to generate realistic, persona-consistent synthetic coaching and therapy session transcripts for evals, demos, and training data. It produces fictional but believable coach/client (or therapist/client) dialogue grounded in a chosen modality (ICF/GROW coaching, CBT, IFS parts work, ACT/motivational interviewing) and exports to Fathom/Granola transcript style, plain dialogue, structured JSON, or Obsidian markdown. Triggers on requests like "generate a synthetic coaching session", "make fake therapy transcripts for evals", "create demo session transcripts", "synthetic CBT dialogue", "persona-consistent coaching transcript", "test data for my session summarizer", or "mock coaching call".
Use this skill when the docs-impact-classifier returns a structural verdict, signalling that the documentation TOC must change to accommodate the PR. Proposes TOC deltas (new pages, moves, merges) and emits new-page outline stubs that the doc-sync panel later fleshes out. Holds the 3-promise narrative (consume / produce / govern) and the persona ramps as hard constraints.
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Given a disease/condition, genomic data (germline variants, somatic mutations, expression), and optional clinical parameters, performs multi-phase analysis across 9 phases covering disease disambiguation, genetic risk assessment, disease-specific molecular stratification, pharmacogenomic profiling, comorbidity/DDI risk, pathway analysis, clinical evidence and guideline mapping, clinical trial matching, and integrated outcome prediction. Generates a quantitative Precision Medicine Risk Score (0-100) with risk tier assignment (Low/Intermediate/High/Very High), treatment algorithm (1st/2nd/3rd line), pharmacogenomic guidance, clinical trial matches, and monitoring plan. Use when clinicians ask about patient risk stratification, treatment selection, prognosis prediction, or personalized therapeutic strategy across cancer, metabolic, cardiovascular, neurological, or rare diseases.
Systematically discover and define your Ideal Customer Profile with firmographic criteria, buyer personas, scoring matrices, anti-ICP signals, and validation methodology.
Quickly capture ideas, thoughts, and fleeting notes to your personal Yuque knowledge base. For personal/individual use — saves to your own repos.
Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.
Stores and retrieves persistent memory about records — contacts, companies, employees, members, and more. Handles memorization (single and batch with per-property AI extraction), semantic recall, entity digests, and data export. Use when storing data, syncing records, querying memory, or assembling context for personalization.