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Found 5,969 Skills
Guides building and deploying Atlassian Forge Teamwork Graph connector apps that ingest external data into Atlassian's Teamwork Graph, making it searchable in Rovo Search and surfaced in Rovo Chat. Use when the user wants to build a Forge connector, ingest external data into Atlassian, connect a third-party tool (e.g. Google Drive, ServiceNow, Salesforce) to Atlassian, make external content searchable in Rovo, build a graph:connector module, use the @forge/teamwork-graph SDK, or implement onConnectionChange / validateConnection functions.
Generate comprehensive OpenSpec specifications directly from the current project state. Use when the user wants to create or populate main specs by analyzing existing code, documentation, AGENTS.md, GitHub issues, and pull requests — without going through the change/proposal workflow. Ideal for bootstrapping specs on a project that already has working code but no specs yet, or for refreshing specs to match the current implementation.
Decision frameworks for DatoCMS content modeling — schema shape, field choice, content reuse, taxonomies, content vs presentation, admin UI organization. Use for modeling *decisions*, not implementation: model vs block; single_block vs Modular Content vs Structured Text; references vs embedded blocks; taxonomy shape (flat/tree/faceted); refactoring page-shaped schemas to reusable content; fitting 300 KB / 500-block / 5-level record limits; model behaviour (singleton, draft mode, all_locales_required, sortable/tree/ordering_field, presentation_title_field, collection_appearance, inverse_relationships_enabled); field config (validator + appearance — enum + string_select, slug auto-fill, required_alt_title, structured_text allowlists, framed vs frameless single_block). Also schema review (reuse, editor ergonomics, omnichannel). *Creating* schema → `datocms-cli` or `datocms-cma`. Query/render → `datocms-cda` + `datocms-frontend-integrations`. Validators + cascade: `datocms-cma/references/schema.md`.
Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / instructions.md / queries.yml / memory pairs, then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.
Where new brain files go. Decision protocol for filing brain pages by primary subject, not by format or source. Reference for all brain-writing skills.
You must use this when critiquing academic manuscripts, evaluating methodological rigor, or providing structured reviewer feedback.
Chinese translation of Google's Agentic Design Patterns book - 21 core AI agent patterns with examples
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.
Search 78 public scientific, biomedical, materials science, and economic databases via REST APIs. Covers physics/astronomy (NASA, NIST, SDSS, SIMBAD), earth/environment (USGS, NOAA, EPA), chemistry/drugs (PubChem, ChEMBL, DrugBank, FDA, KEGG, ZINC, BindingDB), materials (Materials Project, COD), biology/genomics (Reactome, UniProt, STRING, Ensembl, NCBI Gene, GEO, GTEx, PDB, AlphaFold, InterPro, BioGRID, Gene Ontology, dbSNP, gnomAD, ENCODE, Human Protein Atlas, Human Cell Atlas), disease/clinical (COSMIC, Open Targets, ClinicalTrials.gov, OMIM, ClinVar, GDC/TCGA, cBioPortal, DisGeNET, GWAS Catalog), regulatory (FDA, USPTO, SEC EDGAR), economics/finance (FRED, World Bank, US Treasury), demographics (US Census, Eurostat, WHO). Use when looking up compounds, genes, proteins, pathways, variants, clinical trials, patents, economic indicators, or any public database API query.
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating multi-speaker podcasts from research, chatting with documents using context-aware AI, searching across materials with full-text and vector search, or running custom content transformations. Supports 16+ AI providers including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral with complete data privacy through self-hosting.
Create and maintain a product marketing context document covering positioning, ICP definition, messaging hierarchy, competitive differentiation, and value proposition. This skill creates the foundation that all other marketing skills reference. Trigger phrases: "product marketing," "positioning statement," "ideal customer profile," "ICP," "messaging hierarchy," "value proposition," "competitive differentiation," "product narrative," "use case mapping," "update our positioning," "what makes us different," "who is our customer," "messaging framework," "category design," "go to market strategy," "product story."