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Found 145 Skills
Start-here router and tradecraft baseline for any OSINT investigation. Sets authorized scope, turns a vague request into an answerable intelligence question, writes a collection plan, picks the right workflow skill for the starting selector, and applies source grading and competing-hypothesis discipline. Use for "investigate this person/company/domain", "do OSINT on X", "where do I start", or any open-source intelligence, due diligence, or attribution task.
Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore. Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). ALSO USE for prompt caching setup and debugging, quota health checks and throttling diagnosis, cost attribution and tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. NOT for custom model training, Rekognition, or Comprehend.
Apply causal inference whenever the user is interpreting metrics, debugging system behavior, reading A/B test results, or trying to understand whether an observed change was caused by an action or by something else. Triggers on phrases like "X caused Y", "since we deployed this, metrics changed", "the A/B test showed a lift", "why did this metric move?", "is this correlation or causation?", "we changed X and Y improved", "how do we know this worked?", "the data shows…", or any situation where conclusions are being drawn from observational data. Also trigger before any decision based on metric interpretation — confusing correlation with causation leads to interventions that don't work and misattribution of credit. Never assume causation without applying this skill.
Identifies and removes AI writing patterns from text. Use when editing drafts, reviewing content, or rewriting text that sounds artificial. Detects inflated symbolism, promotional language, vague attributions, AI vocabulary, and structural patterns like rule-of-three overuse.
Remove signs of AI-generated writing from text (formerly human-writing). Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Shadow platform help — bot-free AI meeting assistant capturing audio + screen on macOS, on-device transcription, autopilot meeting detection, AI summaries/action items/follow-up emails, Skills system for custom post-meeting tasks. Use when setting up Shadow for the first time, Shadow not detecting meetings automatically, Shadow using too much CPU or memory on Mac, Shadow speaker attribution is wrong, Shadow screen capture not working, Shadow free tier ran out of AI meetings, choosing between Shadow and Granola or Jamie or Bluedot for bot-free recording, or exporting Shadow notes to Markdown or Zapier. Do NOT use for choosing between all AI note-takers (use /sales-note-taker) or reviewing a call for coaching (use /sales-call-review).
Remove AI generation traces from text. Suitable for editing or reviewing text to make it sound more natural and more like human writing. This is a comprehensive guide based on Wikipedia's "Signs of AI writing". It detects and fixes the following patterns: exaggerated symbolic meaning, promotional language, superficial analysis ending in -ing, vague attribution, overuse of em dashes, rule of three, AI vocabulary, negative parallelism, excessive connecting phrases.
Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.
Use when "SHAP", "Shapley values", "feature importance", "model explainability", or asking about "explain predictions", "interpretable ML", "feature attribution", "waterfall plot", "beeswarm plot", "model debugging"
Grafana Cloud cost management — usage monitoring, cost attribution by label, usage alerts, invoice management, and optimization strategies. Covers Adaptive Metrics (cardinality reduction), Adaptive Logs (log filtering), cost attribution labels, and the FOCUS-compliant billing application. Use when analyzing Grafana Cloud spending, setting up cost alerts, attributing costs to teams, reducing metric/log cardinality, or forecasting observability budgets.
Reconciliation Report Analysis Expert - Parses ONLY local Settlement Detail report files (SETTLEMENT_DETAIL_*.csv / .xlsx) for settlement amount validation, fee analysis, and reconciliation knowledge Q&A. Does NOT support Transaction Detail or Settlement Summary reports. Triggers: settlement detail parsing, settlement amount validation, fee analysis, fee model, reconciliation knowledge, interchangeFee, schemeFee, fee rules, settlement, attribution.
Generate dark-mode-compatible inline SVG data visualization charts for blog posts. Supports horizontal bar, grouped bar, donut, line, lollipop, area, and radar charts with automatic platform detection (HTML vs JSX/MDX). Enforces chart type diversity, accessible markup (role=img, aria-label), source attribution, and transparent backgrounds. Use when user says "blog chart", "generate chart", "data visualization", "svg chart", "blog graph", "visualize data", or when the blog-write workflow identifies chart-worthy data points (3+ comparable metrics, trends, before/after data).