Create interactive data exploration experiences with drill-down, cross-filtering, correlation discovery, animated transitions, and responsive layouts. Use this skill whenever the user wants to explore, visualize, or interact with data — dashboards, charts, data stories, KPI monitors, heatmaps, comparison views, funnel or cohort breakdowns. Trigger when the user says "show me the data", "build a dashboard", "visualize trends", "find patterns", "compare metrics", or uploads CSV/JSON/Excel wanting insight. Also trigger for real-time displays, animated charts, interactive filtering, cross-filtering, sparklines, data tables with sorting/search, or any multi-view coordinated visualization. NOT for static one-off charts or pure statistical computation with no visual output.
Enforces a task-based coordination protocol for AI agents editing a shared repository. Every coding session MUST begin by opening a task, declaring target files and objectives, and checking for file locks held by other agents. The agent MUST announce completion and release locks when done. Trigger this skill whenever an AI agent is about to write, edit, refactor, delete, or move any file in a shared codebase. Also trigger on: "start coding", "open a task", "edit files", "begin work on", "implement", "fix bug in", "refactor", "modify", "update code", "coordinate agents", "lock file", "unlock file", "check locks", "task status", "agent coordination", "multi-agent editing", or any request that results in file modifications within a repository. If in doubt about whether another agent might be working on the same repo, trigger this skill. The cost of coordination is low; the cost of a merge conflict or overwritten work is high.
Review prose for low-substance writing ("slop") using a two-tier diagnostic. Tier 1: surface cues (vocabulary, sentence patterns, structure, decorative performance). Tier 2: substance failures (abstraction without specifics, invocation without application, intellectual dishonesty, unfalsifiability). Tier 2 dominates Tier 1. Trigger whenever the user asks to "check for slop", "review writing quality", "detect AI writing", "run anti-slop", "check my writing", "audit this text", "is this filler", "does this say anything", "this reads like AI", "too fluffy", "strip the slop", "make this real", or any variation of assessing whether writing does intellectual work versus performing the shape of thought. Also trigger for self-editing or reviewing Claude's own output for substance. If text is uploaded and the user asks whether it is substantive or worth reading, use this skill.
Analyze architectural implications of design choices across any domain — software, business, infrastructure, data, or process design. Produces a decision report with SVG visualizations (option-space maps, enables/blocks flows, composition matrices), a Markdown document, and interactive follow-up suggestions. Use this skill when the user asks about trade-offs, design decisions, "should I use X or Y", "pros and cons", "what does choosing X block or enable", "help me decide between", "decision matrix", "option analysis", "downstream effects", or any request to understand the consequence landscape of a choice. Also trigger when the user pastes technical context and asks what the options or trade-offs are. If the user wants structured analysis — not just a list of pros and cons — this is the skill.
Read, write, validate, trace, test-derive, translate, and explain Behavior Spec Language (BSL) specifications. Use this skill whenever the user mentions "BSL", "behavior spec", "behavioral specification", ".bsl", or asks to specify observable application behavior declaratively. Also trigger when the user asks to "write a spec for [feature]", "generate test cases from a spec", "trace a rule evaluation", "translate TypeScript to BSL" (or vice versa), "validate this spec", "lint this spec", "explain this spec", "review this spec", or any request involving declarative behavior specifications with rules, invariants, side effects, and state machines. If the user uploads a file with BSL-like syntax (SPEC header, INPUT/STATE/OUTPUT/RULES sections), use this skill even if they don't say "BSL" explicitly.
Map any content — articles, news, books (fiction and non-fiction), scriptures, legal filings, transcripts, technical documentation, academic papers — into a valid Claim Formalization Schema (CFS) v0.5.0 JSON instance. Use this skill whenever the user asks to "formalize", "extract claims from", "map to CFS", "analyze the argument structure of", "decompose the logic of", or otherwise convert textual content into structured claim graphs. Also trigger when the user uploads or pastes content and asks for "propositions", "claim extraction", "argument mapping", "logical decomposition", "epistemic analysis", or "narrative formalization". This skill handles all source kinds: journalism, technical_documentation, legal_filing, policy_paper, transcript, academic_paper, fiction, scripture, and other. Even if the user does not mention CFS by name, trigger this skill whenever structured claim extraction from text is requested.
Design and implement high-quality CLI user experiences in Rust using clap. Trigger this skill whenever the user is building a command-line tool, designing CLI arguments/subcommands, implementing error handling for a CLI, adding progress indicators, color output, shell completions, interactive prompts, help text, or any aspect of how a terminal program communicates with its human operator. Also trigger when the user asks to "review my CLI", "improve the UX of my CLI", "make my CLI more user-friendly", "add --help", "design subcommands", "add shell completions", "add color output", "handle errors nicely", or any phrasing about making a command-line program pleasant, discoverable, or robust for humans. If the user is writing a Rust CLI and the conversation touches argument structure, output formatting, error messages, or user interaction — use this skill. Even if the user just says "build me a CLI for X", this skill applies because every CLI needs good UX from the start.
Apply a structured JSON patch envelope to source code files. Supports Python, JavaScript, TypeScript, Rust, HTML, and CSS. Operations: rename, replace, delete, move symbols; add/remove imports; find/replace within scope; BLOCK/ANCHOR markers; decorator injection. Produces a deterministic EXECUTION_REPORT with per-op status, content hash, and a ULID-named working copy. Use when the user says: "apply code patch", "structured edit", "patch envelope", "rename function", "rename class", "move symbol", "delete function", "add import", "remove import", "replace the body of", "refactor this function", "reorganize imports", "restructure the code", "batch edit", "surgical code change", "swap these functions", "change the signature", "inject a decorator", "find and replace in this function", or provides a JSON object with "op" fields targeting source code. Also trigger for batch mutations with an audit trail, or any targeted, auditable source code changes without rewriting the whole file.