Use when the user gives a source (URL, file, or text) to save into the project's memex — a long-term knowledge bank for AI agents. Stores the raw source, extracts cross-linked entity pages, audits backlinks, updates the index. Not for casual reads.

Use when reading the session-performance cards to find where agent effort is going — running the report over `.claude/metrics/`, interpreting it without the traps that have already produced two wrong findings, and reporting what is actually supported. Also the step before proposing any change to how work is dispatched.

OpenTelemetry Transformation Language (OTTL) expert for writing and debugging telemetry transformations in the OpenTelemetry Collector. Use when authoring or reviewing `transform`, `filter`, `tail_sampling` processor configs or `routing` connector configs, debugging OTTL syntax or semantics, transforming traces, metrics, logs, or profiles, or converting data-processing requirements into OTTL statements.

Answers questions about, summarises key insights from, and helps apply concepts from Simon Martinelli's talk "Lessons from Spec-driven Development" — providing verbatim-grounded explanations, audits, and artifact drafts. Use when the user asks about the AI Unified Process, system use cases as specs (vs user stories), self-contained systems vs microservices, skills/MCP servers/guardrails, AI-assisted ERP modernization, drift management, how architecture style impacts AI coding agents, or applying his spec-driven approach to current work.

Answers questions about, applies frameworks from, and generates artifacts based on Tammuz Dubnov's talk "When Our PM Started Writing Code: What Merge Rate Taught Us About AI Adoption." Grounds every response in the bundled transcript and outline files. Use when a user asks about AI-native org design, merge rate measurement, non-technical contributors shipping PRs, harness engineering, zero-dev-touch rate, AI adoption ROI, or wants to audit their team against Tammuz's framework, apply his concepts to their situation, draft a measurement dashboard or checklist, or understand terms like Calamarous Coding and the PM-to-engineer authority collapse.

Refactor avoidable React useEffect usage in Langfuse frontend code. Use when adding, reviewing, or removing effects; initializing forms or local UI state from query data; synchronizing client and server state; moving mutations or async workflows out of components; cleaning every effect from a frontend submodule; or reviewing whether an effect has a valid external-system owner.

Deploy a monorepo to Railway with multiple services using Dockerfiles, config-as-code, and the Railway CLI/GraphQL API. Use when deploying apps to Railway, setting up Railway projects, or fixing Railway build failures.

Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.

Use when merging delta specs from a change into the main baseline specs. Triggers: "sync specs", "merge delta specs", "update main specs", "apply the spec changes", "sync the change".

Analyzes Playwright E2E `trace.zip` archives (and bare trace JSONL when unpacked). Extracts the action timeline, network waterfall, console errors, and DOM-snapshot anchors, then identifies the highest-impact problems (flaky waits, slow selectors, network bottlenecks, hung actions, unhandled console errors, navigation churn) and proposes concrete test or app fixes ranked by measured impact. Auto-detects whether the input is a `trace.zip`, a directory of unpacked trace files, or a single `trace.trace` / `trace.network` JSONL stream. Iterates via the `/confidence` skill — if root-cause certainty is below 90%, it digs deeper before recommending a fix. Use when handed a Playwright trace, asked "why is this test flaky?", "why did the test time out?", or asked to optimise an E2E suite with evidence. Triggers on "analyze trace", "playwright trace", "e2e trace", "test flake", "why did playwright fail", "playwright timing", "/playwright-trace-analyzer".

Generates, stress-tests, and iteratively evolves ideas for a stated problem — product concepts, features, solution options, strategies — using research-grounded divergent/convergent agent loops: parallel persona generators (nominal-group simulation), independent judges scoring novelty, feasibility, impact, and fit on separate axes, and bounded recombination rounds gated by /confidence. Auto-triages run depth (quick in-context vs deep multi-agent; override with quick|deep). Use when brainstorming, exploring solution options, or pressure-testing a concept. Triggers on "brainstorm", "give me ideas", "help me come up with", "ideate on", "what could we build", "/ideate".

Use when writing or reviewing Bash scripts and shell snippets, especially for shebang selection, quoting, parameter expansion ("${VAR}" style), test expressions (`[ ]` vs `[[ ]]`), ShellCheck-guided fixes, and portability decisions across POSIX sh, Bash, and zsh compatibility targets.

Use when the user wants an elicitative interview — reaching alignment on a plan, decision, or idea by drawing out their decisions, unstated opinions, silent assumptions, and thoughts they find hard to put into words, through rounds of pointed questions. Triggers: "grill me", "interview me about this", "make sure we're aligned", "make sure you understand what I want", "help me pin down the decisions", "what am I assuming", "help me figure out what I actually want".

Fans out a question across all ready AI provider CLIs in parallel and synthesizes a single recommendation. Use when comparing AI answers, asking multiple models (Claude, Codex, Antigravity / agy, Copilot, Qwen, OpenCode), seeking multi-model consensus, or aggregating opinions on read-only questions.

Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.

Search, register, connect, transfer, and renew domain names on Vercel using the CLI or Domains Registrar API. Use for domain availability and pricing, custom domains, DNS records, nameservers, ownership verification, and domain orders.

Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.

Draws architecture diagrams as editable draw.io files with a fixed house style, C4 levels, evidence-tagged shapes, and optional multi-view identity checks. Use when producing a system context, container, component, deployment, data flow, sequence, state, or ERD, or redrawing an ASCII or Mermaid one.

Watch a brand's own product footage (screen recording, product film), pick the moment that proves each line with the user, and render those picks as a product layer — free, local. survey.py turns footage into timestamped contact sheets + scene cuts + motion so the agent can actually look at it; the agent writes a cut list (beat → source window + framing); preview.py draws it as a review sheet the user corrects round by round; cut.py renders the approved list into a silent 1080x1920 layer with the creator's area left as a plate. Also frames footage as a filmed screen (keystone, bezel, handheld drift) with dissolves between beats, and takes still screenshots as sources. Use for any format that shows real product footage beside, between or instead of a creator (split-screen, screen inserts, walkthroughs).

CloudBase PostgreSQL access-pattern and slow-query quality guidance. Use when designing how tables are read and written, eliminating per-row database calls, adding indexes for filters or joins, reviewing high-traffic data access, or explaining a slow SQL query. Not for first-time PG SDK setup, login UI, or NoSQL collections.