Loading...
Loading...
Found 253 Skills
Builds composable, pipeable function chains on the iii engine. Use when building functional pipelines, effect systems, or typed composition layers where each step is a pure function with distributed tracing and retry.
Use the Alchemy MCP server (`https://mcp.alchemy.com/mcp`) for live blockchain data and admin work when MCP is wired into your AI client and the Alchemy CLI is NOT installed locally. Exposes 159 tools across 100+ chains for token prices, NFT metadata, transactions, simulation, tracing, account abstraction, Solana DAS, and app management. Use for live querying, analysis, admin work, or on-machine agent work — not for application code that ships to production. For application code, use the `alchemy-api` skill (with API key) or `agentic-gateway` skill (without). When the CLI is also installed locally, prefer `alchemy-cli` instead.
Salesforce Data Cloud Retrieve phase. Use this skill when the user runs Data Cloud SQL, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. TRIGGER when: user runs Data Cloud SQL, describe, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. DO NOT TRIGGER when: the task is standard CRM SOQL (use querying-soql), segment creation or calculated insight design (use segmenting-datacloud), or STDM/session tracing/parquet analysis (use observing-agentforce).
Salesforce Data Cloud Harmonize phase. Use this skill when the user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. TRIGGER when: user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. DO NOT TRIGGER when: the task is only about streams/DLOs (use preparing-datacloud), segments/insights (use segmenting-datacloud), retrieval/search (use retrieving-datacloud), or STDM/session tracing (use observing-agentforce).
Investigate a bug observed in the running application by reading the generated code in plain_modules/, tracing the issue back to the specs, and fixing only the .plain files. Generated code is never modified. Use when the user reports unexpected behavior, visual glitches, crashes, or incorrect logic in the app.
This skill should be used when the user asks for markup detection, detect manipulation, image tampering, deepfake detection, document integrity, hidden markup, metadata forensics, EXIF analysis, content authenticity, synthetic media, altered image, C2PA, or provenance verification across documents, images, and video. Guides workflow-level assessment of visual tampering indicators (splicing, cloning, inconsistent lighting or shadows, compression artifacts), metadata and provenance checks (EXIF, hashes, source chain), document revision and hidden markup (tracked changes, comments, invisible text), synthetic-media and deepfake red flags, watermarking and content-credentials concepts, and structured reporting with confidence levels and explicit limitations—not training detection models (ml-research-engineer-safeguards), cryptographic watermark design (cryptographer-specialist), full digital forensics lab attribution or legal conclusions, or blockchain-only tracing unless the user scopes on-chain context.
Verify a research claim or academic citation by tracing it through publication → methodology → raw data → independent replication. Routes through perplexity-research for the actual web lookup, then formats results as a citation-checked brain page. Use when a book/article/conversation cites a study and you want to confirm the claim is real, replicated, and accurately characterized.
Unity shaders, materials, and rendering pipelines (URP/HDRP/Built-in). PROACTIVELY activate for: (1) writing shaders in Shader Graph, HLSL, or ShaderLab, (2) URP and HDRP shader authoring, (3) custom render pipeline work (SRP), (4) lighting setup (baked vs realtime, lightmaps, Global Illumination), (5) post-processing stacks, (6) reflection probes and light probes, (7) custom render features and full-screen passes, (8) shader stripping and variant management, (9) compute shaders, (10) ray tracing in HDRP. Provides: Shader Graph templates, HLSL snippets, URP/HDRP differences, lighting setup recipes, render-feature examples, and shader-variant guidance.
Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use this when debugging production issues or working with Datadog observability.
Observability audit worker (L3). Checks structured logging, health check endpoints, metrics collection, request tracing, log levels. Returns findings with severity, location, effort, recommendations.
This skill should be used when adding error tracking and performance monitoring with Sentry and OpenTelemetry tracing to Next.js applications. Apply when setting up error monitoring, configuring tracing for Server Actions and routes, implementing logging wrappers, adding performance instrumentation, or establishing observability for debugging production issues.
Systematic debugging methodology with root cause analysis. Phases: investigate, hypothesize, validate, verify. Capabilities: backward call stack tracing, multi-layer validation, verification protocols, symptom analysis, regression prevention. Actions: debug, investigate, trace, analyze, validate, verify bugs. Keywords: debugging, root cause, bug fix, stack trace, error investigation, test failure, exception handling, breakpoint, logging, reproduce, isolate, regression, call stack, symptom vs cause, hypothesis testing, validation, verification protocol. Use when: encountering bugs, analyzing test failures, tracing unexpected behavior, investigating performance issues, preventing regressions, validating fixes before completion claims.