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Found 311 Skills
Analyze KiCad projects and PDF schematics: schematics, PCB layouts, Gerbers, footprints, symbols, netlists, and design rules. Reviews designs for bugs, traces nets, cross-references schematic to PCB, extracts BOM data, checks DRC/ERC, DFM, power trees, and regulator circuits. Every finding carries a confidence label and evidence source with trust_summary rollup. Analyzes PDF schematics from dev boards, reference designs, eval kits, and datasheets. Supports KiCad 5–10. Use whenever the user mentions .kicad_sch, .kicad_pcb, .kicad_pro, PCB design review, schematic analysis, PDF schematics, reference designs, Gerber files, DRC/ERC, netlist issues, BOM extraction, signal tracing, power budget, DFM, or wants to understand, debug, compare, or review any hardware design. Also for "check my board", "review before fab", "what's wrong with my schematic", "is this ready to order", "check my power supply", "verify this circuit", or any electronics/PCB design question.
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.
DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; add tracing or @observe; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, tracing, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval.
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.
TypeScript-native multi-agent orchestration framework that decomposes goals into task DAGs automatically with MCP and live tracing
This skill should be used when the user asks to draft or structure STR reports, suspicious transaction reports, SAR, suspicious activity reports, draft STR, STR narrative, file suspicious activity, AML STR, goAML, FinCEN SAR, suspicion narrative, or MLRO report. Guides jurisdiction-agnostic STR/SAR drafting—narrative structure (who, what, when, where, why suspicious), red flags and typologies, transaction aggregation and chronology, subject identification fields, supporting documentation checklists, quality review before filing, and escalation to MLRO/compliance—not TM rule building (aml-compliance), full LE case management, legal filing duty determination (commercial-counsel), or deep blockchain tracing (blockint skills). Complements aml-compliance, aml-cft, auditor, compliance-engineer, and commercial-counsel.
This skill should be used when the user asks for a cryptographer, cryptography review, help to choose a cipher (AES-GCM, ChaCha20-Poly1305, ECDH, RSA tradeoffs), key management, PKI design, TLS configuration, protocol security or handshake review, authenticated encryption, digital signature scheme design, post-quantum migration at architecture level, ProVerif or Tamarin modeling concepts, nonce reuse or IV misuse analysis, HKDF vs password hashing (Argon2), HSM or KMS usage patterns, secure randomness, side-channel and constant-time requirements, or cryptographic agility and algorithm deprecation—not general OWASP web app review only (information-security-engineer), secure coding checklists without crypto depth, Solidity or smart contract audits, blockchain wallet tracing, legal export classification, or shipping custom production crypto without design and review gates.
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.
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.
Generate Chi HTTP handlers following GO modular architechture conventions (request/response DTOs, use case orchestration, error handling, swagger annotations, Fx DI). Use when creating HTTP endpoint handlers in internal/modules/<module>/http/chi/handler/ for REST operations (List, Create, Update, Delete, Get) that need to decode requests, call use cases, map responses, and handle errors with proper logging and tracing.
Multi-Model Collaboration — Invoke gemini-agent and codex-agent for auxiliary analysis **Trigger Scenarios** (Proactive Use): - In-depth code analysis: algorithm understanding, performance bottleneck identification, architecture sorting - Large-scale exploration: 5+ files, module dependency tracking, call chain tracing - Complex reasoning: solution evaluation, logic verification, concurrent security analysis - Multi-perspective decision-making: requiring analysis from different angles before comprehensive judgment **Non-Trigger Scenarios**: - Simple modifications (clear changes in 1-2 files) - File searching (use Explore or Glob/Grep) - Read/write operations on known paths **Core Principle**: You are the decision-maker and executor, while external models are consultants.
Load PROACTIVELY when task involves investigating errors, diagnosing failures, or tracing unexpected behavior. Use when user says "debug this", "fix this error", "why is this failing", "trace this issue", or "it's not working". Covers error message and stack trace analysis, runtime debugging, network request inspection, state debugging, performance profiling, type error diagnosis, build failure resolution, and root cause analysis with memory-informed pattern matching against past failures.