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Found 350 Skills
Applies DRY, YAGNI, PORO, Convention over Configuration, and KISS to Rails code; defers style to the project's linter(s). Covers structured logging, comment discipline, and path-specific rules (models, workers, services, controllers, repositories, serializers, RSpec, raw SQL). Use when designing or reviewing Rails structure, avoiding over-engineering, or aligning code with team boundaries by directory.
Identifies silent failures, inadequate error handling, and inappropriate fallback behavior in code. Zero tolerance for errors that occur without proper logging and user feedback. Triggers: When reviewing error handling, checking for silent failures, analyzing catch blocks. Examples: - "Review the error handling" -> audits all error handling in recent changes - "Check for silent failures" -> hunts for swallowed errors and empty catch blocks - "Analyze catch blocks in this PR" -> reviews every try-catch for adequacy - "Are there any hidden failures?" -> finds errors that get silently ignored
Integrates OpenTelemetry tracing, metrics, and logging into iii workers. Use when setting up distributed tracing, Prometheus metrics, custom spans, or connecting to observability backends.
Create, implement, deploy, and debug Adobe Runtime actions with consistent layout, validation, and error handling. Use this skill whenever the user needs to add actions to an App Builder project, understand action structure (params, response format, web/raw actions), configure actions in the manifest, use App Builder SDKs (State, Files, Events, database), deploy and invoke actions via CLI, debug action issues, or implement patterns such as webhook receivers, custom event providers, journaling consumers, large payload redirects, action sequence pipelines, and Asset Compute workers. Also trigger when users mention serverless functions in Adobe context, action logging, IMS authentication for actions, or cron-style scheduled actions.
AI project intelligence system. Manages .ai/ directory for rules, behaviours, sessions, incidents, memory, snapshots, and learning loops. Use when: starting a session, switching behaviour, logging an incident, saving feedback, reviewing past sessions, checking active hotfixes, managing snapshots, creating snippets/prompts. Proactively suggest when: user corrects AI behavior ("no", "don't", "wrong", "stop", "always", "never"), session ends, a mistake pattern repeats, starting work on unfamiliar code, user says "remember this" or "learn this".
Two-layer memory architecture for board meeting decisions. Manages raw transcripts (Layer 1) and approved decisions (Layer 2). Use when logging decisions after a board meeting, reviewing past decisions with /cs:decisions, or checking overdue action items with /cs:review. Invoked automatically by the board-meeting skill after Phase 5 founder approval.
Build immutable audit trails for all financial transactions with user attribution, change logging, tamper detection, and compliance-ready export for external audits
Guides privacy research engineering for safeguards—PII and sensitive-data detection research, redaction and de-identification evals, memorization and extraction risk studies, privacy benchmarks and labeled corpora, logging/retention minimization for safety pipelines, and research memos on privacy–utility trade-offs for guardrail systems. Use when measuring PII detector quality, designing privacy eval suites for moderation stacks, studying training-data leakage or prompt logging risk, or recommending privacy mitigations for safeguard models—not for SOC 2/GDPR evidence automation (compliance-engineer), legal DPIA or AI policy (ai-risk-governance), harm/toxicity classifier R&D (ml-research-engineer-safeguards), production inference gateways (ml-infrastructure-engineer-safeguards), or general non-privacy research (ai-researcher).
Design structured logging systems with context propagation. Use to ensure Python applications are observable and logs are machine-readable.
Debug logging, Debug menu, runtime pitfalls, typing-latency-sensitive paths, SwiftUI list snapshot boundaries, OS-version repros, and local visual iteration for cmux. Use when adding debug probes, diagnosing UI/runtime issues, touching terminal rendering, tab/sidebar list views, drag/drop UTTypes, or using the Debug menu.
Explains middleware concepts, patterns, and implementations. Covers server middleware, edge middleware, request/response pipelines, and common use cases like auth, logging, and CORS. Use when implementing middleware or understanding request processing pipelines.
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.