“debugging”的搜索结果

Design error handling strategies for TypeScript and Python applications — exception hierarchies, Result/Either types, retry patterns, error boundaries, and structured error logging. Use when designing error handling architecture, choosing between exceptions and Result types, implementing retry logic, or building error recovery flows. Activate on "error handling", "exception hierarchy", "Result type", "retry pattern", "circuit breaker", "error boundary", "Pokemon exception". NOT for debugging specific runtime errors, logging infrastructure setup, or monitoring/alerting configuration.

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Expert debugger for Next.js + Cloudflare Workers + Supabase stacks. Systematic troubleshooting for auth, caching, workers, RLS, CORS, and build issues. Activate on: 'debug', 'not working', 'error', 'broken', '500', '401', '403', 'cache issue', 'RLS', 'CORS'. NOT for: feature development (use language skills), architecture design (use system-architect).

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Apply rapport-based elicitation to resistant or semi-cooperative humans and agents. Use when coercive prompting or brittle handoffs damage cooperation. NOT for routine cooperative tasks, pure technical debugging, or already reliable exchanges.

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Analyze hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.

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Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).

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Use when applying Cloudflare D1 migrations, fighting Supabase migration history vs SQL execution, choosing direct psql over CLI, designing idempotent migrations, debugging schema drift between local and remote, or recovering after a half-applied migration. Triggers: supabase migration repair instructions appearing, table missing after "applied" status, "Tenant or user not found" when running psql, --remote vs --local D1 confusion, NOT NULL on a populated column, foreign key constraint failures, drift between staging and prod schemas. NOT for Mongo/document migrations, ORM-managed migrations specifically (Prisma/Drizzle have their own conventions), or pure data backfills.

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Debugs recorded DAG lineage through historical inspection, deterministic decision replay when prerequisites hold, and clearly labeled counterfactual or live re-execution. Activate on "debug DAG", "replay execution", "inspect node state", "compare runs", or "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.

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Diagnose when intuitive judgment, agent confidence, or expert routing can be trusted by classifying environment validity, feedback quality, and task-boundary fit. Use for confidence calibration, agent routing, expertise audits, and escalation design. NOT for deterministic implementation tasks, pure syntax debugging, or domains with explicit verifiable answers.

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Use when designing or fixing a Content Security Policy on a real site, choosing between nonce-based and hash-based CSP, adding strict-dynamic, debugging "Refused to execute inline script" errors, deploying CSP in report-only mode first, configuring report-to / report-uri, or auditing an existing policy for unsafe-inline / unsafe-eval / wildcards. Triggers: "CSP blocks legitimate inline script", strict-dynamic, nonce-{RANDOM}, sha256-{HASH}, object-src none, base-uri none, frame-ancestors, Trusted Types, X-Content-Security-Policy obsolete, report-only vs enforced. NOT for general HTTP security headers (HSTS, COOP/COEP), Trusted Types deep dive, CORS configuration, or building a WAF.

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Interprets GitHub Actions run status and logs, diagnoses CI failure patterns, and suggests targeted fixes. Handles dependency install failures, test timeouts, build OOM, flaky tests, and workflow misconfigurations. Activate on: 'CI failing', 'build broken', 'workflow error', 'GitHub Actions debug', 'flaky CI', 'pipeline timeout', 'CI red'. NOT for: writing new workflows from scratch (use github-actions-pipeline-builder), CI caching strategy (use ci-cache-optimizer), deployment orchestration (use devops-automator).

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Diagnose when intuitive judgment, agent confidence, or expert routing can be trusted by classifying environment validity, feedback quality, and task-boundary fit. Use for confidence calibration, agent routing, expertise audits, and escalation design. NOT for deterministic implementation tasks, pure syntax debugging, or domains with explicit verifiable answers.

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Implement an executable BDI agent cycle with events, context-guarded plans, intention stacks, selection functions, and belief revision. Use when designing or debugging AgentSpeak-style runtimes or concrete BDI program semantics. NOT for high-level BDI adoption, organizational modeling, norms, or hypertree team planning.

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Improve Coval trace quality after basic ingestion works. Use when traces are sparse, missing useful STT/LLM/TTS/tool spans, missing attributes needed for Coval built-in metrics, or when a customer wants maximum debugging and observability value from agent traces.

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Troubleshoot Coval OpenTelemetry trace ingestion, missing trace UI, sparse traces, bad simulation or conversation correlation, auth/org errors, oversized payloads, duplicate spans, and production debugging with Trace Search.

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The cross-repo scheduling system (sch_* tables, matrx-scheduler). Use when touching features/scheduling/**, /schedules or /administration/scheduling, migrations/sch_*.sql, a scheduled agent, cron, trigger, lease, or claim, or debugging ghost tasks, missed fires, double-runs, or stuck claims.

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Overlay system on @ai-matrx/summon: dialogs, sheets and windows declared once in features/overlays/definitions, opened by dispatch. Use when opening, adding, debugging an overlay, or touching features/overlays/**, SummonHost, defineOverlay, useOpenX. NOT for WindowPanel chrome (use window-panels).

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The matrx-frontend side of the matrx-extend Chrome extension bridge. Use when adding or changing a FRONTEND_RPC action handler, a window-panels deep-link the extension opens, a headless API route it calls, or debugging a silent chrome.runtime.sendMessage or Broadcast round-trip. NOT for code in matrx-extend, matrx-local, or aidream (each has its own connect-* skill).

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Diagnostic method for any bug, failure, regression, flake, or slowdown. Use before proposing a fix: something is broken, throwing, not saving, not showing, or 'worked yesterday'; on 'debug this' or 'find the root cause'; and immediately after a fix attempt did not work.

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Perform language and framework specific security best-practice reviews and suggest improvements. Use when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go), and not for general debugging or non-security tasks.

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Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI. Provides project context, component docs, and usage examples. Applies when working with shadcn/ui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for "shadcn init", "create an app with --preset", or "switch to --preset".

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This skill should be used when the user wants to "test a claim against data", "validate an empirical hypothesis", "refine a model/theory with evidence", "characterize a pattern and verify it", "演繹と帰納で検証", "仮説をデータで検証して更新", "実例と反例で確かめる", "弁証法ループ", "主張を現物で裏取り", "傾向分析を検証して精緻化", or mentions iteratively updating a hypothesis by deriving predictions and testing them against a real corpus. NOTE: Use this for VALIDATING/REFINING an empirical claim or model against real data through a derive→test→update loop. Use strong-inference for debugging an UNKNOWN cause, and devils-advocate for adversarially stress-testing a DESIGN proposal.

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This skill should be used when the user wants to "investigate a bug", "debug an issue", "figure out why something is happening", "find the root cause", "troubleshoot", "強い推論で調査", "仮説を立てて検証", "原因を特定", "バグの原因調査", "なぜ動かないか調べて", "問題を切り分け", "原因不明", "デバッグ", or mentions systematic hypothesis-driven debugging. NOTE: Use this for investigating UNKNOWN causes, not for validating design proposals (use devils-advocate for that).

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Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex code.

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[31] ANALYZE. Universal deep thinking methodology for any situation requiring quality reasoning. Use when solving problems, debugging, making decisions, analyzing code, planning, reviewing, or anytime you need thorough thinking instead of surface-level responses. Triggers on "think deeply", "analyze thoroughly", "reason carefully", "deep thinking", "understand completely", or any task requiring careful thought.

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