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Set countdown timers by duration — acknowledges the request and notes that firing requires the cron system.

日本語の概要は準備中です。原文の説明を表示しています。

Used by the Reviewer agent during Phase 9 (DesignVerify) and Phase 11 (Quality) to evaluate frontend code. Assesses visual hierarchy, spacing consistency, accessibility, and detects formulaic 'AI slop' design patterns, providing actionable feedback to the Developer.

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Used by the Developer agent during Phase 8 (Implement) when building web components, pages, or UI features. Provides strict aesthetic guidelines to create distinctive, production-grade frontend interfaces that avoid generic 'AI slop' aesthetics.

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Used by the Lead agent during Phase 6 (Brainstorm) to collaboratively explore user intent, requirements, and alternative approaches before locking in the architecture. Helps turn raw ideas into fully formed specs through natural dialogue.

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Drive a real browser from the command line — open pages, snapshot the interactive elements as refs, click and fill by ref, read text, run JS, screenshot. Use when a task needs a live browser: checking a page renders, walking a login or checkout flow, scraping something that only exists after JavaScript runs, or reproducing a UI bug.

日本語の概要は準備中です。原文の説明を表示しています。

Protocol for agents to write structured outputs that survive session resets and are findable by the orchestrator.

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Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).

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Expert in temporal event detection, spatio-temporal clustering (ST-DBSCAN), and photo context understanding. Use for detecting photo events, clustering by time/location, shareability prediction, place recognition, event significance scoring, and life event detection. Activate on 'event detection', 'temporal clustering', 'ST-DBSCAN', 'spatio-temporal', 'shareability prediction', 'place recognition', 'life events', 'photo events', 'temporal diversity'. NOT for individual photo aesthetic quality (use photo-composition-critic), color palette analysis (use color-theory-palette-harmony-expert), face recognition implementation (use photo-content-recognition-curation-expert), or basic EXIF timestamp extraction.

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Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).

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Use when building Grafana dashboards backed by Prometheus, Loki, or Tempo, designing PromQL/LogQL queries, wiring template variables, setting alert rules, building SLO dashboards, or maintaining dashboards as code. Triggers: rate() vs increase() confusion, irate vs rate, label_replace, recording rules, alerting rule expressions, multi-dimensional template variables, ad-hoc filters, dashboard JSON model, provisioning via Terraform/grafonnet, p99 / histogram_quantile usage. NOT for Datadog/New Relic dashboards (vendor-specific), Grafana plugin development, or Loki ingestion pipeline tuning.

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Implementing distributed tracing with W3C Trace Context — the byte-precise `traceparent` and `tracestate` header formats, OpenTelemetry's W3CTraceContextPropagator as the modern default (replacing X-B3-* and X-Datadog-*), head vs tail sampling, and HTTP→SQL trace propagation via sqlcommenter. Grounded in the W3C REC and OpenTelemetry specs.

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Matches natural language task descriptions to appropriate skills using semantic similarity, ranks candidates by fit and performance history, and maintains the skill catalog. Use when assigning skills to DAG nodes, searching for the right skill for a task, ranking competing skills, or browsing the skill catalog. Activate on "find skill", "match skill", "which skill", "skill for this task", "skill catalog", "rank skills", "best skill". NOT for executing DAGs (use dag-runtime), creating skills (use skill-architect), or grading skills (use skill-grader).

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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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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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Ranks skill matches by fit, performance history, and contextual relevance. Applies multi-factor scoring including success rate, resource usage, and task alignment. Activate on 'rank skills', 'best skill for', 'skill ranking', 'compare skills', 'optimal skill'. NOT for semantic matching (use dag-semantic-matcher) or skill catalog (use dag-skill-registry).

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Design and implement systematic checklists that reduce errors by 30-50% in complex, high-stakes domains (medicine, aviation, construction, finance). NOT for simple tasks or when comprehensive instruction is needed.

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Expert in cluttering -- the lesser-known fluency disorder characterized by rapid/irregular speech rate, excessive disfluencies, and frequent ADHD comorbidity. Covers differential diagnosis from stuttering, assessment protocols, therapy approaches (rate control, self-monitoring, pragmatic awareness), technology-assisted intervention, and AI tools for people who clutter. Activate on 'cluttering', 'rapid speech disorder', 'speech rate too fast', 'speech disfluency not stuttering', 'disorganized speech', 'ADHD speech problems', 'cluttering vs stuttering', 'speech intelligibility', 'mazed speech', 'telescoping words'. NOT for stuttering-only treatment (overlap is common but they are distinct), hearing disorders, or voice disorders (pitch, resonance).

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Describe patterns that may indicate bad-faith or manipulative communication including gish gallop, whataboutism, tone policing, motte-and-bailey, sealioning, lying by omission, deflection, and strategic evasion. Use when the user suspects manipulation, notices repeated non-engagement, wants to analyze debate tactics, or mentions 'bad faith', 'deflection', 'manipulation', 'dodging the question', 'avoiding the issue', 'gaslighting', 'moving goalposts'. Describes behavioral patterns without making psychological diagnoses.

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Capstone/orchestration skill — build an M-Agent + N-Human cooperative IDE in Rust gpui (the Harbor): many agents and humans co-editing the same files as co-equal CRDT replicas, governed by claims/guard/salvage, across LAN/shared/remote harbors. The INDEX that dispatches into the sibling rust skills. Use when building the collaborative editor, the agent-fleet console, multiplayer editing with agents-as-peers, or any slice of the Harbor. Trigger on: cooperative IDE, collaborative editor, multiplayer editor, agents and humans co-editing, gpui IDE, Loro CRDT editor, harbor editor, claims/salvage, "build the cooperative IDE". NOT for: a single non-collaborative gpui screen (compose the siblings directly), web editors, or non-editor apps.

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Design roadside signs and large-format ads for businesses, especially automotive dealers. Use for pylon signs, monument signs, lot banners, highway billboards, digital roadside boards, service-lane signage, used-car event signs, lease or finance campaign concepts, and other driver-at-speed messaging where legibility, offer hierarchy, and dealer ad compliance matter. Activate on "road sign", "billboard", "dealership sign", "car dealer ad", "pylon sign", "monument sign", "service lane signage", "automotive billboard", "roadside ad", "dealer lease offer", or "drive-by signage". NOT for fabrication drawings, permitting or local sign-code review, web or social ad campaigns, or full brand identity systems.

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Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

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Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

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Apply normative BDI reasoning to agents that must detect norms, choose which commitments to internalize, and resolve conflicts by comparing consequences. Use when obligations, prohibitions, or policies collide in autonomous systems. NOT for simple fixed-priority rules, pure constraint satisfaction, or domains where no real normative conflict exists.

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Structured step-by-step reasoning before acting, to avoid guessing. For complex debugging, multi-step analysis, hypothesis checks. NOT for being stuck on approach (mk:problem-solving).

日本語の概要は準備中です。原文の説明を表示しています。