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Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".
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Signals scout that watches a PostHog project's most-viewed dashboards and insights for recent anomalies — sudden bursts, drops, flat-lines, and trend breaks at the daily or hourly level. It discovers what the team actually looks at (view counts, dashboard access), curates a durable watchlist in the scratchpad, and balances re-checking known high-value insights (exploit) against discovering new ones (explore) across runs, since no single run can cover a busy project. Anomalies are scored by robust deviation from each insight's own seasonality-matched baseline; it emits a finding only when a move clears the confidence bar, otherwise it updates the baseline memory and closes out empty. Self-contained peer in the signals-scout-* fleet.
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Explore PostHog's Inbox — the surface where signal reports surface as actionable issues and trends. Use when the user asks "what's in my inbox?", "what should I look at?", "which reports are actionable?", "what's PostHog flagged recently?", asks about a specific report by ID or title, or wants to see which signal sources are configured. Covers listing, filtering, and drilling into reports, plus pointers to the deeper `signals` skill when raw signals or semantic search are needed.
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Explore and diagnose a PostHog endpoint's execution logs — error messages, failed runs, cache misses, slow runs, or unexpected row counts during endpoint invocations. Use when the user says "my endpoint is failing", "show me the logs for endpoint X", "what error did endpoint Y produce", "why did endpoint Z return no rows", "is this endpoint hitting cache", or "check the last N runs". Focused on a single named endpoint's runtime log entries, not project-wide auditing or query performance profiling.
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Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
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Launch multiple explore subagents in parallel to investigate architecture, data models, auth, APIs, and deployment. Synthesize into an onboarding document.
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Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
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Explore goals, constraints, and alternative approaches before choosing a design. Use when the user requests ideation or design exploration.
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Design-first ideation workflow — explore user intent, clarify constraints, propose approaches, and produce an approved design document before planning
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Explores a CARLA map's road network through the Map/Waypoint API — a natural-language rundown of the whole map ("give me the topology"), drawing the topology to confirm the modelled map matches the visible one, enumerating junctions to resolve phrases like "the 4-way junction in the middle", nearest-waypoint lane facts, and next/previous lane navigation. Use when the user asks about the map's layout, lanes, junctions, topology, or to locate/navigate specific map elements.
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Explore possible next roles from a user's actual tasks, interests, and constraints, including adjacent roles and changes within their current workplace. Build evidence-linked career hypotheses, identify unknown duties and requirements, and propose small ways to test them. Use when someone does not know what work to pursue or asks what they could do beyond their current job; do not diagnose aptitude, rank careers, or predict salary or hiring success.
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Re-analyze an existing GitHub Issue using its current title and body, refresh the implementation plan against the latest code state, and update the Issue in place. Use when the user provides an Issue number (numeric, `#`-prefixed, or Issue URL) and wants to regenerate the code analysis via the explore-agent subagent. For reflecting comment-driven updates, use update-issue. For creating a brand-new Issue from a natural-language task description, use create-issue.
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Builds an interactive HTML dashboard visualizing the Agent Skills ecosystem by fetching live data from github.com/agentskills/agentskills. Use this skill whenever the user wants to explore, research, or visualize Skills architecture, the SKILL.md spec, ecosystem adoption, repo stats, or any Skills intelligence dashboard. Trigger when the user mentions "skills dashboard", "skills ecosystem", "skills architecture overview", "agentskills repo", or wants to analyze how different tools implement Agent Skills. Optimized for Perplexity Computer — Computer fetches all data live, no static files required.
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Detects TAD boundaries from balanced Hi-C contact matrices via the diamond-window insulation score (cooltools insulation) and HiCExplorer hicFindTADs, returning a continuous log2 insulation track, valley-prominence boundary_strength, and Li/Otsu-thresholded is_boundary flags across a list of window sizes. Covers the multi-scale window sweep (sub-TAD to compartment-domain), why the boundary is reproducible but the domain partition is not, cross-condition comparison via differential SCORE not differential partition, and the insulation-vs-compartment orthogonality. Use when calling TADs or domain boundaries, computing insulation scores, choosing a window size, ranking boundary strength, comparing boundaries across conditions, or annotating CTCF-backed boundaries; route domain rendering to hic-visualization and boundary-feature overlap to genome-intervals.
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Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condition side-by-side and log2-ratio maps, and interactive (HiGlass) vs scripted-static publication figures. Use when plotting a contact matrix, choosing a normalization or color scale, building a multi-track Hi-C figure, making a virtual 4C profile, piling up loops/boundaries, or comparing two conditions.
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Map the repository and recommend the next CodeConductor slash command. Read-only.
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Comprehensive AWS cost analysis and optimization recommendations using AWS CLI and Cost Explorer
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Index a codebase with GitNexus and serve an interactive knowledge graph via web UI + Cloudflare tunnel.
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Use when the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: "How does X work?", "What calls this function?", "Show me the auth flow"
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Execute an explicitly authorized exploratory charter and record notes, findings, evidence, and follow-up without claiming false coverage.
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Use before any creative work - creating a feature, building a component, adding functionality, or changing behavior - to explore intent and design before implementation
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Analisa custo AWS e identifica desperdício em EC2, RDS, S3, EKS, EBS, Elastic IP, NAT Gateway e tráfego. Use quando o usuário disser "custo", "fatura", "está caro", "FinOps", "reduzir gasto AWS", "Cost Explorer", "otimizar custo". Quase todo leitura — seguro por natureza.
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Facilitate ideation, explore alternatives, and refine concepts through structured brainstorming techniques before implementation. Use when starting new projects, designing features, exploring solutions, evaluating tradeoffs, challenging assumptions, generating creative ideas, planning architecture, or refining rough concepts into detailed designs.
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Design shotgun: generate multiple AI design variants, open a comparison board, collect structured feedback, and iterate. Standalone design exploration you can run anytime. Use when: "explore designs", "show me options", "design variants", "visual brainstorm", or "I don't like how this looks". Proactively suggest when the user describes a UI feature but hasn't seen what it could look like. (gstack)
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