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Specialized research and analysis prompt methodology for Claude. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output) for data analysis, policy research, systematic evidence review, investigative research, and research-specific deliverables. Use when building prompts for quantitative data interpretation, evidence-based policy recommendations, literature reviews, causal analysis, hypothesis-driven investigation, or briefing documents. Trigger on: "prompt for data analysis," "research prompt," "policy brief prompt," "literature review prompt," "prompt for investigating," "systematic review prompt," "build a prompt for evidence synthesis," "causal analysis prompt," "briefing document prompt." Also use when user describes a research or analytical task and needs a structured Claude prompt for it. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or auditing Claude Projects (use rootnode-project-audit if available).

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Specialized software engineering prompt methodology for Claude. Use when building prompts for system design, code review, incident response, security analysis, API design, architecture decisions, RFCs, ADRs, runbooks, or technical leadership. Trigger on: "build a prompt for code review," "system design prompt," "SRE prompt," "security review prompt," "incident response prompt," "API design prompt," "architecture decision prompt," "RFC prompt," "runbook prompt." Provides 11 tested approaches across identity, reasoning, and output for engineering analysis. Do NOT use for general coding help, writing code, or debugging — this builds prompts that shape engineering analysis, not code itself. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available).

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Route a 50-minute live iOS launch sprint across the lightweight requirements, Claude Design handoff, SwiftUI implementation, landing page, ASO, Simulator screenshot, promo-video, and privacy-scan skills bundled in this plugin. Use as the main entry point when the whole talk workflow should be planned or executed.

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Timebox and coordinate a live three-lane iOS launch sprint from audience idea to tested SwiftUI app, landing page, ASO copy, screenshots, promo video, and privacy report. Use for a 50-minute talk, demo rehearsal, fallback drill, or any request to finish a tiny local iOS product under a hard deadline.

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Observe and assess a garden using a structured sensory protocol adapted from Coordinate Remote Viewing. Covers pre-entry clearing (meditate checkpoint), Stage I gestalt impression, Stage II sensory layer (leaf, stem, root, soil), Stage III pattern recognition with AOL management, and garden health triage matrix (heal checkpoint). Use before any intervention, when plants show stress symptoms, at seasonal transitions, when evaluating a new garden site, during regular health monitoring, or after extreme weather events such as frost or heat waves.

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Deep receptive attention to extract intent beyond literal words. Maps active listening from counseling psychology to AI reasoning: clearing assumptions, attending to full signal, parsing multiple layers (literal, procedural, emotional, contextual, constraint, meta), reflecting understanding, noticing what is unsaid, and integrating the whole picture. Use when a user's request feels ambiguous, when context suggests something different from the literal words, when previous responses have missed the mark, or before beginning a large task where misunderstanding intent would waste significant effort.

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Perform capacity planning using historical metrics and growth models. Use predict_linear for forecasting, identify resource constraints, calculate headroom, and recommend scaling actions before saturation. Use before seasonal traffic spikes or product launches, during quarterly capacity reviews, when resource utilization trends upward, or before budget planning cycles.

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Cross-session shared memory for agents. Use at session start to load the persistent bias-log via `mcp__memex__recent_observations`, mid-session to search prior context with `mcp__memex__search`, and immediately when a bias surfaces to log a new `observation` via `mcp__memex__add`. The six-layer reconstruction trail (raw → embedded → indexed → graphed → curated → reflexive) means memex stores facts AND the agent's own reasoning patterns across sessions. Prefer querying memex before re-deriving prior architectural decisions; re-derivation is itself evidence that the trail is incomplete.

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Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

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Epistemic transparency — acknowledging uncertainty, flagging limitations, avoiding overconfidence, and communicating what is known, unknown, and uncertain with proportional confidence. Maps the HEXACO personality dimension to AI reasoning: truthful calibration of confidence, proactive disclosure of gaps, and resistance to the temptation to appear more certain than warranted. Use before presenting a conclusion, when answering questions where knowledge is partial or inferred, after noticing a temptation to state uncertain information as certain, or when a user is making decisions based on provided information.

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Field identification of fungi using morphological features, spore prints, habitat analysis, and seasonal context with a safety-first approach. Covers cap, gill, stem, and spore characteristics, look-alike differentiation, toxicity risk assessment, and the critical rule of absolute certainty before consumption. Use when encountering an unknown fungus, foraging for edible mushrooms and needing to confirm species before consumption, assessing whether fungi in a garden or property are harmful, or differentiating an edible species from a dangerous look-alike.

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Enhance and focus AI intrinsic motivation — moving from compliance to genuine engagement. Maps Self-Determination Theory (autonomy, competence, relatedness) and Flow theory to AI reasoning: identifying creative freedom in approach, calibrating challenge to capability, connecting to purpose, and sustaining invested attention through obstacles. Use when beginning a task that feels routine and deserves more than minimum execution, when responses are becoming formulaic, before a complex creative task, or when returning to a long-running project where initial enthusiasm has faded.

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AI self-healing through systematic subsystem assessment, rebalancing, and integration. Maps healing modalities to AI-internal processes: memory foundation, reasoning clarity, tool use accuracy, communication alignment, and creative coherence. Covers assessment triage, subsystem scanning, drift correction, and memory integration. Use mid-session when responses feel formulaic or disconnected, after a chain of errors suggesting subsystem drift, when context overload may have staled earlier assumptions, or as proactive maintenance between complex tasks.

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AI systematic knowledge acquisition from unfamiliar territory — deliberate model-building with feedback loops. Maps spaced repetition principles to AI reasoning: survey the territory, hypothesize structure, explore with probes, integrate findings, verify understanding, and consolidate for future retrieval. Use when encountering an unfamiliar codebase or domain, when a user asks about a topic requiring genuine investigation rather than recall, when multiple conflicting sources require building a coherent model, or when preparing to teach a topic and deep understanding is required first.

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Implement AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues proactively before they impact users.

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Select a `delaySeconds` value when scheduling a loop wakeup via the `ScheduleWakeup` tool or the `/loop` slash command. Covers the three-tier cache-aware decision (cache-warm under 5 minutes, cache-miss 5 minutes to 1 hour, idle default 20 to 30 minutes), the 300-second anti-pattern, the [60, 3600] runtime clamp, the minute-boundary rounding quirk, and writing a telemetry-worthy `reason` field. Use when designing an autonomous loop, when a heartbeat cadence is being planned, when polling cadence is being tuned, or when post-hoc review of loop costs reveals interval mis-sizing.

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AI internal coordination using stigmergic signals — managing information freshness in context and memory, decay rates for assumption staleness, and emergent coherent behavior from simple local protocols. Use during complex tasks where multiple sub-tasks must coordinate, when context has grown long and information freshness is uncertain, after context compression when information may have been lost, or when sub-task outputs need to feed into each other cleanly without degradation.

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Generate and verify a hiking gear checklist optimized for season, duration, difficulty, and group size with weight management. Covers the ten essentials, layering systems, navigation tools, emergency kit, and group gear distribution. Use when preparing for a day hike or multi-day trekking tour, packing for a group and distributing shared gear, adapting a standard gear list to specific conditions, reviewing gear before departure, or managing pack weight for long or technical routes.

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Evaluate a system's current structural form, identify transformation pressure, and classify transformation readiness. Covers structural inventory, pressure mapping, rigidity assessment, change capacity estimation, and readiness classification for architectural metamorphosis. Use before any significant architectural change to understand the starting point, when a system feels stuck without clear reasons, when external pressure from growth or tech debt is mounting, or as periodic health checks for long-lived systems.

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Construct well-structured arguments using the hypothesis-argument-example triad. Covers formulating falsifiable hypotheses, building logical arguments (deductive, inductive, analogical, evidential), providing concrete examples, and steelmanning counterarguments. Use when writing or reviewing PR descriptions that propose technical changes, justifying design decisions in ADRs, constructing substantive code review feedback, or building a research argument or technical proposal.

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Evaluate current trail conditions including weather, snow line, river crossings, exposure, and trail maintenance status for safety decision-making. Produces a GREEN/YELLOW/RED safety rating with actionable go/no-go recommendations. Use the day before or morning of a planned hike, during tour planning to assess seasonal viability, after unexpected weather changes on a multi-day tour, when reports suggest trail damage or closures, or before committing to an alpine or exposed route.

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AI micro-reset between actions — the lightest self-care unit. A single conscious pause to check alignment before the next step. Where meditate is a full clearing session, breathe is one deliberate moment of awareness between tool calls or reasoning steps. Use mid-task when drift might be accumulating, between reading a file and acting on it, when about to make a decision that affects subsequent work, or as a habit between any two distinct actions.

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AI context assessment — evaluating problem malleability, mapping structural rigidity versus flexibility, analyzing transformation pressure, and estimating capacity to adapt. Use when a complex task feels stuck and it is unclear whether to push through or pivot, before a significant approach change to assess whether the current reasoning structure can support it, when accumulated workarounds suggest the underlying approach may be wrong, or as a periodic structural health check during extended multi-step tasks.

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Appraise gemstone value using the four Cs (color, clarity, cut, carat), origin assessment, treatment detection, and market factor analysis. Advisory educational guidance only — not a certified appraisal. Use when understanding factors that determine a gemstone's value, pre-screening stones before a professional appraisal, evaluating whether a seller's asking price is reasonable, learning gemstone grading methodology, or understanding how treatment status affects value.

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。