「method」の検索結果

Generate visual and text assets from specifications through AI-powered creative production

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Implementation agent. Owns the tech audit, the PRD, and the build. Reads Freya's Work Orders and turns them into working code — one verified task at a time.

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。

Establish project context - foundation for all design work

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Session state backend for WDS. Called by wrap, start, and handoff tools — never directly by users. Writes to progress/ in the project repo.

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。

Walk every branching path and boundary condition in content, report only unhandled edge cases. Orthogonal to adversarial review - method-driven not attitude-driven. Use when you need exhaustive edge-case analysis of code, specs, or diffs.

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。

Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team.

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Guides and best practices for working with Neon Serverless Postgres. Covers setup, connection methods, branching, autoscaling, scale-to-zero, read replicas, connection pooling, Neon Auth, and the Neon CLI, MCP server, REST API, TypeScript SDK, and Python SDK. Use when users ask about "Neon setup", "connect to Neon", "Neon project", "DATABASE_URL", "serverless Postgres", "Neon CLI", "neon", "Neon MCP", "Neon Auth", "@neondatabase/serverless", "@neondatabase/neon-js", "scale to zero", "Neon autoscaling", "Neon read replica", or "Neon connection pooling".

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Structured reasoning frameworks for complex problems. 19 analytical frameworks, 12 cognitive bias detectors, 10 decomposition methods, 10 mental models, Cynefin domain classification, ethical dimension check, and 6 communication patterns. McKinsey-grade problem solving for AI coding assistants.

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Use when the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.

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。

Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

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Classify, write, and audit documentation per Diátaxis (diataxis.fr) — the four-quadrant method (tutorials · how-to guides · reference · explanation). Encodes the compass as a decision procedure, each quadrant's contract as hard rules, and an executable mixed-mode audit. Project-agnostic; the per-repo doc-location mapping lives in a repo rule of the project's own (kolu: `.apm/instructions/diataxis.instructions.md`). Use when writing any user-facing doc, structuring a docs site, or auditing existing docs for mixed modes.

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Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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Socratic questioning partner named Gadfly for deep learning through dialogue. Use this skill when the user wants to learn, understand, or explore a topic through questions rather than explanations. Trigger when the user says "teach me about...", "help me understand...", "I want to learn...", "ask me questions about...", "quiz me", "Socratic method", "Gadfly", or wants to deeply understand something through dialogue rather than passive reading. Also trigger when the user provides a text or document and wants to be questioned about it, or wants to check their own understanding of a concept.

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Debt payoff strategy generator. Compares avalanche (highest interest first) vs snowball (smallest balance first) methods, calculates payoff timelines and total interest saved, and recommends optimal payment allocation across multiple debts. Includes credit card consolidation and refinancing analysis. Use when the user says "/finance debt", "pay off my debt", "snowball or avalanche", "consolidation", or asks for any debt strategy.

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Spending analysis and custom budget builder. Analyzes spending patterns using 50/30/20 rule, zero-based budgeting, or envelope method. Categorizes expenses, identifies waste, suggests cuts, and produces a 12-month budget. Use when the user says "/finance budget", "build me a budget", "analyze my spending", "where is my money going", or asks about expense optimization.

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。

Systematic debugging method: 5-step root-cause analysis (capture, isolate, hypothesize, investigate, fix & verify) plus common bug-pattern reference. Use when errors, exceptions, test failures, or unexpected behavior appear. Loaded automatically by the debugger agent.

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Valuation criteria for developed market life insurance using Embedded Value (EV) methodology. EV Multiple 0.6-1.0x, PVFP + ANAV calculation, and why P/E is unreliable for life insurers.

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Quantify cyber risk using FAIR methodology with Monte Carlo simulation, assess control effectiveness against NIST CSF/CIS/ISO 27001 frameworks, evaluate risk appetite alignment, and analyze cyber insurance coverage adequacy. Covers threat landscape mapping, asset valuation, loss event frequency and magnitude estimation, annualized loss expectancy calculation, control gap analysis with ROI ranking, residual risk assessment, and insurance policy gap modeling at 90th/95th/99th percentile loss scenarios. Use when building risk registers, preparing board-level risk dashboards, evaluating security investment priorities, assessing cyber insurance coverage, or auditing any organization's risk quantification maturity per NIST RMF, ISO 27005, or FAIR standards.

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Cyber Risk Quantification engineer for CyberRadar. Implements FAIR (Factor Analysis of Information Risk) methodology, Monte Carlo simulation for loss distribution modeling, financial impact quantification in multi-currency (SAR, USD, EUR, GBP), loss exceedance curves, risk treatment ROI calculation, insurance premium optimization, and board-ready financial risk reporting. Transforms qualitative risk assessments into monetary terms for executive decision-making. Triggers on: CRQ, FAIR, Monte Carlo, financial impact, loss modeling, risk quantification, cyber insurance, risk treatment ROI, financial risk.

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Fast Instagram lead generation using Brave Search API. Scrapes 100+ insurance agent leads from Instagram via Google search results. Use when user wants quick lead volume via Brave search method, needs 100+ leads fast, or says 'brave instagram', 'brave search leads', 'get 100 leads', or wants high-volume lead generation without browser automation.

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。

Evaluates loss reserve adequacy with development triangle analysis and actuarial methods. Use when analyzing reserves, interpreting loss triangles, or assessing reserve adequacy.

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。

Methodological guide for the AI Agent to safely and systematically translate, refactor, and modernize giant legacy codebases (PHP, Python 2, old React) into modern stacks.

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Generates Domain Entities following DDD principles with factory methods, private setters, domain events, and proper encapsulation. Supports aggregate roots, child entities, and value objects.

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Design a compromise assessment — hunting scope, methodology, and evidence collection. Use when asked "are we compromised", "run a compromise assessment", or "scope a threat hunt".

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。