「method」の検索結果

Volatility Smile reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.

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Hedging Scenarios reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.

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Earnings IV Crush reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.

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Supply-chain Bottlenecks reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.

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Put-spread Backtest reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.

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Strategy Builder reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.

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Problem-solving strategies for gradient methods in optimization

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Apply plugin knowledge base updates to an existing generated system. Consults the Ars Contexta research graph for methodology improvements, proposes skill upgrades with research justification. Never auto-implements. Triggers on "/upgrade", "upgrade skills", "check for improvements", "update methodology".

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Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (→ See `samber/cc-skills-golang@golang-benchmark` skill) or debugging workflow (→ See `samber/cc-skills-golang@golang-troubleshooting` skill).

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Apply Michael Feathers-inspired legacy-code rules when changing hard-to-test code safely with characterization tests, seams, sprout methods, or wrap methods.

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Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 6-stage recon pipeline (seed → asset expansion → enrichment → exposure analysis → convergence → operator-armed active validation) with connector-resilience and stage-vs-gating discipline, asset-graph discipline, severity rubric, confidence upgrade workflows, time budgeting, identity-fabric mapping, breach×identity correlation with per-person identity dossiers, detectability tagging, detection-aware probing, WAF/CDN bypass, vulnerability prioritization, phishing infrastructure planning, bug bounty submission, and client deliverable templates. Use when planning or executing reconnaissance against authorized targets, mapping an organization's external attack surface, investigating a person/entity, or producing client deliverables.

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Reconnaissance and methodology playbook. Use when mapping assets, discovering endpoints, fingerprinting technology, and building a structured testing plan for a new target.

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Safely change and test untested codebases using Feathers' "Working Effectively with Legacy Code". Use when the user mentions "legacy code", "no tests", "untested codebase", "how do I test this", "seams", "characterization tests", "golden master", "sprout method", "afraid to change this code", "monster method", "dependency breaking", or "inherited a messy codebase". Also trigger when changing code without tests safely, getting a class under test when constructors, statics, or singletons block it, adding features to tangled modules, or planning incremental test coverage for an old codebase. Covers the legacy-code change algorithm, seams, characterization tests, sprout/wrap, and dependency-breaking techniques. For refactoring code that already has tests, see refactoring-patterns. For day-to-day code quality, see clean-code.

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Apply named refactoring transformations to improve code structure without changing behavior. Use when the user mentions "refactor this", "code smells", "extract method", "replace conditional", "technical debt", "move method", "inline variable", "decompose conditional", or "clean up this messy code". Also trigger when cleaning up legacy code, preparing code for new features by restructuring, or identifying which transformation fits a specific code smell. Covers smell-driven refactoring, safe transformation sequences, and testing guards. For code-quality foundations, see clean-code. For managing complexity, see software-design-philosophy.

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Guided journey from a leaking conversion flow - landing page, signup, checkout, or in-app onboarding - to a measured, tested funnel. Orchestrates six skills phase by phase - lean-analytics, cro-methodology, storybrand-messaging, hundred-million-offers, influence-psychology, design-everyday-things - each phase carries its full method inline so it runs standalone, asking the user questions at every decision point and recording results in the project docs/ folder (FUNNEL.md, METRICS.md, EXPERIMENTS.md, CONVERSION-OPTIMIZATION-PLAN.md) so the journey resumes across sessions. Use when the user wants to raise conversion on a specific flow, fix checkout or signup abandonment, diagnose onboarding drop-off, or says 'people start but never finish'. With no site yet, use create-website; for a whole-site look, message, and speed audit, improve-website; if the flow converts but needs traffic, grow-website; for in-product engagement and retention, improve-app. For one framework in isolation, invoke that skill directly.

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Add another file (resource) to an EXISTING dataset in a PortalJS portal — a data dictionary, methodology, or an additional data file. Turns a single-file dataset into a multi-resource one; the showcase renders a section per resource. Use when a dataset needs a second file, such as a data dictionary, methodology doc, or an additional period's data.

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End-to-end orchestrated workflow that runs a task the way Fable ran sessions - parallel evidence subagents, one committed plan, surgical execution with an intent gate, adversarial verification agents, honest outcome-first report. Use for non-trivial multi-step tasks when the user says "/fable-loop", "run the fable loop", or "do this the way Fable would". For the rules alone without orchestration, use fable-method; for large multi-phase projects, prefer the GSD workflow and use this inside phases.

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Adversarial verification of finished work. Treats any "done" as a set of claims, then re-runs the claimed verifications, diffs what actually changed, detects weakened tests and false completion claims, and delivers an evidence-based verdict (VERIFIED / VERIFIED WITH CAVEATS / REFUTED). Use after any agent or model claims work is complete - "/fable-judge", "judge this work", "verify what it did", "did that actually work?". Also runs the fable-method trap suite against a skill or model via "/fable-judge suite TARGET".

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Discuss a domain with the user, research it from real sources, then generate a trusted skill bundle for it - a step-by-step workflow with a flowchart, a domain adapter, a trap fixture, and a smoke eval. Use when the user says "/fable-domain SECTOR", "make a skill for DOMAIN", "add a domain to the fable method", or "give a lesser model Fable's workflow for DOMAIN". The bundle is the deliverable; a workflow without its flowchart, sources, and trap is not done.

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Write and revise the Methods section of research papers to ensure reproducibility; use when preparing an IMRAD manuscript or responding to journal/reporting-guideline requirements (e.g., CONSORT/STROBE/PRISMA).

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Generates the Methods section for a meta-analysis paper, including search strategy, screening, quality assessment, data extraction, and statistical analysis.

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Matches a user’s biomedical research direction, disease problem, study aim, data modality, and resource constraints to the most relevant recent algorithms and method papers. Always search real recent algorithm literature first, prioritize the last 12 months, expand to 1–3 years only when needed, and add canonical baselines only when necessary. Every formal algorithm recommendation must include the verified primary method paper, plus published downstream papers that actually cite/use the algorithm when such papers are found, with DOI when available. Never fabricate papers, algorithm names, authors, journals, years, DOI, PMID, links, or benchmark claims. If no directly verified algorithm paper is found, say so explicitly.

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Migrate Spring Security 5 to Spring Security 6 configuration. Use when removing WebSecurityConfigurerAdapter, replacing @EnableGlobalMethodSecurity with @EnableMethodSecurity, converting antMatchers to requestMatchers, or updating to lambda DSL configuration style. Covers SecurityFilterChain beans and authentication manager changes.

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Guide for translating Python classes, inheritance, and object-oriented patterns to Scala. Use when converting Python code with classes, dataclasses, abstract classes, inheritance, properties, static methods, class methods, or design patterns.

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