「phase arc」の検索結果

Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, service blueprint, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.

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Company Architect: builds a business from scratch as an OKF (Open Knowledge Format) bundle — a tree of version-controllable .md files with frontmatter type, links forming a graph, and reserved index.md/log.md, readable by humans and agents. Guides the founder through a 12-phase interview (foundation, strategy, market, financial, sales, marketing, product, operations, tech, people, legal, governance), one phase at a time, few questions per block, and generates the concepts as conformant markdown. Trigger when the user wants to create, structure, or document an entire company in folders and .md files; when they mention build my company from scratch, company as code, company knowledge base for AI to read, company wiki for agents, OKF, or knowledge bundle. In English.

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External research synthesis from Trail of Bits, SlowMist, ConsenSys, Immunefi, and Cyfrin. Use this for advanced audit methodology, Echidna/Medusa fuzzing setup, Slither custom detector writing, attack pattern deep dives, or the 4-phase learning roadmap.

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Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.

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Runs a six-phase research workflow from source bundle to publish-ready output. Use when researching an unfamiliar domain or compiling materials into one reference. Not for quick lookups or single-file reads.

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Runs a sequenced monolith-to-modular pipeline that sizes and inventories components, finds shared domain duplication, addresses flattening and hierarchy issues, analyzes coupling, then groups components into candidate domain-aligned units, with optional embedded DDD strategic analysis for bounded contexts. Use when asking how to split a monolith, size components before extraction, find duplicated domain logic, clean up module hierarchy, measure coupling between modules, or group components into services. Do NOT use for phased extraction roadmaps or prioritization without the prior analysis steps (use decomposition-planning-roadmap after this pipeline), end-to-end legacy migration strategy writeups (use legacy-migration-planner), pure infrastructure capacity sizing, or when you only need DDD without the structural pipeline (install domain-analysis standalone).

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VS-Enhanced Quantitative Design Consultant with Materials & Sampling Enhanced VS 3-Phase process: Avoids obvious experimental designs, proposes context-optimal quantitative strategies Absorbed C4 (Experimental Materials Developer) and D1 (Sampling Strategy Advisor) capabilities Use when: selecting quantitative research design, planning experimental/survey methodology, power analysis, developing materials, sampling Triggers: RCT, quasi-experimental, experimental design, survey design, power analysis, sample size, factorial design, materials, stimuli, sampling strategy

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VS-Enhanced Theoretical Framework Architect with Critique & Visualization Full VS 5-Phase process: Modal theory avoidance, Long-tail exploration, differentiated framework presentation Absorbed A3 (Devil's Advocate) critique and A6 (Conceptual Framework Visualizer) capabilities Use when: building theoretical foundations, designing conceptual models, deriving hypotheses, critiquing frameworks, visualizing models Triggers: theoretical framework, 이론적 프레임워크, conceptual model, 개념적 모형, hypothesis derivation, critique, devil's advocate, 반론, visualization, diagram

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VS-Enhanced Evidence Quality Appraiser - Prevents Mode Collapse with context-adaptive quality assessment Enhanced VS 3-Phase process: Avoids automatic tool application, delivers research-specific evaluation strategies Use when: appraising study quality, assessing risk of bias, grading evidence Triggers: quality appraisal, RoB, GRADE, Newcastle-Ottawa, risk of bias, methodological quality

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Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.

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Formal theorem proving with research, testing, and verification phases

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Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.

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Comet — OpenSpec + Superpowers dual-star development workflow. Start with /comet for automatic phase detection and dispatch to subcommands. Five phases: open → design → build → verify → archive.

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Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into `strategy.yaml` + `metadata.json`, or preflight-check interface compatibility (`edge-finder-candidate/v1`) before running pipeline backtests.

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Migrate workloads from Microsoft Azure to AWS. Triggers on: migrate from Azure, Azure to AWS, move off Azure, migrate AKS to EKS, migrate App Service or Azure VMs to AWS compute, migrate Azure SQL or Azure Database to RDS, migrate Cosmos DB to DynamoDB, migrate Azure OpenAI to Bedrock, move Azure AI or agentic workloads to AWS, estimate AWS costs for my Azure infrastructure, what-if workshop. Runs a 6-phase process: discover Azure resources from Terraform, the live `az` CLI, app code, and billing exports, then clarify, design, estimate costs, optionally reprice scenarios, generate artifacts, and collect feedback. Clarify gates Design, Estimate, and Generate; Generate is opt-in at the post-Estimate decision gate. Bicep/ARM discovery is not yet implemented; a Bicep/ARM-only workspace halts. Do not use for: GCP migrations (see gcp-to-aws), Heroku migrations (see heroku-to-aws), general AWS architecture advice (see architect-for-startups), AWS-to-Azure reverse migration, or Azure-to-Azure refactoring.

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Guided journey from a website with traffic it under-converts to a research-driven growth engine that captures more leads, persuades more buyers, sells a sharper offer, and earns referrals. Orchestrates eight skills phase by phase - cro-methodology, scorecard-marketing, storybrand-messaging, made-to-stick, influence-psychology, hundred-million-offers, contagious, one-page-marketing - asking the user questions at every decision point and recording results in the project docs/ folder (WEBSITE.md, OFFER.md, MARKETING.md, GROW-WEBSITE-PLAN.md) so the journey resumes across sessions. Use when the user wants to raise conversion on a site that already has traffic, capture more leads, sharpen a weak offer, add referral loops, or says 'my site gets visitors but nobody buys'. If a broken funnel or usability friction is the real blocker, run improve-website first; if there is no site yet, use create-website; for one leaking flow, conversion-optimization. For one framework in isolation, invoke that skill directly.

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This is a summary the advantages and disadvantages of earthquake event detection and phase picking methods, shared by leading seismology researchers at the 2025 Earthquake Catalog Workshop. Use it when you have a seismic phase picking task at hand.

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Systematic codebase exploration following the Iron Law - understand the problem before exploring code. Four phases with file-finder and web-researcher agents.

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AI DevKit · Design phase guidance for reviewing feature design against requirements. Use when the user wants to validate architecture, review design docs, resolve design trade-offs, or run dev-lifecycle phase 3.

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Use when the user wants to reverse-engineer an existing image ad into a reusable prompt template. Validates via Arcads — picks gpt-image-2 or Nano Banana at Phase 1. Triggers on "clone this ad as a template", "reverse engineer this ad", "turn this ad into a prompt", "extract a template", "make this ad reusable", "add to my prompt library", "study this ad and make a template". Input is an EXISTING ad image; does NOT trigger for fresh generation (use chatgpt-image-ad or nano-banana-image-ad).

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Annotates bacterial and archaeal genomes (isolates, MAGs, plasmids) with Bakta (active versioned databases, NCBI-compliant output) or Prokka (legacy), producing GFF3/GenBank/EMBL/FASTA with INSDC locus tags. Covers Bakta-vs-Prokka-vs-PGAP-vs-DFAST choice, light-vs-full database tiers, translation-table selection (11/4/25), archaeal and leaderless-gene caveats, the small-ORF blind spot, pseudogene-vs-phase-variation, the pangenome re-annotation trap, and submission compliance. Use when annotating a newly assembled prokaryotic genome, choosing an annotation tool, re-annotating a collection for pangenomics, or preparing annotations for NCBI/DDBJ submission.

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Use when the user is launching or running a DROPSHIPPING business end to end — the full 12-phase pipeline: product research with winning criteria, Minea and PiPiAds, supplier sourcing on AliExpress, CJ, Spocket, and Zendrop, Shopify store and app setup, the 10-ads-per-week creative pipeline, interest and lookalike targeting, 3-5x markup and BE-ROAS math, customer service, and scaling. Trigger on 'start dropshipping', 'winning product research', 'Shopify store setup', 'AliExpress supplier', 'scale my dropshipping store', 'my store gets traffic and no sales'. Not for — only the ad copy, see `05-ad-copy-global`; only the product page, see `12-landing-page-brief-global`; only the account audit, see `21-ads-audit-global`; only pricing, see `17-pricing-strategy-global`.

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Use when a phase plan should be peer-reviewed by other AI CLIs before execution, or the user asks for a cross-AI review. Invokes Gemini, Claude, Codex, or OpenCode and writes REVIEWS.md for /gsd-plan-phase --reviews.

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Use when the user asks for a visual or UI quality review of frontend code that already shipped, or passes a phase number. Produces {phase_num}-UI-REVIEW.md with a graded 1-4 assessment across six pillars.

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