「context」の検索結果

Get a PDF into the model without blowing the context window or losing structure. Native PDF beats OCR-then-text for most cases; extract-then-summarize beats native for very long docs.

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Use when the user mentions GPT Image 2.0 or GPT Image 2.5, gpt-image-2, gpt-image-2.5, gpt_image_2_5, GPT-Image-2 prompts, the Flare / Sunburst variants, a transparent-background image, or wants to generate an image with GPT Image 2.0 or 2.5. Covers when to prefer GPT Image 2.5 over 2.0 (transparent background, xhigh/max quality, extra aspect ratios — from platform and OpenAI facts), the CLI-vs-MCP disagreement on gpt_image_2, the three-format prompt taxonomy (Format A structured JSON for UI mockups and layout-dense images; Format B dense cinematic prose for single-subject scenes; Format C auto-derive meta-prompt for theme-only concepts), per-format craft patterns, output conventions, the 6-item pre-delivery checklist, and cross-surface workflow context (companion static-ads-workflow.md for ad recreation; higgsfield-marketing-studio cross-surface-workflow.md §3 for ms_image / DTC Ads Higgsfield-native alternative).

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Search previous Claude Code conversations for facts, patterns, decisions, and context using semantic or text search

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Recognize when disagreements reveal valuable context, preserve multiple valid approaches instead of forcing premature resolution

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Create detailed implementation plans with bite-sized tasks for engineers with zero codebase context

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Take 1 blog post or article and generate 15-30 platform-native micro-content pieces. Not reformatting — re-contextualizing for each platform's culture. Triggers on: "atomize this content", "repurpose my blog post", "turn this into social posts", "content atomizer", "pillar content", "one to many content", "repurpose content", "multiply my content", "content explosion", "turn article into posts", "break down this article", "micro content from blog", "content pillar strategy", "10x my content", "platform-native content", "atomize", "content multiplication".

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Generate an AI Citations Report (GEO) for a domain — which AI-search prompts cite the site across Google AI Overview and ChatGPT, plus organic-traffic context and per-article citation coverage. Use when the user asks for an 'AI citations report', 'GEO citations report', or 'which AI prompts cite <domain>'.

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Use when the user asks m0 recall, where were we, what did we do last time, catch me up, what is next, or to resume context from another tool. Reads the recent M0 operational thread per project, newest first, from the M0 daemon.

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Use when work spans sessions and does not belong to any specific phase, or when the user wants to create, list or resume a context thread. Keeps lightweight threads under .planning/threads/.

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Use when you resume work and the last session ended mid-phase, a .continue-here checkpoint exists, or a PLAN has no SUMMARY. Restores STATE.md context, shows status, and routes to the next command.

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Use when 999.x backlog items need triage, or the user asks to review, promote, or prune the backlog. Shows each item with its accumulated context, promotes chosen ones into the active milestone, and removes stale ones.

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Use when the user asks what is pending, what to do next, or to list todos (optionally filtered by area). Lists pending todos, loads full context for the selected one, and routes to work now, add to a phase, brainstorm, or create a phase.

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Use when a bug, failing test, or unexpected behaviour needs investigation, especially across sessions or context resets. Isolates it in a debugger subagent with persistent .planning/debug state, unlike skills/systematic-debugging.

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Use when onboarding someone to a finished milestone or preparing a team review. Reads ROADMAP, REQUIREMENTS, CONTEXT, SUMMARY, and VERIFICATION artifacts into one MILESTONE_SUMMARY in .planning/reports/, then offers Q&A.

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Use when the user asks to refresh CLAUDE.local.md, regenerate project context, or after a brain-update session flush. Rewrites only the sentinel-delimited auto-generated section from the brain DB, preserving manual behavior rules.

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Use when the user says 'cognee recall', 'search memory', 'what do we know about', 'find related', or 'graph search'. Runs semantic, graph, and lexical search across the Cognee knowledge graph and injects results as agent context.

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Generate architecture documentation using C4 model Mermaid diagrams. Use when asked to create architecture diagrams, document system architecture, visualize software structure, or generate context/container/component/deployment diagrams.

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Use when working with this machine's local LLM setup (LM Studio + mlx-dspark) -- checking status, changing context window, diagnosing a reasoning hang or dead request, understanding RAM/speed tradeoffs, or wiring a new script to the local inference endpoint.

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Loads full context for a React Flow (@xyflow/react) user journey mapping project. Use when working on, extending, debugging, or building features for the journey map tool. Triggers on: "journey map project", "open journey map", "work on journey map", "build user journey", "react flow journey project", "journey-map app", or any request to add features like swimlanes, emotion curves, persona nodes, touchpoints, or pain point markers.

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Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimental_log.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER when the orchestrator delegates Step 2 or when the user asks to "generate the figures for my paper" or "render the plots from this experiment log".

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Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.

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JTBD discovery techniques adapted for AI product owner context. Four Forces extraction, job dimension probing, question banks, and anti-patterns for interactive feature discovery conversations.

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Foundational platform engineering knowledge from key references -- Continuous Delivery, SRE, Accelerate, Team Topologies, Chaos Engineering, and Secure Delivery. Load when contextual grounding in platform engineering theory is needed.

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DDD-guided legacy refactoring patterns -- strangler fig, bubble context, ACL migration, 14 tactical/strategic/infrastructure patterns, and incremental monolith-to-microservices methodology

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