其他技能

Build frameworks from scattered insights across all braindumps and notes

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Evaluate URLs and tools — check vault coverage, assess relevance, recommend save or skip

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Cross-domain pattern analysis and strategic reflection for weekly review

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Publish any markdown file from the vault to Confluence with format conversion and approval gate

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Maintain and update product knowledge base from releases, features, and project changes with optional wiki sync

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Generate personalized news intelligence with verified sources (7-day freshness requirement)

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Quick capture URLs with automatic content extraction, insights, and categorization into knowledge booklets

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A passive daily work journal that Claude keeps FOR you so you never have to write it yourself. Append short entries after meaningful work (what was done, what you focused on, artifacts touched) to 01-daily/journal/YYYY-MM-DD.md. Run a guided reflection at night or in the morning. Use when you run /daily-journal, say "log this to my journal", "add to today's journal", "reflect on today/yesterday", or when finishing a meaningful chunk of work in any session.

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Edit drafts into sharper, more human writing while preserving the writer's personal voice, or detect AI-slop patterns without rewriting. Use when the user wants a draft clearer, more direct, more opinionated, or less AI-sounding, or asks whether writing reads as AI.

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Quick capture of raw thoughts with intelligent domain classification and competitive intelligence extraction

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Generate product requirements documents with optional publishing to Confluence or other wiki platforms

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Check for and apply upstream COG framework updates (skills, docs, scripts) without touching personal content

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INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.

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INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output. Covers HumanInTheLoopMiddleware for human approval of dangerous tool calls, creating custom middleware with hooks, Command resume patterns, and structured output with Pydantic/Zod.

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Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.

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Dispatches many independent items in parallel: create a table, fan out to subagents, aggregate results. One row = one unit of work.

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Sets up Caliber for automatic AI agent context sync. Installs pre-commit hooks so CLAUDE.md, Cursor rules, and Copilot instructions update automatically on every commit. Use when Caliber hooks are not yet installed or when the user asks about keeping agent configs in sync.

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Map tanstack ecosystem partner metadata to installable add-on ids using tanstack ecosystem --json, tanstack create --list-add-ons --json, and tanstack create --addon-details --json. Covers exclusive categories, provider options, and router-only compatibility constraints.

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Retrieve machine-readable context with tanstack libraries, tanstack doc, tanstack search-docs, tanstack create --list-add-ons --json, and --addon-details for agent-safe discovery and preflight validation.

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Build and iterate custom add-ons/templates with tanstack add-on init, add-on compile, add-on dev, and tanstack create --dev-watch, including sync loop preconditions, watch-path validation, and project metadata constraints.

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Use when writing the response to an American Anthropologist (AA) revise-and-resubmit. AA uses anonymous peer review under an ethics of care, often with cross-subfield reviewers, so the response must convert each reviewer while keeping the editor confident and the ethnographic contribution intact. Structures the response letter; it does not fabricate new evidence.

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Use when building the conceptual/theoretical argument of an American Anthropologist (AA) manuscript into a four-field contribution — whether the work is ethnographic, archaeological, biological, or linguistic. AA rewards a portable concept and reflexive argument over a bare finding or thick description alone. Structures the argument; it does not run analyses or write fieldnotes.

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Use when the quantitative model is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — DSGE, New Keynesian, heterogeneous-agent (HANK / Aiyagari-Bewley), or structural estimation — and calibration, parameter identification, solution accuracy, or counterfactual validity need discipline. For empirical shock identification see aejmac-identification.

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Use when building or revising exhibits for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — impulse-response figures, fan charts, model-fit overlays, and regression/moment tables — to AEA house standards and macro conventions. Formatting and clarity; it does not generate the underlying estimates.

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