「memo」の検索結果

Use BEFORE reading any data file that could be large (CSV/TSV, Parquet, HDF5, FITS, NetCDF, NDJSON, genomics FASTQ/FASTA/VCF/BAM, GRIB, ROOT, or big text/simulation logs like VASP OUTCAR). Returns a compact memory pointer — header/schema/shape/sample/key numbers — by introspection and sampling in bounded memory, so you never load a file bigger than the context window into the model. Reference data via the pointer; read specific ranges deterministically.

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Prepare and manage durable MEX team handoffs. Use when the user asks to hand work to a person or team, prepare an end-of-session handoff, save what the next engineer needs, create a MEX Relay, take or close a Relay, or explicitly invokes /mex-relay or $mex-relay. Treat Relay as a memory and context baton, not chat, notifications, task assignment, issue tracking, or a Jira replacement; do not activate for ordinary status messages that are not durable handoffs.

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Write efficient C code with proper memory management, pointer arithmetic, and system calls. Handles embedded systems, kernel modules, and performance-critical code. Use PROACTIVELY for C optimization, memory issues, or system programming.

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Für Aktenvorblatt: Schriftsatz-, Brief- und Memo-Bausteine: erstellt Entwurf mit Antrag, Beweis und Anlagen; Ergebnis: Schriftsatz mit Begründungs- und Anlagenlogik.

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Für Risk Acceptance Memo: erstellt Entwurf mit Antrag, Beweis und Anlagen; Ergebnis: Schriftsatz mit Begründungs- und Anlagenlogik.

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AI DevKit · Use the memory CLI as a durable knowledge layer. Search before non-trivial work, store verified reusable knowledge, update stale entries, and avoid saving transcripts, secrets, or one-off task progress.

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Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.

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List recent meetings and voice memos. Use when the user asks "what meetings did I have", "show my recent recordings", "any meetings today", "list my voice memos", or wants an overview of their meeting history. Also use when they need to find a specific meeting by browsing rather than searching.

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Routes Claude Code: plugin/Skill repair, marketplace, statusline, model profiles/source sync, 1M/[1m] context, usage/quota reset ping, memory→docs, .txt repair, turning auto memory off. Reads one bundled specialist; history, hooks, and Lark keep their own entries.

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Optimizes or restructures CLAUDE.md/AGENTS.md with progressive disclosure and zero information loss. Use when the user asks to optimize, audit, 精简/瘦身/重构, or split instruction files, asks for their best practices (最佳实践), sees Memory files taking a large share of /context, reports rules in instruction files being ignored, or a task starts moving instruction sections. Not for generic task drift unless instruction files are in scope; not for moving memory entries into docs (use claude-code-ops-router).

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Offboard an AI agent the way you'd offboard an employee — inventory what it knew and touched, export then purge its memory, revoke every credential and access grant, and write the handover for its successor (human or agent). Use when decommissioning an agent or bot, switching agent vendors, ending an AI pilot, or when someone asks 'what did this thing have access to?'. Produces a severance checklist, an access-revocation table, a memory disposition record, and a successor handover.

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Simulate the acquirer's diligence team hunting for reasons to cut your price — their internal red-flags memo with a price-chip estimate per finding. Use when asked to red-team my company before a sale, how will an acquirer attack our valuation, pre-diligence audit, or what will DD find. Produces the acquirer's internal memo (revenue quality, key-person, tech debt, concentration, legal) and a debrief on which flags are fixable before a process.

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Use this skill when producing a polished, Commonly-branded deliverable (.docx brief / memo, .xlsx data matrix, .pptx deck) and you do not have specific brand guidance from the user. Trigger on: 'write me a brief', 'one-pager', 'memo', 'data sheet', 'status matrix', 'short deck', 'summary deck', 'closing slide', 'final deliverable'. Routes to `officecli merge` with one of three pre-built starter templates that already carry the Commonly palette, fonts, and structure — so you populate content with one merge call instead of 30 individual `officecli set` commands. DO NOT use for fundraising decks (use `officecli-pitch-deck`), academic papers (use `officecli-academic-paper`), or financial models (use `officecli-financial-model`).

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Use this skill any time a .docx file is involved -- as input, output, or both. This includes: creating Word documents, reports, letters, memos, or proposals; reading, parsing, or extracting text from any .docx file; editing, modifying, or updating existing documents; working with templates, tracked changes, comments, headers/footers, or tables of contents. Trigger whenever the user mentions 'Word doc', 'document', 'report', 'letter', 'memo', or references a .docx filename.

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Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

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Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

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Install and use GrayMatter as an OpenClaw skill that provides primary durable memory, shared object-graph state, and authenticated access to the live api-docs schema via api-0. Use when the agent should persist durable memory, inspect organizational data models, and operate inside the business domain through RBAC-scoped API access.

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Use Waggle automatically as portable, local project memory during meaningful coding work; retrieve prior decisions and constraints, then store only durable outcomes.

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INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.

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Periodic trust sweep of persistent memory and durable knowledge notes - re-verifies environment-dependent claims against the live environment, stamps last_verified + confidence, and proposes archiving drifted entries

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Optimizes Simulink models for Embedded Coder generated code. Use when asked to optimize or improve generated code, or reduce code metrics for a Simulink model. Targets: execution time, memory footprint (RAM, ROM, stack, data copies), code size, MISRA compliance, or any semantically similar generated-code metric. Works iteratively — measures baseline, suggests changes, applies, and re-measures to confirm improvement. Triggers can be prompts similar to: optimize generated code runtime, reduce runtime, shrink code size, improve code efficiency, reduce memory usage, speed up generated code, follow MISRA compliance and so on. CAUTION: Do NOT attempt to optimize Simulink models for generated code efficiency without following this skill — the iterative measurement, gating, and rollback workflow is essential for safe optimization.

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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.

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Use when an Apple-platform app feels slow, memory grows, battery drains, or ANY performance issue needs diagnosing. Covers memory leaks, profiling, Instruments workflows, retain cycles, optimization.

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Debug iOS apps and profile performance using LLDB, the interactive Memory Graph Debugger, and Instruments. Use for crashes, retain-cycle inspection, hangs, build failures, and generic CPU, memory, energy, or network profiling. Use ios-memgraph-analysis for .memgraph capture, leaks CLI ownership paths, or persistent heap growth; use ios-ettrace-performance for ETTrace capture and JSON.

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