「process」の検索結果

Process and act on code review feedback. Use when receiving review results.

日本語の概要は準備中です。原文の説明を表示しています。

Write effective blameless postmortems with root cause analysis, timelines, and action items. Use when conducting incident reviews, writing postmortem documents, or improving incident response processes.

日本語の概要は準備中です。原文の説明を表示しています。

Master on-call shift handoffs with context transfer, escalation procedures, and documentation. Use when transitioning on-call responsibilities, documenting shift summaries, or improving on-call processes.

日本語の概要は準備中です。原文の説明を表示しています。

Master memory forensics techniques including memory acquisition, process analysis, and artifact extraction using Volatility and related tools. Use when analyzing memory dumps, investigating incidents, or performing malware analysis from RAM captures.

日本語の概要は準備中です。原文の説明を表示しています。

Fintech engineering expertise — payment processing (Stripe, Plaid), PCI DSS compliance, financial data modeling (double-entry bookkeeping), fraud detection patterns, bank-grade security (encryption, tokenization), open banking APIs, cryptocurrency/blockchain integration, regulatory compliance (KYC/AML), and idempotent financial transaction design. Use for payment systems, banking apps, trading platforms, and fintech infrastructure.

日本語の概要は準備中です。原文の説明を表示しています。

Data processing expert including parsing, transformation, and validation

日本語の概要は準備中です。原文の説明を表示しています。

Expert Electron desktop application development — main/renderer process architecture, IPC communication, native OS APIs (menus, tray, notifications, dialogs), auto-updates, code signing, packaging with electron-builder/forge, security hardening (contextIsolation, sandbox), and performance optimization. Use for building cross-platform desktop apps.

日本語の概要は準備中です。原文の説明を表示しています。

Process external specifications through transformation, classification, gap analysis, component discovery, and decomposition. Creates workflow items with classified context.

日本語の概要は準備中です。原文の説明を表示しています。

Hunt business logic flaws — race conditions, workflow state bypass, parameter pollution affecting logic, coupon/discount abuse, refund logic, multi-step process bypass, integer overflow, negative quantities, double-spend. Use when looking for non-CVE bugs that require understanding the app's intended workflow.

日本語の概要は準備中です。原文の説明を表示しています。

RED-GREEN-REFACTOR for process documentation - baseline without skill, write addressing failures, iterate closing loopholes

日本語の概要は準備中です。原文の説明を表示しています。

TDD for process documentation - test with subagents before writing, iterate until bulletproof

日本語の概要は準備中です。原文の説明を表示しています。

Prepare and ship an iOS build to TestFlight — runs the submission pre-flight checks, fixes the config that blocks uploads/processing, then walks internal→external beta review. Use when releasing an iOS build to testers.

日本語の概要は準備中です。原文の説明を表示しています。

Process unhandled Slack messages as Claudicle with persistent memory. Loads user models and soul state, posts terminal-styled thinking messages, generates cognitive-step responses, updates memory based on mentalQuery decisions. Requires Session Bridge listener running.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used to spawn specialized OpenAI Codex CLI subagents for code review, debugging, architecture analysis, security audits, refactoring, documentation, comparative evidence adjudication, and autonomous /goal runs. It adds each persona through process-local developer instructions while preserving the project's AGENTS.md chain, including during parallel launches. Supports GPT-6-Astra across low, medium, high, xhigh, max, and ultra reasoning with default or priority service tiers, plus GPT-5.6 and GPT-5.5 models. Triggers on 'delegate to Codex', 'Codex Astra', 'Astra subagent', 'Codex subagent', 'code review agent', 'security audit', 'refactor with Codex', 'goal run', 'autonomous goal', 'have Codex weigh this'.

日本語の概要は準備中です。原文の説明を表示しています。

Send, read, listen, respond, and manage SMS conversations via Telnyx and Twilio. Supports 8 phone numbers across both providers with auto-detection, conversation threading, MMS, real-time inbound listening, background watcher, batch reply processor, and soul-aware auto-reply. 10 Python scripts for SMS operations.

日本語の概要は準備中です。原文の説明を表示しています。

Desktop GUI automation via OpenInterpreter — mouse, keyboard, screenshot, and OCR control for native macOS/Linux applications. Three modes: Library (Claude reasons, OI executes), OS subprocess (full autonomous computer use), and Local agent (Ollama, offline). This skill should be used when interacting with desktop apps that have no CLI or API, automating GUI workflows, reading screen content via OCR, or controlling mouse/keyboard.

日本語の概要は準備中です。原文の説明を表示しています。

Reference WebGPU APIs, TSL syntax, node materials, WGSL integration, compute shaders, and post-processing in Three.js. For complete particle canvases use threejs-particle-canvas; for GLSL shaders use rocaille-shader. Triggers on TSL shader, WebGPU renderer, node material, WGSL, GPU compute, WebGPU setup.

日本語の概要は準備中です。原文の説明を表示しています。

Edit images with precision using ImageMagick 7, sips, transparent-background, and Pillow — with intelligent routing to AI semantic editing (Gemini/nano-banana-pro) for content-aware operations. Covers cropping, resizing, compositing, annotating, format conversion, color adjustment, background removal, batch processing, and montage creation. Triggers on image editing, crop, resize, composite, annotate, remove background, format conversion.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze a book (EPUB/PDF) and generate detailed chapter-by-chapter notes with extracted key concepts. Use when given a book file path to process.

日本語の概要は準備中です。原文の説明を表示しています。

Extract transcript and key slides from a local video file, then create a vault-formatted lecture note. Use this skill whenever a user provides a local video file (.mp4, .mov, .mkv, .avi, .webm) and wants notes from it. Triggers on "lecture", "take notes", "I have a recording of", "class video", "transcribe this presentation", "process this video for notes", or /lecture. Works with any language — auto-detects transcript language.

日本語の概要は準備中です。原文の説明を表示しています。

Tested output format specifications for Claude prompts. Use when the user wants a specific deliverable structure — retrieving, reviewing, customizing, or building output formatting. Trigger on: "give me the Executive Brief format," "show me the output template for," "I need the Decision Matrix structure," "show me available output formats," "build a custom output format for." Provides 10 ready-to-use structures (executive briefs, technical designs, research summaries, implementation plans, decision matrices, competitive analyses, post-mortems, stakeholder updates, strategic memos, process documentation) plus a custom template, with per-section length guidance and format constraints tested against Claude formatting defaults. If unsure which format fits, use rootnode-block-selection first if available. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or choosing reasoning/identity approaches (use rootnode-block-selection if available).

日本語の概要は準備中です。原文の説明を表示しています。

Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds and scaffold splits, BRICS/RECAP fragmentation, 3D conformer generation, SDF/CSV/Excel and cloud I/O, and parallel processing via n_jobs. Returns native rdkit.Chem.Mol objects, so it composes with RDKit throughout. Also trigger on datamol, `import datamol as dm`, dm.to_mol, dm.standardize_mol, dm.cluster_mols, or dm.pick_diverse. For advanced control or custom parameters, use the rdkit skill directly.

日本語の概要は準備中です。原文の説明を表示しています。

Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological relationships across genes and proteins, drugs, diseases, phenotypes, pathways, biological processes, exposures and anatomy. Use this skill to search entities by name, pull direct neighbours and their evidence types, summarise the local network around a disease, and find direct or two-hop drug-disease connections for repurposing hypotheses. Also trigger on PrimeKG, kg.csv, Harvard Dataverse knowledge graph, disease_protein, drug_protein, indication and contraindication edges, or network pharmacology over a biomedical knowledge graph.

日本語の概要は準備中です。原文の説明を表示しています。

Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Web runtime package: uses memory in this order: connector-backed, project-pack, none. Never assumes subprocess access or unrestricted local files. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles.

日本語の概要は準備中です。原文の説明を表示しています。