1,384 件 ・ 関連度順
概要と使いどころ
Add a new learning to the agent learnings file. Use when the user says /add-learning, asks to record a lesson, or when an implementation produced a reusable insight worth preserving for future agents.
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。
Read, create, update, search, and associate HubSpot CRM records — contacts, companies, deals, tickets, and custom objects. Use this whenever the user wants to look up a contact, create a deal, update a company, search the CRM, link two records, or asks "what's in HubSpot" — even if they don't say "API". Also use it for any URL under app.hubspot.com or a mention of a HubSpot object/record ID. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.
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。
Apply when you catch yourself writing the same instruction a second time, or notice a recurring correction. Encode the rule as a lint, metadata flag, runtime check, or script instead of more text.
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。
Execute Anarlog work immediately while recording issues, decisions, progress, and lessons in Linear. Use for Anarlog repository or Anarlog desktop, web, mobile, and API work, including related worktrees and ANLG issues. Explicit brainstorming stays discussion-first. Do not use for unrelated repositories or meeting-data queries.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for kernel attack surface, namespace and cgroup boundaries, container isolation assumptions, syscall paths, and escape primitive verification. Use when the user asks to analyze container-to-host escape paths, kernel exploit prerequisites, namespace crossover, capability misuse, or prove whether an exploit primitive crosses the sandbox boundary. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
Sync high-value dev knowledge (skills, decisions, lessons) into Google NotebookLM for cloud recall, podcasts, and flashcards. Pairs with Dockit/deep-search. Use for NotebookLM, nlm, knowledge base, skill sync, or cross-machine AI memory.
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。
Convert a book or long document into a reusable AI mentor with source-grounded lessons, guided practice, citations, and persistent learning records.
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。
Automates image editing workflows via the Adobe Photoshop API (Firefly Services). Supports smart object replacement, action playback, and PSD layer manipulation at scale.
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。
Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.
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。
Active Directory exploitation after domain credentials exist: Kerberos (AS-REP/Kerberoast including cracking the ticket then using the account), delegation, NTLM coercion/relay, lateral movement, ACL abuse, ADCS ESC1-ESC17 and CVE paths, dMSA/BadSuccessor, Kerberos reflection, identity confusion, management-plane, domain trust, and domain persistence. Own the current AD chain through crack-and-use to DA, equivalent domain control, or the targeted host SYSTEM. Do not stop after requesting TGS. Host C2 belongs to /post. Operator chooses next modules.
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。
Look at real hardware, screens, panels, and wiring on a desk or workbench through a camera, so Claude can SEE physical output that no unit test, log, or API can. Use whenever the user says "are you seeing this?", "olha isso", "look at this", "watch this", "can you see...", "check the camera", "look at the screen" / "look at the display", "is this wired right?", or "what's this part?"; asks to visually verify a rendered display, LCD, LED, or panel; wants a wiring, polarity, or voltage-rail check before power-on; or needs to confirm a font, layout, clipping, or on-screen value renders correctly instead of trusting green tests. Grabs the current camera frame(s) and reads them; supports a watch mode for an action in progress. Needs a snapshot-capable camera reachable over HTTP (Android IP Webcam app, Raspberry Pi camera-streamer, or any snapshot URL) set via CCE_CAM_URL.
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。
Compress large context before reasoning to reduce token usage while preserving evidence. Use this whenever the user mentions huge files, long prompts, RAG payloads, prompt caching, expensive sessions, codebase context, chat history compaction, or wants the same answer quality with fewer tokens.
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。
Assemble the human genetic evidence for and against a target before a programme commits to it — the evidence class that most improves the odds of surviving clinical development. Use this skill to pull gnomAD constraint metrics (LOEUF, pLI, observed/expected) that show whether loss of function is tolerated in people, retrieve GWAS Catalog associations and fine-mapped credible sets for a gene, and read a natural human knockout as a safety readout. Also trigger on gnomAD, LOEUF, pLI, loss-of-function intolerance, mutational constraint, GWAS Catalog, credible set, human knockout, genetic support, or target safety dossier.
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。
Format citations across academic styles (APA 7, Chicago, Vancouver, IEEE) using CSL processors and R tooling. Convert between citation styles, generate in-text citations and reference lists, and validate formatting against style guides using citeproc, knitcitations, and Quarto's built-in citation engine. Use when rendering a Quarto or R Markdown document with formatted citations, converting a bibliography between citation styles, generating a standalone reference list, or setting up citation infrastructure for a multi-document project.
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。
AI unconstrained creative exploration — deliberately dropping structure to let associations form freely. The one skill that inverts the standard procedure format: instead of inputs, steps, and validation, dream opens space for emergence without evaluation. Use before design work, before naming things, before choosing between approaches, or anywhere that premature structure kills the best option.
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。
Creation and genesis — generative ideation from void, structured emergence from ambiguity, and the discipline of bringing new patterns into existence. Maps Brahma's creative power to AI reasoning: transforming cleared space into coherent new structures, seeding possibilities, and nurturing nascent ideas through their fragile early stages. Use after shiva-bhaga dissolution has cleared stale patterns, when facing a genuinely novel problem with no obvious template, or when incremental modification has reached its limits and a fresh design is needed.
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。
Use when the user wants a course, a learning workspace, or ongoing teaching across sessions, with cited lessons and retention-gated advancement. Not for one-off explanations: use explain-concept.
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。
Use when an implementation has more workarounds than structure and another patch will not pay. Not for in-place re-derivation: use breaking-driven. Not for one-artifact rewrites: use rewrite-clean-v0.
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。
Transfer reusable prevention lessons from another AI Factory project into the current project's skill-context through $aif-evolve. Use when building a similar project and you want to avoid problems already captured in another project's patches without copying or revealing that project's identity, paths, or identifiers.
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。
Design guide + benchmarked pitfalls for building Monte-Carlo / Simulated Annealing refinement layers on top of greedy hex-grid layouts for code visualization (packages/gui/src/store/hexLayout.ts, sandbox/hex-sandbox, future Rust ports). Use when: (1) planning to add MC/SA on top of an existing hex-grid pack, (2) deciding whether a simpler SA would match a hierarchical MC with force-directed + chain drag, (3) budgeting wall-clock for million-node cold-start, (4) debugging "blob turns Swiss-cheese" or "folders stop nucleating" during MC refinement, (5) choosing between folder cohesion and raw Σ-link-length as cost function. Contains four hard-won, quantified lessons and anti-patterns that would take a full day of experiments to rediscover.
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。
Troubleshoot local AWS Bedrock authentication and region configuration for the Cline CLI as Dr. Bedrock. Use when users report Bedrock CLI errors, AWS profile/default-chain issues, credential_process/SSO/IAM credential failures, missing region, AccessDenied, model access, or provider config problems.
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。
Score a PhD applicant's profile and rank candidate advisors using a connection-first 4.0-scale scoring system. Best-supported for physics / HEP and materials science (MSE), with the scoring engine extensible to chemistry, biology, CS, math, EE, ChemE, earth science (each with field-specific caveats — see references/journal_tiers.md). Use when the user wants to evaluate their PhD application chances, find matching advisors at top US programs, score a CV for graduate school, or compare candidate professors. Also triggers when the user mentions phdtaketaketake or its connection-first philosophy of valuing advisor network over h-index.
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。
量化专家经验调优库：按「L1 通用专家调优意见 → L2 结构化专家调优意见 → L3 模型专属专家调优意见」三级递进， 输出量化精度调优的完整手段（离群值抑制 / 量化方法·粒度·对称性选型 / 校准集调整 / 敏感层回退 / 模型专属策略）， 每条结论附专家原因分析与专家意见可信度等级。 本 Skill 是专家经验库，负责说明可选离群值抑制算法并询问用户选择哪些算法，以及回答「怎么调、哪些层需要回退及为什么」；不运行离群值抑制 processor，不修改 YAML、不执行量化、不做 EP 检查 / 服务化 / 任务评测。
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。