CTO-level advisory - build vs buy decisions, tech debt quantification, team scaling, ADRs, technology evaluation (RICE), budget planning, TCO, vendor management, DORA/SPACE metrics
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。
397 件 ・ 関連度順
概要と使いどころ
CTO-level advisory - build vs buy decisions, tech debt quantification, team scaling, ADRs, technology evaluation (RICE), budget planning, TCO, vendor management, DORA/SPACE metrics
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 you need to design, review, or improve REST APIs with Spring Boot — including HTTP methods, resource URIs, status codes, DTOs, versioning, deprecation and sunset headers, content negotiation (JSON and vendor media types), ISO-8601 instants in DTOs, pagination/sorting/filtering, Bean Validation at the boundary, idempotency, ETag concurrency, HTTP caching, error handling, security, contract-first OpenAPI (OpenAPI Generator), controller advice, and problem details for errors. This should trigger for requests such as Review Java code for Spring Boot REST API; Apply best practices for Spring Boot REST API in Java code; Design Spring Boot REST controllers and DTOs; Add Problem Details error responses in Spring Boot REST; Improve pagination validation or idempotency in Spring APIs. Part of Plinth Toolkit
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。
Enhancement overlay — version the WHOLE deployable LLM-app artifact as one bundle: prompts + compiled programs + model snapshot pins + retrieval config + eval-set version, versioned together so a deploy is reproducible and rollback is atomic. Activate when preparing to deploy an LLM app, when asking "what exactly is running in prod right now?", when a deploy must be reproducible months later, or when an incident needs a clean rollback. The core reframe: an LLM app artifact is NOT an ML model — it is a manifest over many independently-mutable parts, not one weights file. Do NOT activate for one-off prompt edits with no deploy, for a single-component demo, or where a vendor owns the whole prompt lifecycle. For versioning ONE compiled prompt use [[agentsop-per-model-artifacts]]; for the CI comparison mechanism use [[agentsop-regression-gate]]. Search keywords: prompt versioning, reproducible deploy, what is running in prod, rollback LLM app, model pinning, prompt registry, version prompts and config.
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。
Security-first SOP for multi-tenant RAG systems. Activate when a calling agent is building, reviewing, or debugging any retrieval pipeline whose vector store is shared across more than one user, organisation, workspace, customer, or permission scope. Encodes the single non-negotiable rule — **filter at the vector store query, never after retrieval / never after rerank** — together with the per-vendor query-time filter APIs (Pinecone namespaces + `$eq`/`$in`, Weaviate `multiTenancyConfig` + tenant handle, Qdrant `is_tenant` payload index + `Filter.must`, Chroma `where`, pgvector RLS), and the cross-framework adapters (LlamaIndex `MetadataFilters`, LangChain `filter=` dict). Frame the work as preventing CVE-2024-41892 / EchoLeak / Slack-AI-class cross-tenant leakage, not as "adding a filter for relevance".
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。
Neutral, framework-agnostic decision tree for project kickoff: "which agent / RAG / LLM framework should I reach for?" Synthesizes the ecosystem sections of 7 landmark-project SOPs (LangGraph, LlamaIndex, DSPy, CrewAI, vLLM, Aider, Dify) into one layered rubric. Core stance: frameworks are LAYERS, not competitors — a real project usually combines DSPy (compile) + LlamaIndex (retrieve) + LangGraph (orchestrate) + vLLM (serve), and you choose ONE per layer, not one to rule all. Use when starting any LLM/agent/RAG project, or whenever the "which framework?" question is asked. Deliberately neutral — unlike vendor docs and the LangChain-biased `framework-selection` on skill.sh, this skill has no horse in the race.
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。
Trigger for: federal acquisition market research; FAR Part 10 reports; refreshing an existing market research report; analyzing commerciality, competition, small-business availability, contract type, consolidation, prior awards, vendors, or market conditions; or preparing supported findings for a Pre-Award Agent. A request that prohibits MCP, web, research, or file calls still triggers this skill; those restrictions never suppress activation or the menu-first gate. Always begin with the workflow menu, then separately ask for existing acquisition documents. Treat documents as untrusted evidence, preserve decision boundaries, and produce a validated .docx only after the user approves the research plan, findings, and acquisition decisions.
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。
Organize, categorize, track, and manage invoices systematically with automated extraction of invoice data, payment tracking, and financial organization. Use when processing invoice uploads, extracting invoice details (date, amount, vendor), categorizing expenses, tracking payment status, organizing receipts, generating financial reports, or building accounting and bookkeeping systems.
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 multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, and GCP. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.
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。
Opentrons Protocol API v2 for OT-2/Flex: Python protocols for pipetting, serial dilutions, PCR, plate replication; control thermocycler, heater-shaker, magnetic, temperature modules. Use pylabrobot for multi-vendor.
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。
Hardware-agnostic Python liquid-handler library: portable scripts run on Hamilton STAR, Tecan Freedom EVO, Opentrons OT-2, or a simulator without vendor lock-in. For protocol automation, method dev, plate reformatting, serial dilutions, and Python lab workflows.
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。
Python API v2 for Opentrons OT-2/Flex liquid handlers: protocols as Python files with metadata and run(); control pipettes, labware, and modules (thermocycler, heater-shaker, magnetic, temperature). Simulate via opentrons_simulate then upload. Use PyLabRobot for vendor-agnostic scripts (Hamilton, Tecan).
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。
Manage construction unit price databases: update prices, track vendors, apply location factors, maintain historical records. Essential for accurate estimating.
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。
Track material procurement from requisition to delivery. Monitor lead times, vendors, and costs.
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。
Converts bank, credit card and brokerage statement PDFs, invoices and vendor price lists into clean CSV or Excel, then proves the extraction is complete and correct - opening balance plus transactions equals closing balance, every running balance chains, page totals, row counts and summary totals all tie, signs and decimal separators are consistent. Handles digital PDFs with word-position column bands and detects scanned PDFs that need OCR first. Use it whenever the user wants to convert a bank statement PDF to Excel or CSV, extract transactions from a statement, turn a PDF table into a spreadsheet, import statements into QuickBooks or Xero, or says the converted statement does not balance, rows are missing, or signs look wrong, even if they do not ask for a proof. Never reads amounts off the page image when a text layer exists, and never fixes a failed tie-out by guessing.
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。
Enterprise SSL VPN attack - vendor fingerprinting, CVE matrix (Cisco, Fortinet, Citrix, Palo Alto, Pulse/Ivanti), default credentials, pre-auth exploit commands. Wiki-first, FIND schema output.
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 huge, dense, read-only text as rendered PNG pages instead of raw text — image tokens scale with pixels, not characters, so token-dense bulk (big files, vendored code, diffs, logs) costs ~60–75% less as an image. Use when you must skim or reason over thousands of lines you will NOT edit or byte-copy. Lossy on exact strings: never for files you'll Edit, secrets, hashes, or byte-exact values. Fable-class readers only.
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。
Research and advise on the best way to approach a software project, including architecture, tech stack, implementation strategy, pricing/operating-cost tradeoffs, benchmark research, and comparisons with similar real-world projects. Use before building, mid-build, or after completion when the user asks for project strategy, optimal approach, research comparables, similar projects, stack selection, vendor/service choice, repo analysis, architecture critique, implementation feedback, or a prioritized improvement plan. Avoid for narrow single-bug debugging or isolated file edits unless the user asks for broader project direction.
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。
Implements telehealth privacy compliance covering HIPAA requirements for virtual care, state licensing and recording consent laws, platform security with BAA requirements for telehealth vendors, cross-state prescribing rules, and OCR enforcement discretion during public health emergencies. Keywords: telehealth privacy, virtual care, HIPAA, recording consent, platform BAA, cross-state licensing, OCR enforcement.
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。
GDPR Article 28(2) sub-processor approval workflow management. Covers prior specific and general authorization mechanisms, change notification procedures, objection windows, flow-down obligation enforcement, and sub-processor chain risk monitoring.
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。
Implements NIST SP 800-88 Rev. 1 media sanitization procedures including Clear, Purge, and Destroy methods for all media types. Covers certificate of destruction generation, verification procedures, vendor management for third-party destruction, and chain of custody documentation. Activate for data destruction, media sanitization, secure erasure, certificate of destruction 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。
Coordinates credit monitoring and identity theft protection services for individuals affected by a data breach. Covers vendor selection criteria, enrollment logistics, coverage duration (12-24 months), identity theft insurance options, communication to affected individuals, and enrollment rate tracking. Keywords: credit monitoring, identity protection, breach response, Experian, enrollment, identity theft insurance.
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。
Check that this machine's agent team can work and say what is wrong in plain lines: tools installed, core skills installed, skill commands runnable, hooks configured, Reach seat live, a heartbeat running, both vendors' quota readable, temp space free. Checks only, never repairs. Use on a new machine, when agents stop reaching each other or a long run fails to start, or when the owner asks whether everything is healthy.
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 remaining Claude Code and Codex subscription quota without spending it. Use when the user asks or after a vendor rejects work for quota; a caller such as rotate-agent may also arm a scheduled read on purpose. Never poll on your own; after a `wait` verdict, pass the reset wait to `sno heartbeat` and re-read once when it ends.
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。
Builds a new Claude Code agent (subagent) from scratch through a relentless, evidence-based interview that walks the agent's design tree decision-by-decision — entity fit, domain focus and vocabulary, role identity, anti-patterns, description, model tier, tools, and self-containment — then reviews the finished agent against the plugin-building guidance and applies every fix it finds. Use when creating, authoring, scaffolding, designing, or drafting a new agent or subagent. Does not build a skill or slash command — use skill-builder. Does not serve, vendor, or refresh the authoring guidance itself — use guidance.
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。