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概要と使いどころ
BA (Business Analyst) agent. Analyzes requirements and produces 4 artifact files: requirements.md, user-stories.md, acceptance-criteria.md, business-rules.md. Use --spec <path> to load requirements from any markdown file. Part of the Virtual Team Skill pipeline.
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。
Full-pipeline orchestrator. Runs all 7 agents (BA → TechLead → PM → BE Dev → FE Dev → Tester → QA/QC) in sequence by invoking each per-agent skill via the Skill tool. All writes go through the pre_write_validator.py hook for hard enforcement. Use per-agent commands (/team-ba, /team-techlead, etc.) for manual control.
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。
BA (Business Analyst) agent. Analyzes requirements and produces 4 artifact files: requirements.md, user-stories.md, acceptance-criteria.md, business-rules.md. Use --srs to load from SRS workflow artifacts (spec.md / brainstorm.md). Part of the Virtual Team Skill pipeline.
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。
BE Dev (Backend Developer) agent. Reads BA + TechLead + PM artifacts and generates backend source code files, .env.example, and pr-description.md. No hardcoded credentials. Part of the Virtual Team Skill pipeline.
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。
FE Dev (Frontend Developer) agent. Reads BA + TechLead + PM + BE artifacts and generates frontend source code files and pr-description.md. Part of the Virtual Team Skill pipeline.
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。
Independent senior-engineering quality auditor — architecture, ownership, domain integrity, abstraction, and maintainability. Use as `--gate` inside the ck:cook pipeline (blocks a phase on BLOCKER/HIGH/current-change MEDIUM findings) or call directly with `--audit`, `--diff`, `--changed` on any repository, directory, or file. Never writes tests and never edits production code — it returns structured findings only; ck:cook or ck:fix apply the fix.
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。
Tester agent. Reads ALL preceding artifacts (BA + TechLead + PM + BE + FE) and produces test plan, test cases (unit/integration/e2e), and bug report template. Declares Gate 2: UAT Readiness. Part of the Virtual Team Skill pipeline.
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。
Plan a feature or system before implementation. Use when the user says "plan this", "I want to build X", "how do I implement Y", or when /ck:brainstorm produces a spec.md. Always run before /ck:cook. Modes (pick one): --fast (simple, single-file), --hard (research + red-team + validate). Composable flag (combine with any mode): --tdd — propagates into the cook pipeline. Every phase gets Design Constraints and Quality/Testing State; ck:cook confirms optional test and quality checks per phase.
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。
Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types, including the Agent Script language for the new Agentforce Builder (hybrid-reasoning -> logic vs | prompt instructions, @actions/@subagent/@variables references, subagents). Use when creating agents in source control, writing or debugging Agent Script, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'how do I deploy an Agentforce agent using source control', 'convert an agent topic to a subagent', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship', 'Agent Script logic vs prompt instructions'. NOT for Apex-based agent actions — use agentforce/custom-agent-actions-apex. NOT for clicking the agent together in the Builder UI — use agentforce/agentforce-agent-creation.
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 this skill when writing, debugging, or optimizing SAQL queries in CRM Analytics — covering pipeline syntax, aggregation, windowing functions (rank, dense_rank, moving_average), cogroup joins, rollup subtotals, piggyback queries, and REST API execution. NOT for SOQL GROUP BY aggregates in Apex — use apex/apex-aggregate-queries. NOT for dashboard JSON steps and bindings — use admin/analytics-dashboard-json.
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 building, auditing, or troubleshooting Salesforce Reports and Dashboards. Triggers: 'report', 'dashboard', 'missing data in report', 'pipeline report', 'cross-filter', 'report subscription', 'dashboard refresh', 'report-meta.xml', 'dashboard running user', 'report folder sharing', 'package.xml report wildcard'. NOT for choosing a report type, bucket fields, summary formulas or joined-report limits — use admin/reports-and-dashboards-fundamentals. NOT for Einstein Analytics / CRM Analytics — use admin/einstein-analytics-basics.
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。
Eliciting and documenting a sales process before it is built in Salesforce: stage sequencing, entry/exit criteria per stage, win/loss analysis requirements, and stage transition rules. Trigger keywords: sales process design, stage discovery, entry criteria, exit criteria, stage gate, win/loss categorisation, sales methodology, pipeline stages, opportunity stage mapping, stage ladder, probability ladder, forecast category mapping, required fields per stage, stage swim lane, discovery workshop, handoff brief. NOT for configuring OpportunityStage, Sales Processes or Path in Setup — use admin/opportunity-management. NOT for cross-functional swim-lane maps — use admin/process-flow-as-is-to-be.
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。
Configure Einstein Activity Capture (EAC) for an org: enable the feature, create Configuration profiles, set auth type (User-Level or Org-Level OAuth), define email and calendar sync scope, excluded domains and privacy settings, assign users, verify sync health. NOT for querying synced emails or events in SOQL, Apex or reports — use apex/einstein-activity-capture-api. NOT for Opportunity Scoring or Pipeline Inspection — use agentforce/einstein-copilot-for-sales.
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 a practitioner needs to classify the risk level of an upcoming Salesforce deployment, define rollback trigger conditions, or assign decision authority before the release window opens. Triggers: 'how risky is this release', 'what needs rollback planning', 'classify deployment risk', 'pre-deployment risk checklist', 'rollback decision authority', 'high risk metadata types'. NOT for executing the deployment itself, writing CI/CD pipeline code, or troubleshooting failures after a deployment has already run — use admin/change-management-and-deployment for execution planning and devops/deployment-error-troubleshooting for post-failure diagnosis.
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 this skill when defining AI governance policies, designing human oversight workflows, documenting bias mitigation strategies, or meeting regulatory transparency requirements for Salesforce AI features. Trigger keywords: responsible AI, AI bias, AI audit trail, AI transparency, human-in-the-loop, AI risk inventory, AI disclosure. NOT for the MLOps pipeline or Policy-as-Code engine architecture — use architect/ai-governance-architecture. NOT for Trust Layer technical configuration — use agentforce/einstein-trust-layer.
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。
Router for the 70 SfSkills `devops` skill packages. Salesforce delivery: source tracking, packaging, branching, CI/CD pipelines, environment strategy and deployment troubleshooting. Use when the request mentions deploy, deployment, sfdx, sf CLI, scratch org, sandbox, unlocked package, change set, CI/CD, GitHub Actions, release, rollback, source tracking. Finds and opens the exact skill package to read; it does not contain the guidance itself.
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。
GitHub Actions workflow patterns for React Native iOS simulator and Android emulator cloud builds with downloadable artifacts. Use when setting up CI build pipelines or downloading GitHub Actions artifacts via gh CLI and GitHub API.
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, mock up, build and review terminal UIs — full-screen TUIs, interactive CLIs and pickers, and plugin panels or popups inside a host (herdr, tmux, Neovim, k9s, lazygit). Use when designing, reviewing (from code, a live capture or a screenshot) or redesigning a TUI's layout, keybindings, states or colors, including requests that name a known TUI (lazygit, k9s, btop, yazi, helix, fzf); writing terminal mockups; building or fixing one in Ink, Textual, Bubble Tea, Ratatui or gum; mapping a color scheme (Catppuccin, Dracula, Nord, Gruvbox, Tokyo Night, Rosé Pine…) onto a TUI's theme; fixing misaligned glyphs, flicker, NO_COLOR, exit codes, signals, pipes or a broken terminal after exit; suspend or $EDITOR handoff; or profiling a slow TUI. Not for web or native GUIs, terminal-emulator, editor or shell-prompt themes.
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。
B2B sales pro AI — SPIN, MEDDIC, BANT, cold outreach, objection handling, pipeline
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。
ML Engineer — model deployment, MLOps pipeline, feature store, model monitoring, A/B testing, production ML system design
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 a finished piece from human-written fragments via an orchestrated two-loop pipeline - an assembly loop (assembler + cold reviewer) that sequences the fragments into a draft, then a polish loop (polisher + cold reviewer), then frequency analysis and a catalog-deslop pass with a regression gate. Two modes with opposite fidelity contracts - stitch mode preserves the author's exact wording, narrative mode preserves every idea but may rewrite freely. Use when the user says "mosaic", "assemble my fragments", "stitch these notes into a post", or provides a fragments file to turn into a finished piece. For writing from a topic with research, use article-factory; for prose from scratch, use the prose skill.
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。
Fully autonomous bug hunting pipeline — discover bugs in a scoped area using parallel subagents, independently triage each finding, fix confirmed issues with subagents, then audit all fixes against repo constraints and target platforms. Runs end-to-end without user interaction.
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。
Orchestrate AI coding agents (Claude Code, Codex, OpenCode) in isolated sandboxes using the @ai-hero/sandcastle SDK. Use when the user needs to (1) run agents AFK in Docker/Podman containers, (2) build multi-agent pipelines with plan-execute-review patterns, (3) run parallel agents on separate worktrees, (4) create iterative agent loops with maxIterations, (5) extract structured output from agent runs, (6) set up sandcastle in a new or existing project, or (7) write prompt files with template args and shell expressions.
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。
Firmware development for the WeAct Studio RP2350A Core Board (RP2350A — dual Cortex-M33 @ 150 MHz, 520 KB SRAM, 4 MB or 16 MB QSPI flash) in both of its revisions, RP2350A_V10 and RP2350A_V20 — the 40-pin Pico-2-shaped header, 3-or-4-channel 12-bit ADC, PWM, PIO, USB-C CDC, the V1.0 LDO vs V2.0 buck-boost power wiring, and the PlatformIO + arduino-pico (earlephilhower) setup around them. Use when working on this board, a WeAct RP2350 core board of unknown revision, or any Raspberry Pi Pico 2 / RP2350A clone: project setup, platformio.ini, board = rpipico2 vs weact_rp2350b, telling V1.0 and V2.0 apart, pin mapping, LED and KEY pins, powering it from a battery, 4 MB vs 16 MB flash, erratum RP2350-E9 pull-down trouble, BOOT/RESET UF2 flashing, or debugging why something on the board does not work.
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。