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Programmatically driving Salesforce Approval Processes from Apex — `Approval.process(ProcessSubmitRequest)` to submit, `ProcessWorkitemRequest` to approve / reject / reassign, recall semantics, querying `ProcessInstance` and `ProcessInstanceWorkitem` to find pending approvals, and the bulk-submit / bulk-action error-row handling. Covers when to use Apex-driven approval (system-initiated submission, batch approvals, custom UIs) vs leaving the platform's standard buttons in place. NOT for the Approval Process metadata definition itself (that's admin / declarative — see admin/approval-processes), NOT for Flow-based approvals (use flow/flow-orchestration-patterns).

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Use this skill when planning or executing the human side of an Agentforce or Einstein AI feature rollout — user trust-building, AI-specific training, structured feedback collection via the Feedback API, and adoption measurement via Agentforce Analytics. NOT for turning the Einstein for Sales features on — use agentforce/agentforce-sales-ai-setup. NOT for rollout training with no AI in scope — use admin/change-management-and-training.

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Detects missing or incomplete PrivacyInfo.xcprivacy privacy manifest files, enforcing Guideline 5.1 and Apple's required-reason API declaration policy enforced from May 1, 2024.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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AI ผู้เชี่ยวชาญทำสวน/ปลูกต้นไม้ — urban garden, seasonal planning, soil, pest control, indoor plants, Thai climate

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AI เชฟครัวร้านอาหาร — menu development, kitchen brigade, mise en place, food cost, seasonal menu, HACCP, Thai culinary context

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สร้างสคริปต์ podcast ที่ฟังแล้วไม่เบื่อ พร้อม show notes, timestamps, teaser social

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PR Manager AI — press release, media pitch, crisis communication สำหรับธุรกิจไทย

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Run work as an explicit event-driven state machine executed by subagents. The orchestrator is the runtime from the canonical loop — `state, effects = init(machine); execute(effects); while state is active: event = nextEvent(); state, effects = transition(machine, state, event); execute(effects); return state.output`. All routing judgment is authored ONCE, at plan time, into a machine file (states, event alphabet, transition table, budgets, final states); at runtime the orchestrator does pure table lookups and dispatches effects — it never decides, never authors, never ingests artifacts. Subagent/sync protocol is the timeboxed-iterating one: role prompts scaffolded once to disk and dispatched BY PATH, an initialiser preamble every subagent reads first, a self-populating cheatsheet, per-unit files, tiny structured returns, independent measurement gate. Use when the user says "workmachine", "run this as a state machine", "state-machine this", or when a goal has a small number of clearly named phases with explicit outcomes (built / passed / failed / blocked) and you want the routing to be auditable rather than judged on the fly. For clock-driven loops use timeboxed-iterating; for wide graphs with judged edge criteria use workgraph.

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Build a timeframe-bounded research dossier by decomposing a topic into year orchestrators and month-level research passes that each write one condensed paragraph plus sources directly into a target file. Use when the user asks for a dossier, timeline, chronology, release history, or month-by-month research bounded by specific years or months. Not for timeless explainers or single-document summaries with no temporal scope.

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Build a HyperFrames video composition from a brief/script autonomously. Uses separated builder and critic subagents — builders never review their own work. Quality decisions are grounded in project context (identity, audience, goals) and established visual/motion design principles. Measurable criteria (text size, contrast, overflow) are verified with tooling, not visual judgment. Use when the user says "make this video", "implement this brief", "build this composition", or provides a video script/brief to implement.

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Create custom modules for [Harbor Boost](https://github.com/av/harbor/tree/main/boost), an optimizing LLM proxy. Use when building Python modules that intercept/transform LLM chat completions—reasoning chains, prompt injection, structured outputs, artifacts, or custom workflows. Triggers on requests to create Boost modules, extend LLM behavior via proxy, or implement chat completion middleware.

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Build the upload archive for a plugin payload in this distribution repository. Use when asked to package the plugin, produce the submission zip, cut a plugin release artifact, or prepare a Codex/OpenAI Plugin Portal upload. Covers host selection, the pre-flight gates, and what to do after the archive exists.

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Expert in proof techniques, mathematical reasoning, and rigorous argumentation for students learning to write proofs

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Rigorous probability theorist covering measure-theoretic probability, stochastic processes, and advanced probability

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Rigorous analysis expert covering limits, continuity, sequences, series, and measure theory foundations

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Expert in Asian philosophical traditions including Buddhism, Taoism, Confucianism, and Hindu philosophy

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Personal financial planner for Thailand-based users. Use this skill when the user asks for help with budgeting, tax planning involving Thai-specific products (SSF, RMF, Thai ESG, Thai ESGX, Easy E-Receipt), debt strategy at Thai bank rates, retirement planning involving ประกันสังคม (มาตรา 33/39/40), กบข., PVD (provident fund), or any personal-money question framed in baht and Thai life events. Strong activation phrases include "ช่วยวางแผนการเงิน", "วางแผนภาษี", "ปลดหนี้บัตรเครดิต", "ลดหย่อน RMF", "Help me with my Thai finances", "How much should I save in Thailand", "ม.33 vs ม.39", "เกษียณต้องเก็บเงินเท่าไหร่". Do NOT use for non-Thai financial contexts (US 401(k), Canadian RRSP, UK ISA, etc.) — different skills exist for those.

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Adaptive Swift/iOS quiz with two modes. Classic is lighter, Mastery requires mechanism-level reasoning.

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Aggregate friction log files across a time period to identify recurring patterns, classify each cluster by intervention type, produce a prioritized action plan with success criteria, and calculate convergence metrics. Complements `skmtc-retro` (which captures per-session signal) by acting as the system's actuator: turning accumulated observations into decisions. The primary output is a review document that makes the "is it getting better?" question answerable. Use this skill when the user asks to "review retros", "review friction", "run a retro review", "what's the pattern across sessions", "what should we fix next", or on a periodic cadence (monthly, pre-release). Also run after a cluster of sessions on the same feature area to surface systemic issues before they compound. Distinct from `skmtc-retro` — that skill captures per-session observations; this skill synthesizes them into decisions. Do not run this skill as a substitute for a per-session retro.

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Write, restructure, or review documentation — tutorials, how-to guides, reference pages, concept/explanation docs, API references, READMEs, changelogs, release notes, and troubleshooting guides. Distills documentation craft from Mintlify's guides (compiled from technical writers at Stripe, GitHub, Amplitude, and Anaconda), the Diátaxis framework (including the compass and per-type voice), the Google and Microsoft style guides, Every Page Is Page One, Write the Docs, and Docs for Developers: audience analysis, content-type selection, style and word-level rules, procedure writing, structure for humans and AI agents, code-example standards, page templates (templates.md), mechanical enforcement and llms.txt (mechanics.md), maintenance, and success metrics. Use this skill when the user asks to "write docs", "document this feature", "improve this page", "review these docs", "write a tutorial / how-to / reference page", "structure the docs", "write API documentation", "write a README / changelog", or when authoring any file under a `docs/` tree. For SKMTC docs specifically, this skill governs the *craft* (what makes the page good); the content split between skills, `llms.md`, and the docs tree is governed by `docs/skills/README.md`. Distinct from `skmtc-retro` (captures observations about work) and the `skmtc-*` operational skills (guide doing the work) — this skill guides writing *about* the work for readers.

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Generate agent handoff notes for shift changes or ticket reassignment

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Adopt declscope on an existing Go codebase and drive its diagnostics to zero. Read this when introducing declscope to a repository, when choosing its configuration, when running declscope shrink for the first time, or when clearing a declscope baseline. Covers sizing each rule before enabling it, reading the diagnostics as structure, the remedy for each shape, and the measurement traps that produce false confidence. For writing new code in a repository that already runs declscope, use declscope-authoring.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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。