「first run」の検索結果

Use this skill whenever the user wants to see how their branch will look on the live Gaia website before merging — "preview this branch", "show me what this looks like", "deploy a preview", "trigger a design preview", "can I see the site with these changes", "preview the skill profile", "check how this renders on the site", "spin up a preview deployment". This skill dispatches a Cloudflare Worker preview deployment for the current branch via GitHub Actions — no local build tooling required. Ideal for containerized environments, design reviews, and confirming that newly curated skills or profile pages render correctly before a PR is merged to main. The preview does NOT regenerate docs; it deploys `docs/` from the branch as-is. If your change depends on regenerated Class S artifacts, run `gaia dev docs` and commit those first — the preview then reflects them.

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。

Scan project once per session. Run first — all other skills depend on this.

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Use when diagnosing or improving frontend performance, Core Web Vitals, bundle size, runtime rendering cost, network waterfalls, memory leaks, long tasks, Lighthouse findings, or performance budgets; Chinese triggers include performance optimization, page freeze, slow first screen, bundle volume, Web Vitals.

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Use when a task needs the judgment of a First-Line Supervisor of Entertainment and Recreation Workers (camp, pool, ride crew, attractions, or activities floor) — building today's shift roster against certification/ratio requirements when call-outs hit, running a pre-shift briefing, triaging an escalated guest complaint, deciding whether a new hire is ready to sign off independent, or choosing when to consolidate or close a program area for the day.

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Use when a task needs the judgment of a credit authorizer, checker, or clerk — deciding a real-time point-of-sale credit authorization against available credit, verifying a credit application's income/employment documentation, issuing an adverse-action notice on a declined application, or intaking a chargeback/dispute within its filing window. Distinct from a credit analyst, who underwrites commercial credit with a multi-year financial spread and a risk rating — this role runs a high-volume rules engine against a preset limit, it does not assess creditworthiness from first principles.

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Use when a task needs the judgment of a CNC Tool Operator — diagnosing a dimension trending toward its tolerance limit across consecutive parts before it actually crosses, deciding whether to apply a tool wear offset or flag the tool for replacement, evaluating whether a failed part traces to work-holding repeatability versus a program or tool issue, or verifying a setup change with a first-article inspection before running production quantity.

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Use when a task needs the judgment of a corrections sergeant or shift supervisor — mandating overtime to restore a post below minimum staffing, reviewing and countersigning a subordinate's use-of-force report, running progressive discipline for a line officer, assuming incident command during a multi-officer response, or handling a first-level inmate grievance.

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Use when operating Volcengine TLS (日志服务) through volclog in agent or CI sessions and you need a contract-first workflow for runtime selector resolution, discover/describe/exec routing, App resource or Topic resolution, envelope reading, large-result delivery, and error recovery without guessing commands, fields, or shortcut flows. 当用户提到火山引擎日志服务 TLS、volclog、日志检索/分析/导入/导出、 日志项目或日志主题等操作时调用。

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。

Deno runtime guidance — TypeScript-first development, permissions, standard library, and deploying Deno apps.

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Run exploratory data analysis on a dataset and produce a structured report. Use when the user says "explore this dataset", "EDA on X", "analyze this data", "what's in this dataset", "summarize this data", "first look at the data", "understand this dataset before modeling", "data quality check", "describe this dataframe", or wants to understand a new dataset before building models or dashboards.

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Use this skill when asked to search public content platforms such as Bilibili or YouTube, pick the top relevant results for a topic, and summarize each item with links and brief overviews. Uses Runtime-aware routing — opencli first, fetch second, browser last.

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Use when the user provides a plot summary, scene idea, character relationship, emotional beat, or short video concept and wants a cinematic AI video prompt. First diagnose the story, then rewrite it into a model-ready prompt for Kling, Seedance, Veo, Sora, Runway, Jimeng, or general AI video models.

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。

Explicit-only last resort. Draws game art from code, with no image model - pixel sprites from a PixelSpec, flat vector props, UI and icons from portable SVG, rigged characters with planted feet, FX with a JS runtime, seam-proven autotiles, playable top-down layouts with collision, stylized parallax and ambient plate motion - palette-exact, with QA and review sheets. Use only when the user explicitly asks for code-drawn art, or when generate2dmedia route_media.py finds no image or video route (no-route, exit 3). Never the default - image generation (generate2dsprite, generate2dmap, video2dsprite) comes first for characters, props, maps and FX. Not for painterly, detailed or likeness art, HD-2D plates or realistic fire, smoke or water. Always disclose the result as code-drawn.

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Use when learning, designing, or explaining Salesforce Reports and Dashboards from first principles — report types, custom report types, groupings, bucket fields, summary formulas, charts, dashboard components, dynamic dashboards, report subscriptions, and folder permissions. Triggers: 'how do I build a report', 'what report type should I use', 'set up a dashboard', 'joined report limits', 'bucket field vs formula', 'dynamic dashboard running user'. NOT for CRM Analytics (use crm-analytics-* skills). NOT for Einstein Discovery predictive analytics. NOT for troubleshooting missing report data caused by sharing model issues (use admin/reports-and-dashboards for that).

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Use this skill when building or maintaining a Requirements Traceability Matrix (RTM) on a Salesforce project: one row per requirement, columns for source, user-story id(s), test-case id(s), defect id(s), sprint, release, and status. Covers forward traceability (req → story → code → test) and backward traceability (test → req). Trigger keywords: RTM, requirements traceability matrix, audit trail for salesforce delivery, traceability for steerco, deferred requirement tracking, regulatory traceability. NOT for eliciting the requirements in the first place — use admin/requirements-gathering-for-sf. NOT for user-story authoring — use admin/user-story-writing-for-salesforce. NOT for UAT test design — use admin/uat-test-case-design. NOT for Apex test design — use apex/test-class-standards. NOT for backlog prioritization — use admin/moscow-prioritization-for-sf-backlog. More triggers: traceability.md, build-layer traceability, REQ-XXX and FG-XXX id mapping, artefact-to-requirement orphan report, coverage-gap report, check_rtm.py --manifest-dir, owning run-time agent column, design decision ref column, metadata API name in a traceability row.

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 configuring or troubleshooting Salesforce Enterprise Territory Management (ETM): territory models, territory types, territory hierarchies, account assignment rules, opportunity territory assignment, and forecast by territory. Trigger keywords: territory model, territory hierarchy, ETM, assign accounts to territories, territory forecast, territory activation. NOT for choosing the coverage model first — use admin/territory-design-requirements. NOT for role hierarchy — use admin/sharing-and-visibility. NOT for Field Service ServiceTerritory — use admin/fsl-service-territory-setup. Trigger keywords: Territory2, Territory2Model, Territory2Rule, territory2Model-meta.xml, ObjectTerritory2Association, UserTerritory2Association, territory assignment rule, run assignment rules, territory access level, Manage Territories permission.

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Produce a researched long-form article from a topic prompt via an orchestrated pipeline - research agent (first-person sources, working-definition gate), narrative-architecture outline, writer/cold-reviewer loop with an explicit ACCEPT/REVISE verdict contract, then a catalog-deslop pass with a regression gate. The orchestrator dispatches subagents only; the writer never judges its own draft. Use when the user says "article factory", "write an article about X", "run the article pipeline", or asks for a researched long-form piece produced end-to-end. For essays and micro posts in the user's own voice without a research stage, use the prose skill instead.

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Run a task iteratively over a user-specified duration by dispatching subagents. The orchestrator stays strictly linear and time-checked, but each iteration fans out MULTIPLE subagents in parallel over independent units (and those subagents may fan out further). Shared state — a compact progress digest and a growing environment cheatsheet — lives on disk and is passed to every subagent BY REFERENCE, killing the per-subagent rediscovery tax. Every subagent first reads an initialiser preamble that points it at that shared state and makes it write findings back, so the cheatsheet populates itself; role prompts are produced ONCE on the filesystem by a single scaffold command and dispatched by path, never re-typed per dispatch. Subagents persist their results to disk and return only a tiny status, so the orchestrator's context stays small and lasts. The orchestrator classifies the goal as FINITE or OPEN-ENDED: finite lists may finish early; open-ended goals use the full duration, never idle, never manufacture busywork. Use when the user gives a task and a duration and wants it ground out iteratively by subagents over that time.

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Use the Skmtc CLI to scaffold projects, install or clone generators from JSR, configure schema sources and enrichments, and produce code artifacts from an OpenAPI v3 or GraphQL SDL schema. Teaches the workspace mental model (`<root>/.skmtc/<project>/`, client.json, bundle, manifest) and the agent contract (strict text / strict JSON modes, exit codes, recipe errors, `agent-context` + `doctor`); the command surface itself is discovered from the binary — `skmtc --help`, `skmtc <cmd> -h` — rather than carried in this skill. Use this skill when the user asks to "run skmtc", "generate code from an OpenAPI schema", "install a skmtc generator", "scaffold a skmtc project", "watch a skmtc project", "configure enrichments", "publish a stack", "deploy to skmtc-hub" (the command is `publish`; there is no `deploy`), "skmtc in CI", or invokes any CLI subcommand. For *authoring* a generator package (Projections, Snippets, transform functions), defer to `skmtc-generator`. When something is broken (no output, wrong output, error messages, a failed bundle build), verify before proposing a fix: read the manifest and the parse issues, and reproduce the failure first.

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。

Run /fl on a ticket with moflo's three standing considerations loaded first — cross-platform (Rule

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Remove AI-generated code smells (slop) from branch changes or an explicit file list. Locks behavior with regression tests FIRST, then runs categorized cleanup via parallel `deep` agents in batches of 5, then verifies with quality gates. Covers 10 slop categories including performance equivalences, excessive complexity (object annotations, if/elif variant chains), and oversized modules (250+ pure LOC with mandatory modular refactoring). MUST USE when the user asks to "remove slop", "clean AI code", "deslop", "clean up AI-generated code", "remove AI slop", or wants to clean up AI-generated patterns from recent changes. Triggers - "remove ai slops", "clean ai code", "deslop", "cleanup AI generated", "remove AI slop", "clean up AI-generated code", "strip slop", "ai-slop cleanup".

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Good-faith divergent brainstorming for achieving a goal. Validates assumptions, then spawns brainstormers running different techniques (first-principles, working-backwards, lateral, analogical, constraints-shift, etc.) in isolation, and synthesizes the pool into a report of standouts, reasonable ideas, and hybrid combinations. Produces feedback only — no code, no tickets, no artifacts.

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White-box security audit. Blue-teamer and lead red-teamer run in parallel isolation for an independent first pass — neither sees the other's output during reconnaissance. A synthesis step categorizes findings into four prescriptive buckets (anchoring-suppressed, convergent, blue-flagged-unverified, divergent), producing a unified target list. Focused red-teamers then deep-dive each target. Iterates when exploit chains are discovered. Heavy and thorough by design. Advisory only — produces findings and proposes tickets; does not implement remediation.

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 local voice transcription instead of OpenAI Whisper API. Switches to whisper.cpp running on Apple Silicon. WhatsApp only for now. Requires voice-transcription skill to be applied first.

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。