「raise」の検索結果

Use this skill when the user wants the full existing-registry Trust Magnitude inspector: understand why a named Gaia skill has a particular star rank, what its Trust Magnitude (TM) score is, which evidence rows are contributing (or dead), how far it is from the next grade, or what evidence to add to move the needle. Also use when asked for a full leaderboard of all named skills ranked by TM. Triggers: "why does X have 3 stars", "what's the TM for X", "inspect TM", "explain the score for X", "what evidence would rank up X", "show leaderboard", "rank all skills by TM", "what's dragging down X's score", "dead evidence rows", "how close is X to grade A/S".

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。

Close out a review/meta branch that lands an evidence-ingest / curation batch: create the named nodes, ingest verified evidence, appraise Trust Magnitude, calibrate stars, wire suites, correct upstream naming, stage ONLY the intended artifacts (dropping CRLF churn and blocking leaks), validate, and open the PR. Use when a curation/ingest batch is verified and ready to land, or when the user says "close the review/meta branch", "land the ingest batch", "wrap up the intake PR", "/gaia-review-meta-close". Wraps the deterministic hygiene in scripts/review_meta_close.py and gates the judgment calls (calibration, Origin, suite promotion, naming, merge) behind explicit human checkpoints.

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。

Post-review intake closing skill. After an intake is resolved (whether accepted via review/meta PR merge or rejected/deferred at triage/evidence review), run this skill to post standardized, personalized closing comments on the PR (if any) and each intake issue. Comments include: evidence pipeline findings with /trust-appraise TM output, per-row artifact scores, decisions rationale, path-to-promotion/acceptance guidance, and a mandatory badge status note including the universal Powered by Gaia badge. Use after curation completes, before or immediately after merge for accepted intakes, or immediately upon rejection.

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。

Fuse all of a contributor's named skills into a single fusion node (suite capstone) in the registry. Use when the user says "fuse <contributor>'s skills", "create a suite for <contributor>", "build a capstone for <user>", "fuse all of <user>'s named skills", "create a suite fusion", "consolidate <contributor> into one skill", or explicitly types /gaia-fuse-full-suite. Also triggers when a contributor has accumulated 3+ named skills and the user asks what to do next with them, or when someone asks "can we make a suite from these?". This skill: collects component IDs from registry/named/<contributor>/, verifies nodes exist, researches evidence and appraises Trust Magnitude, runs `gaia dev fuse` to write the fusion node and suite manifest, validates, and opens a PR on a review/meta/ branch.

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。

Atomically ingest one already-verified evidence row into one named Gaia skill using the CLI, then appraise Trust Magnitude and propose—never silently apply—a calibration. Use after /ev-pipeline has verified an approved intake source, or whenever a curator says "ingest evidence", "add verified evidence", "gaia-ingest", or "turn this source into registry evidence".

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。

Batch wrapper for /gaia-ingest. Ingests a bounded set of already-verified evidence rows through CLI-only writes, uses --no-build on every row, appraises Trust Magnitude for every affected named skill, then runs exactly one build and validation pass. Use for an L4-approved intake after /ev-pipeline; benchmark-result rows also require a Phase 2B report and explicit human gate approval.

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。

Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.

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 task needs the judgment of an animal trainer — designing a behavior-modification plan for a specific problem behavior, choosing a reinforcement schedule and deciding when to raise criteria, structuring session length and frequency to avoid fatigue or frustration plateaus, or setting a handler/public safety protocol around a training animal.

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。

Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas.

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。

Author and own ONE system's design document (the A–K SYSTEM GDD) at .unikit/gamedesign/systems/SYS-<slug>.md — the depth layer of the GDD, single-system scope. Create the skeleton and walk the collaborative section-cycle to write its parameters, rules, formulas, and acceptance criteria ("detail the combat system", "write the GDD for the inventory system", "fill in the rest of this system doc", "spec out the parameters of X"), AND revise it after approval under the delta discipline ("raise the damage by 10%", "tune the economy", "rework the status system", "nerf X", "change this rule") — a number change is Tuning, a small rule a Tweak, restructuring a Rework; each bumps the version and logs the delta. To add a NEW system or restructure the system map use /unikit-gd-spec; to edit GAME.md content use /unikit-gd-spec; the moment an edit also touches a second system, a content type, a flow, or GAME.md (two or more zones) use /unikit-gd-apply; to invent a brand-new game concept use /unikit-gd-brainstorm.

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。

Product Discovery through an 18-task methodology in 6 blocks — from market analysis to an investor-ready financial plan. Use when someone wants to run Product Discovery, validate a startup, assess a market hypothesis, prepare for an investment round, or raises an adjacent topic — idea validation, niche sizing, competitor teardown, product-market fit, pitch preparation, startup unit economics, geographic expansion into a new country, go-to-market for SEA / Asia / MENA. People rarely name PD explicitly; they say 'is there a market for this', 'assess my idea', 'I need a deck for an angel', 'we are entering Thailand/Singapore/UAE, how do we size it'. Use the skill in those cases too. Three modes: Light (~45 min, idea stage), Full (~2-3 h, MVP with customers), Geographic Expansion (~2 h, product entering a new geography). Produces 3-4 artifacts in /mnt/user-data/outputs/: one-pager.pptx, financial-plan.xlsx, presentation.pptx, optionally interview-guide.docx.

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。

Prepares jurisdiction-specific charitable solicitation registration packages for U.S. nonprofits. Covers multi-state nexus analysis, exemption eligibility, URS filing strategy, financial disclosures, and professional fundraiser documentation. Use when a nonprofit plans fundraising campaigns, online donation drives, direct mail, telephone solicitation, or any cross-state charitable solicitation.

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。

Produces a practitioner-grade Blue Sky compliance memorandum and execution plan for U.S. securities offerings. Analyzes NSMIA covered securities preemption, classifies offerings under Regulation D/Rule 506 or alternative federal exemptions, maps state jurisdictional nexus distinguishing offers from sales, and generates a state-by-state filing plan with Form D/EFD workflow, fee schedules, amendment triggers, and renewal calendars. Use this skill when advising on private placement Blue Sky compliance, Reg D notice filings, multi-state offering coordination, state securities registration exemptions, Form D filings, EFD submissions, or state fee budgeting. Also trigger when the user mentions covered securities preemption, NSMIA analysis, or asks about state notice filing requirements. Even if the user just says "Blue Sky" or "state filings for our raise," use this 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。

Generates a cross-referenced U.S. nonprofit annual filing package for state charity-bureau registration. Produces Full Compliance Package, Form-Field Mode, or Executive Briefing with source citations, disclosure cross-checks, and filing instructions. Trigger when the user drafts annual report submissions, renewal packets, penalty-of-perjury certifications, or charity bureau filings. Also trigger on Form RRF-1, CHAR500, BCO-10, nonprofit annual report, charity registration renewal, or fundraiser disclosure attachments.

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。

Weekly-cadence engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas. Use when asked to "weekly retro", "what did we ship", or "engineering retrospective". Proactively suggest at the end of a work week or sprint. NOT for per-session or per-task reflection (see mk:memory / Phase 6 Reflect).

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 or adapt a dual-theme soft-neumorphic UI board for dashboards, workspaces, settings panels, admin tools, and design-system previews. Use when Codex should build or restyle interface work in a tactile soft-neumorphic style with both light and dark themes, shared semantic tokens, large radii, raised surfaces, inset fields, restrained accent states, and a complete preview board covering foundations, controls, navigation, data display, and feedback.

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。

Derive an execution roadmap from a project's vision/spec docs by first principles, then diff that fresh derivation against the existing roadmap to prove the task ORDER is correct. Use this whenever the user wants to review, validate, re-derive, or sanity-check an execution roadmap or delivery plan; asks whether the tasks/epics are "in the right order"; suspects a task is scheduled before its prerequisite (a producer-after-consumer / dependency-inversion error like "OpenAPI after the web client that consumes it", OR a cross-cutting inherited default like observability/auth/tenancy scheduled after the feature work that inherits it — a seam-after-consumer error); wants to find missing tasks, circular dependencies, or over-fragmented epics; or wants to compact a plan to raise velocity. Also trigger on "review the roadmap", "re-derive the plan from the spec", "is this the right build order", "check the dependency graph of our epics", "which task should come first". It reads the vision + spec corpus, decomposes actors → use-cases → modules → epics → typed tasks, identifies cross-cutting inherited-default seams, builds a real dependency graph with hard + soft (seam) edges (deterministic helper), breaks cycles, topologically orders, compacts, and emits a derived roadmap PLUS a delta report against the current one — it never blindly overwrites a hand-crafted roadmap.

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。

In patients with primary aldosteronism receiving mineralocorticoid receptor antagonist therapy, the guideline recommends titrating the MRA dose upward to raise renin when blood pressure remains uncontrolled and renin is suppressed. Consider this step when hypertension is not at goal despite MRA therapy and plasma renin activity (or direct renin concentration) is low.

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。

Prices research, tests, surveys, dashboards and experiments before running them, by computing how much the result would raise expected utility - which is zero whenever no outcome would change the decision. Use when someone proposes an A/B test, user survey, market study, competitor analysis, analytics build, consultant, pilot, or "let's gather more data", and when deciding how large a study needs 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。

Use when shaping an engineering hiring loop — stages, take-home vs live, calibration, bar-raiser, signal-vs-noise audit. Triggers on 'design our interview loop', 'audit our hiring bar'.

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 shaping a capital-raise pitch — why-now / why-us / why-this framing, market-size reasoning, traction-story construction. Triggers on 'tighten the pitch', 'why-now is weak'.

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。

中国药品权威数据库查询 — NMPA(国家药监局) + CDE(药品审评中心) + DrugBank(英文名/ATC) + PubChem(化学结构) 多源融合,提供(通用名 ↔ 商品名 ↔ 英文名 ↔ 适应症 ↔ NMPA 批文号 ↔ 上市日期)精确映射。Make sure to use this skill whenever the user mentions 药品名 / 通用名 / 商品名 / NMPA / 国药准字 / 药监局 / CDE / drug brand name / generic name / drug registry / pharmaceutical lookup / drug verification / 药物核验 / 商品名对应 / drug cross-check / drug authority lookup. 在任何 medical / market-sizing / clinical / disease-research 报告生成阶段,**所有药品提及必须先调 lookup_drug() 验证**,严禁 LLM 凭训练记忆拼凑通用名↔商品名↔英文名对应关系。LLM 训练截止后药企会更换商品名、新药持续上市,记忆完全不可信。Iron Law:lookup 返回 None → 调用方必须 raise UnverifiedDrugError,不允许 fallback 到记忆。免费公开数据源,跨平台:Claude Code / Codex / Gemini CLI 三端均可 auto-trigger,实现仅依赖 httpx + beautifulsoup4 标准生态,不绑定任何厂商 SDK。

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。

Author a complete both-seats coaching lesson for a BBA-NATIVE bidding scenario and produce all served artifacts in one consistent pass: curate the pool → tokenized coaching-curated/<scn>.pbn → gated promote to served coaching/<scn>.pbn → resolve to South=student coaching-non-rotated/<scn>.pbn (bridge-classroom) → update both toc.json files. Invoke as `/coach-lesson <Scenario>` (e.g. `/coach-lesson Fourth_Suit_Forcing`). Use ONLY for conventions BBA bids natively (its bba/<scn>.pbn [Auction] is the answer key — Basics, New_Minor_Forcing, Fourth_Suit_Forcing). For conventions BBA CANNOT bid (Spiral Raises), use the py/spiral_auction.py substitution path instead — not this 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。

Write a test that can fail. Use whenever adding a test, when a suite is being created for a project that has none, when asked to raise coverage, or before claiming a behaviour is covered. Covers the oracle problem, when to choose the integration suite over the unit one, and how to prove the test is not tautological.

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。