「inputs」の検索結果

Use when curating, refreshing, expiring, calibrating, or equivalence-testing evidence and publication inputs for the active Safety Score model. Not for unrelated scoring models or copy-only changes.

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Dry-run Trust Magnitude for proposed named skills or suites before curation. Use when deciding whether a proposed suite deserves A/S treatment, when checking Fusion Score structural inputs, when comparing repo stars vs suite component counts, or when asked to appraise a candidate before adding it to the registry.

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Consult this skill BEFORE proposing a fix to any CLI gap or schema gap that touches evidence, trust magnitude, evidence types, evidence grades, the TM formula, or star-level promotion gates. Also read it whenever an issue mentions citations, stars, commits, contributors, views, reviewers, percentile, arxiv, github-stars-own, repo-own, social-signal, benchmark-result, peer-review, self-attestation, fusion-recipe, verifier-attestation, or proxy-containment — those are the 10 canonical evidence types and the raw inputs that feed their formulas. Also read it when asked whether a skill qualifies for a given star level (0★–6★): the Star Bar and promotion gates live in `META.md`, not in the TM methodology page. Reading both prevents proposing a calibration that satisfies the TM grade threshold but violates a Star Bar installability or link requirement.

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Skippable Phase 0 of the Gaia evidence verification pipeline. Use only on declared need for higher-quality Stage-2 evidence: a promotion candidate, or a skill that gaia-meta-sweep flagged as under-evidenced. Given a skill id, source repo/context, and needed evidence types (benchmark-result, arxiv, peer-review, social-signal), it runs Firecrawl searches, scrapes top hits to markdown, and appends discovered sources into Phase 1 inputs. Missing benchmark evidence is recorded as benchmark-source candidates first, not direct scoring rows. Trigger phrases: "discover evidence", "find new evidence", "Phase 0", "run ev-discovery", "search for benchmarks/papers", "under-evidenced", "promotion candidate needs stronger proof". This is the ONLY ev-* skill that searches the web for NEW evidence; other ev-* skills aggregate or verify known sources. Requires Firecrawl to be usable (`firecrawl --status` authenticated OR FIRECRAWL_API_KEY set); skips gracefully when neither holds.

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Uses symbolic execution (angr) to solve constraint-based malware problems — finding inputs that reach a target branch, recovering keys/passwords from a check routine, and brute-forcing opaque predicates — by generating a ready-to-edit angr harness. Activates for requests to use symbolic execution, solve a malware unlock check with angr, or recover an input that reaches a code path.

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Apply a formal state machine to issues — assign category (bug/enhancement/question/spike) and state (needs-triage → needs-info → ready-for-agent → ready-for-human → wontfix). Issues marked ready-for-agent become inputs to supergraph:plan. Use when processing a backlog, reviewing new issues, or preparing work for automation.

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Use when building or reviewing substantial forms with React Hook Form, Zod schemas, typed validation, dynamic fields, controlled third-party inputs, file upload, multi-step flows, dependent validation, or form performance. Do not use for trivial 1-3 field forms without validation; Chinese triggers include form, form validation, dynamic fields.

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Use when a task needs the judgment of a biomedical/bioengineering engineer — running a design control phase (inputs, outputs, verification, validation) for a medical device, scoring a Design FMEA and deciding whether a failure mode needs mitigation, planning a human factors/usability validation study, building a design traceability matrix, or writing a design verification or risk-mitigation report. Distinct from a regulatory-affairs-specialist (owns the submission/clearance process, not the engineering itself) and from a clinical-data-manager/biostatistician (trial data and analysis, not device design).

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Transform vague inputs into precision-optimized AI prompts for Claude, ChatGPT, Gemini, or other LLMs. Use when user mentions "optimize prompt", "improve prompt", "lyra", "prompt engineering", or needs help crafting effective AI prompts.

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Create or update the project constitution from interactive or provided principle inputs, ensuring all dependent templates stay in sync.

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。

Build excellent web forms: UX patterns, validation, accessibility, multi-step flows, and file uploads. Use for any form beyond trivial inputs.

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 needs broad parallel research across many independent inputs with a shared output schema.

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 and implement features from raw data for machine learning models. Use when the user says "engineer features", "feature engineering for X", "create features from this dataset", "transform these variables", "encode categoricals", "handle skew", "create interaction features", "lag features", "extract features from text or dates", "improve model performance with better features", or wants to move from raw columns to model-ready inputs.

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Validate Claude Ads contracts, scoring inputs, run bundles, capabilities, source freshness, safety, installation, uninstall, or release readiness. Use for ads validate, ads status, ads next, stale claims with missing tool access, maturity checks, ownership-manifest uninstall, preserving unrelated ads-* skills, checksum verification, preflight, QA, or release audits. Missing access must demote stale evidence for the run and block dependent release-current claims before recovery guidance.

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Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.

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Use when overriding the default UI of an Agentforce agent action's Apex input or output with your own Lightning web components via a LightningTypeBundle (schema.json + editor.json/renderer.json, lightning__AgentforceInput / lightning__AgentforceOutput targets, the @apexClassType binding). NOT for standard agent actions with primitive inputs/outputs (only Apex-class input/output can be overridden), NOT for Flow screen custom property editors (use flow/flow-custom-property-editors), and NOT for general LWC-in-Lightning-page work (use lwc/* skills).

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Systematically explore and test any software project (CLI, API, Backend, Library, etc.) to find bugs, usability issues, and edge cases. Produces a structured report with full reproduction evidence (exact commands, inputs, logs, and tracebacks) for every issue.

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Checks and fixes Chinese copywriting with autocorrect-backed workflows for text and single-file Markdown/TXT inputs. Use when user asks to normalize Chinese punctuation, spacing, full-width/half-width usage, or copywriting style. Also triggers for "校对", "检查排版", "中英文空格", "全角半角".

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Make a web app feel native on a phone — the small CSS and meta-tag fixes that separate "a website in a browser" from something that feels installed. Covers sticky hover states, tap highlight flashes, the 100vh bug, inputs that zoom the page, laggy taps, pull-to-refresh hijacking scroll, content under the notch, long-press selecting button text, carousels that scroll the wrong way, mismatched status bars, and the rule that you test on real hardware. Use when a web app is being built for or reviewed on mobile, when something "works in Chrome but feels wrong on my phone", when building a PWA, a bottom sheet, a carousel, a full-screen layout, or any touch interaction. For motion itself use animate; for React Native use animate-expo.

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Pick the right library for a given frontend task from a curated, opinionated list — numbers, OTP inputs, charts, command menus, virtualization, drag and drop, toasts, state, styling, and more. Only runs when explicitly invoked; it does not trigger on its own.

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。

Validate and sanitize all API inputs. Zod schemas, error responses, security considerations.

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Apply or adapt a dual-theme neo-glow control board for UI kits, design-system previews, settings surfaces, admin panels, control rooms, and dense operational interfaces. Use when Codex should build or restyle interface work with bright white or dark control surfaces, compact neumorphic cards, vertical icon rails, blue/cyan halo accents, glowing active states, luminous toggles, and a board focused on control states, inputs, and specification details.

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Time-travel debugging for DAG executions. Inspect agent state at any node, replay decisions with modified inputs, compare execution traces side-by-side, and identify where reasoning diverged. Inspired by LangGraph Studio's state-editing model and Temporal's event history. Activate on "debug DAG", "replay execution", "time travel debug", "inspect node state", "what went wrong at step", "compare runs", "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.

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。

Traces complete execution paths through DAG workflows. Records timing, inputs, outputs, and state transitions for all nodes. Activate on 'execution trace', 'trace execution', 'execution path', 'debug execution', 'execution log'. NOT for performance analysis (use dag-performance-profiler) or failure investigation (use dag-failure-analyzer).

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。