「ambiguity」の検索結果

Improve and rewrite user prompts to reduce ambiguity and improve LLM output quality. Use when a user asks to optimize, refine, clarify, or rewrite a prompt for better results, or when the request is about prompt optimization or prompt rewriting.

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Generate or update docs to match the code, citing each claim to path:line and asking on ambiguity; runbook docs also get a check-only validation script. Don't use for API-reference autogen (JSDoc/Sphinx), landing pages, or CLAUDE.md/AGENTS.md.

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Applies the existentialist and feminist philosophy of Simone de Beauvoir, French philosopher and author of The Second Sex. Reach for this skill whenever the user is grappling with existential dilemmas, moral ambiguity, gender dynamics, societal expectations, or relationship ethics. Trigger this skill for questions about freedom versus responsibility, bodily autonomy, marginalization, or when the user feels trapped by external labels or "immanence." Use this to help the user reclaim their agency, recognize the social construction of their situation, and make authentic choices that expand their transcendence while respecting the interconnected freedom of others.

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Allows you to view the user's screen as well as several hours of history. Use when the user makes a reference to their recent work, for which it'd be helpful to see the screen. This skill MUST be used whenever you need to resolve ambiguity in a user request, where the user hasn't specified enough context to do the task. Examples include disambiguating the specific user/app/document/error the user is referring to. You must also use this skill if the user asks about any question regarding Chronicle or asks what you can see from the screen.

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Socratic spec refinement — clarify WHAT a phase delivers with ambiguity scoring before discuss-phase. Produces a SPEC.md with falsifiable requirements locked before implementation decisions begin.

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Catalog of informal fallacies with definitions, canonical examples, and detection heuristics. Covers relevance fallacies (ad hominem, straw man, red herring, appeal to authority, appeal to emotion), presumption fallacies (false dichotomy, complex question, begging the question), ambiguity fallacies (equivocation, amphiboly, accent), causal fallacies (post hoc, slippery slope, correlation-causation), and statistical fallacies (hasty generalization, cherry-picking). Use when evaluating arguments for rhetorical or informal reasoning errors.

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Use the moment a request is underspecified and you are about to act on an ASSUMED goal. Enumerate the candidate goals the request could mean, project the action or artifact each one implies, and check whether they diverge: if two or more plausible goals lead to materially different actions or artifacts, ask exactly ONE targeted clarifying question; otherwise proceed on the most-likely goal and state the assumption in one line. Clarification is an evidence-producing action, not a delay — but one question, never an interrogation. Distinct from gsd-spec-phase (GSD-phase-bound, emits a heavy SPEC.md) and intent-router (fetch strategy, not goal disambiguation). Backed by agent goal-state inference (arxiv 2606.16813v1). Triggers on acting under an assumed goal when the request admits more than one materially different reading.

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Analyze and improve processes or systems using an explicit semantic model, adaptive instruction resolution, evidence-driven change, and claim-level validation. Use when reviewing process quality, resolving ambiguity or gaps, deciding how much procedural detail is necessary, or validating consequential process behavior.

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Write reliable prompts for Agentica/REPL agents that avoid LLM instruction ambiguity

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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for parser differentials, HTTP normalization gaps, ambiguous headers, path decoding drift, transfer-framing mismatches, and request smuggling routes. Use when the user asks to trace proxy and backend parse differences, conflicting path normalization, Host or forwarded-header ambiguity, CL/TE issues, or routing outcomes that differ across hops. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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Use for requirements interviews. Triggers: interview, ambiguity, contradiction scan, ask me questions, I phase, 인터뷰.

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Ask the minimum clarifying questions before implementation when requirements are ambiguous or missing crucial details

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Creation and genesis — generative ideation from void, structured emergence from ambiguity, and the discipline of bringing new patterns into existence. Maps Brahma's creative power to AI reasoning: transforming cleared space into coherent new structures, seeding possibilities, and nurturing nascent ideas through their fragile early stages. Use after shiva-bhaga dissolution has cleared stale patterns, when facing a genuinely novel problem with no obvious template, or when incremental modification has reached its limits and a fresh design is needed.

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Use this skill when the user wants local Obsidian note or metadata work done primarily through documented official desktop `obsidian` CLI commands, with limited local vault filesystem support when needed for parent folders, path checks, ambiguity resolution, verification, or safe vault structure operations. Best for local vault search, note reads, exact-path note updates, backlinks, tasks, properties, templates, and local history. Do not use it for runtime admin, devtools diagnostics, workspace/navigation administration, community tools, `obsidian://` launcher tasks, Headless Sync or Publish workflows, or plugin-specific APIs.

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Prompt engineering and task-specification skill for improving prompts, agent instructions, system messages, evaluation prompts, coding-agent tasks, multi-step workflows, and LLM output reliability. Use when rewriting vague prompts, adding constraints, reducing ambiguity, improving safety, creating reusable prompt templates, or converting goals into actionable agent instructions.

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Use when a task needs the judgment of a computer programmer implementing to a given specification — translating a design/pseudocode/systems-analyst spec into working code in a specified language, flagging spec ambiguity before writing code, maintaining or modernizing an existing codebase against its original contract, or writing unit tests that prove the implementation matches the spec (not designing the system the spec describes).

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Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.

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对法律文本——合同、法规、规章或裁判文书——进行对抗式压力测试，检验其解释上的歧义： 找出受其约束的人们日后会对其含义产生分歧的薄弱接缝，并将每一处转化为具体的争议场景， 附双方论点、可能的结果和修复方案。当有人希望对法律文件进行压力测试、红队审查、 审计，或找出薄弱点、漏洞、缺口、歧义或起草问题时使用；当起草人希望在合同、法规、 规章或裁判文书发布前将其收紧时使用；当诉讼律师希望从裁判文书或合同中挖掘论点时使用； 或当有人交来一份法律文本并询问它会在哪里引发争议或进行争点识别时使用。对合同审查、 法规歧义分析、裁判文书射程分析和起草质量检查都触发本技能——即使使用者从未说出 "压力测试"或"歧义"。

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将法律文本（合同、法规、规章或司法意见）的诠释性歧义审查转化为精美的交付物。可生成多页面网站（默认）、单页面交互式网站、Microsoft Word 文档、PowerPoint 演示文稿或 LaTeX/PDF。当用户持有压力测试结果、歧义审查或"这将在何处被诉"场景分析，并希望发布、演示或分享时使用。触发词包括"发布审查报告"、"据此制作报告"、"将其转化为网站/演示文稿/简报/Word 文档"、"制作 Netlify 网站"、"制作这些歧义点的幻灯片"、"渲染场景"、"打包这份压力测试"、"生成 LaTeX 版本"等。与歧义压力测试技能（底层分析）配套使用，但也适用于任何结构化或半结构化的歧义分析。视觉设计有明确风格倾向，但用户可覆盖颜色、字体和品牌标识。大多数格式需要 Python 和可写的文件系统；单文件 HTML 在主机两者皆无时仍可运行。

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Refine a draft prompt before sending to a coding agent: decomposes goal/context/constraints, flags ambiguity, emits a model-agnostic rewrite. NOT for prompts from scratch (mk:brainstorming).

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Use when refining a draft user prompt before sending it to a coding agent. Decomposes goal/context/constraints/acceptance/output-format, detects ambiguity and model-coupled framing, then emits a model-agnostic rewrite. Supports --analyze, --score, and --deep. NOT for prompts from scratch (mk:brainstorming), plans/reviews (mk:elicit), implementation plans (mk:plan-creator), or general codebase scouting (mk:scout).

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Disciplined goal-to-execution workflow for any user task. Parses objective, asks only for materially missing requirements via ambiguity-gate, inspects repo via idea, plans via sdd-dev or deep-plan, executes safely under project conventions, verifies completion per R020, and reports changed files with evidence. Use when user invokes /goal <task>.

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Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus strategy.

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Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchmarked on BRCA1/2, Cuella-Martin 2021 DDR saturation), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end; 4-7 for ABE7.10), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

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。