Results for “ambiguity”

Turn an interpretive-ambiguity audit of a legal text — contract, statute, regulation, or judicial opinion — into a polished deliverable. Produces a multi-page website (default), a single-page interactive site, a Microsoft Word document, a PowerPoint deck, or a LaTeX/PDF. Use whenever a user has a stress-test result, ambiguity audit, or "where will this be litigated" scenarios and wants to publish, present, or share them. Triggers on "publish the audit," "make a report from this," "turn this into a website / deck / brief / Word doc," "make a Netlify site," "build slides of these ambiguities," "render the scenarios," "package this stress test," "produce a LaTeX version," and similar. Pairs with the ambiguity-stress-test skill (the underlying analysis) but works on any structured or semi-structured ambiguity analysis. Visual design is opinionated but the user can override colors, fonts, and brand label. Most formats need Python and a writable filesystem; single-file HTML works where the host has neither.

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Adversarially stress-tests a legal text — a contract, statute, regulation, or judicial opinion — for interpretive ambiguity: it finds the seams where the people governed by it will later disagree about what it means and turns each into a concrete dispute scenario with both sides' arguments, the likely outcome, and a fix. Use it whenever someone wants to pressure-test, red-team, audit, or find weak spots, loopholes, gaps, ambiguities, or drafting problems in a legal document; whenever a drafter wants to tighten a contract, statute, regulation, or opinion before it issues; whenever a litigator wants to mine an opinion or contract for arguments; or whenever someone hands over a legal text and asks where it will be fought over or for issue-spotting. Trigger it for contract review, statutory-ambiguity analysis, judicial-opinion scope analysis, and drafting QA — even if the user never says "stress-test" or "ambiguity."

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Turn a vague idea into requirements ready for planning or implementation through focused, one-question-at-a-time interviews. Clarify intent, scope, constraints, success criteria, exclusions, and decision authority, while tracking ambiguity and checking assumptions against available project evidence.

• Clarify a vague feature idea
• Define success criteria before implementation
• Agree on exclusions and agent decision authority

Clarify what a development phase should deliver before discussing implementation. Through structured questions and ambiguity scores, the skill produces SPEC.md with testable requirements, explicit scope boundaries, and acceptance criteria for judging completion.

• Clarifying phase requirements before discussing implementation
• Defining scope boundaries and acceptance criteria
• Drafting a specification with recommended defaults

Rewrite long prompts into shorter, clearer instructions for language models. Preserve essential requirements while removing repetition, defining scope, and making output formats and success criteria explicit to reduce ambiguity and unnecessary tokens.

• Shortening a long prompt while preserving requirements
• Clarifying vague instructions and success criteria
• Making output formats and length limits explicit

Rewrite English tool descriptions, error messages, prompts, and agent instructions into clearer sentences using principles from Simplified Technical English. Preserve facts, conditions, and uncertainty, with an optional comparison explaining the rules behind each change.

• Clarify English tool descriptions
• Remove ambiguity from instructions between agents
• Rewrite English error messages and status reports

Turn a project description and existing project guidance into a requirements document. Organize expected behavior into EARS statements, a structured format for clear conditions and outcomes, and review coverage, ambiguity, and scope before saving.

• Turning a feature idea into testable requirements
• Clarifying what a feature includes and excludes
• Aligning requirements with existing project guidance

Mathematically rigorous Socratic interview system that drives ambiguity below 20% before any code is written. One question per message, weighted ambiguity scoring, brownfield-aware, outputs a complete PRD. Replaces discovery-interview with a stricter protocol.

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"/clarify", "clarify this", "keep asking until clear", "remove ambiguity", "clarify requirements", "clarify design", "clarify the plan", "질문 계속해", "모호한 게 없게", "명확해질 때까지", "계속 물어봐", "Q&A로 정리", "질문답변 기록", "요구사항 명확화", "설계 명확화". Relentless ambiguity-resolution interview that records Q&A under .hoyeon/clarify/<topic>/ and hands off to specify/blueprint/docs when clear.

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Hoyeon ambiguity-resolution interview for Codex. Use when the user invokes "$hoyeon-clarify", "/clarify", asks to keep asking until clear, remove ambiguity, clarify requirements/design/domain/plan, or says Korean phrases like "모호한 게 없게", "명확해질 때까지", "질문 계속해", or "Q&A로 정리".

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Use for sensemaking, frame revision, anomaly handling, questioning strategy, and ambiguity diagnosis with the Data/Frame model before action is chosen. NOT for deterministic classification, settled execution planning, or situations where the frame is already stable.

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Pre-routing ambiguity analysis — scores request clarity and asks clarifying questions when needed (inspired by ouroboros)

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Collapse meaningful ambiguity with contrastive examples. Use when a plan/design has several plausible meanings, requirements feel vague, examples would clarify intent faster than grilling, or the user asks to disambiguate, find ambiguity, use behavioral kernels, or ask contrastive questions.

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Systematic ambiguity resolution through tiered information gathering. Use when facing unclear requirements, unknown context, uncertain implementation choices, or any situation where guessing would be risky.

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Determine ambiguity audit scope, enumerate source documents from filesystem, and persist rubric file bindings

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Execute ambiguity audit document-by-document with implementer simulation, rubric scoring, cross-layer checks, remediation, and verdicting

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Orchestrate ambiguity auditing across selected layers with rubric-driven scoring, remediation flow, and next-step gating

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Use for sensemaking, frame revision, anomaly handling, questioning strategy, and ambiguity diagnosis with the Data/Frame model before action is chosen. NOT for deterministic classification, settled execution planning, or situations where the frame is already stable.

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Designs and audits native-unit capacity reservation, serial admission, ambiguity holds, stop reserves, effect closure, and control-disjoint settlement handoff for consequential agent attempts. Use when scarce capacity must be conserved across retry, rework, resurrection, or compensation. NOT for scheduling work, choosing models, pricing unlike resources into one scalar, authorizing effects, grading work, paying workers, or asserting runtime containment.

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Turn a design request into a concrete specification covering colors, fonts, layout, mood, spacing, and constraints. Generate DESIGN.md and an HTML preview, with a clear report of defaults applied to unspecified choices.

• Clarifying vague visual requirements before implementation
• Defining colors, fonts, and spacing for a landing page
• Reviewing design choices through an HTML preview

Audit CSS class names for inconsistency and ambiguity, then organize them using BEM, a convention that names components, their elements, and variations. Check the rendered interface across screen sizes and interaction states when reviewing changes.

• Auditing inconsistent CSS class names
• Renaming component classes using BEM
• Separating utility classes and JavaScript hooks

Compare how proxies and backend servers interpret HTTP requests in a CTF sandbox. Use paired requests and logs to pinpoint differences in paths, headers, or message boundaries, then document a minimal reproduction of unintended routing.

• Compare HTTP interpretation across proxies and backend servers
• Investigate path normalization and decoding differences
• Trace routing changes caused by ambiguous headers

Turn a vague idea into a written specification through focused questions. The skill scores clarity across goals, constraints, and success criteria, explores hidden assumptions, and requires explicit approval before moving from planning to execution.

• Clarifying a vague idea before implementation
• Defining goals, constraints, and acceptance criteria
• Checking requirements across multiple project components

Manage a running task list in a gbrain page using stable IDs and four priority levels. Add, complete, defer, remove, or review tasks while recording completion dates and deferral reasons, and clarifying ambiguous task references before changes.

• Add tasks and organize them by priority
• Complete a specific task by ID
• Defer work with a target date and reason