81 skills · sorted by relevance
Turn unresolved requirements into a compact clarification form. Use choices and input fields to collect answers that affect design direction, content structure, or delivery format, so the workflow can proceed.
Clarify unresolved product decisions before implementation, such as scope, supported platforms, or API agreements. Use a small set of focused questions to establish the intended outcome, while leaving routine technical choices to the agent.
Take an idea or issue through requirements clarification, reviewed planning, implementation, and verification. Supervise all stages in one recoverable OMX workflow, preserving artifacts and progress so work can resume or return to planning when findings require changes.
Lets an agent ask for clarification, preferences, or approval through an on-screen dialog. Questions can offer single or multiple choices and custom text input, giving the agent a user response to guide its next step.
Keep implementation work moving by turning routine uncertainty into explicit assumptions. The agent follows existing project patterns, makes small reversible changes, validates results, and reports its decisions, completed changes, checks, and remaining risks.
Structured clarification workflow for underspecified requirements. Use before planning to resolve ambiguities through coverage-based questioning. Records answers in spec clarifications section.
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Compress planner candidate questions into a compact frontdesk-facing clarification batch and normalize answers back to planner.
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The research-domain clarification skill — find underspecified items in a thesis spec.md (prose wrong_if, universe rows without rationale, missing budget/expiry/pins, ambiguous pillars), ask the human structured questions WITH options (max 5 per round), encode the answers back into spec.md's Clarifications section, and re-evaluate checklists/thesis-quality.md after every write (Q32 bidirectional maintenance).
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Generate targeted clarifying questions (2-3 max) that challenge vague requirements and extract missing context. Use after request-analyzer identifies clarification needs, before routing to specialist agents. Helps cto-orchestrator avoid delegating unclear requirements.
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Structured clarification workflow for underspecified requirements. Use before planning to resolve ambiguities through coverage-based questioning. Records answers in spec clarifications section.
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Reads two or more supplier, bid or tender responses against a requirements list and returns a DRAFT comparison pack: a requirement-by-supplier matrix, gaps per supplier, clarification questions and neutral observations, for an evaluation panel. Never scores, weights, ranks or recommends a winner; the award stays with the panel. Use when the user asks to "compare these bids", "matrix the tender responses against our requirements", "collate the RFQ replies", "show me where each supplier falls short" or "draft clarification questions for the bidders". Do not use for scoring or weighting against agreed criteria, use supplier-evaluation-matrix instead, or for writing the requirements, use rfp-requirements-pack instead. Drafts for human review; never approves, authorises or signs off.
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Implement pending tasks from an OpenSpec change using its specifications and design documents. The skill updates task checkboxes, confirms progress, supports resuming unfinished work, and pauses when requirements or implementation issues need clarification.
Shape agent collaboration around focused clarification, practical options, and small steps that can be checked. This profile emphasizes early drafts, preserving the user's intent, and turning agreed direction into concrete outputs through iteration.
Compare a job posting with your experience to score how well the requirements match. Identify strengths, missing qualifications, and potential concerns, then plan application priorities, resume adjustments, and points to address in a cover letter.
Looks up a person's identity, role, and relationship before asking the user for clarification. Searches stored knowledge and connected records, then the web, updates incomplete pages, and asks a specific question with a hypothesis when evidence remains inconclusive.
Review captured agent sessions related to a GitButler branch, pull request, or change. Start with a chronological overview of previous discussions and actions, then inspect specific turns when the rationale or supporting evidence needs clarification.
Supports analysis of tables already in the workspace through data inspection, Python calculations, and charts chosen for the question. It also explains how to ask users for clarification and label results with meaningful units and names.
Read existing code to reconstruct system specifications, including architecture, data flows, APIs, and business rules. Produces documentation tied to code locations, separating observed behavior from inferences and questions that need clarification.
Prepare a structured JSON handoff for feature work, bringing together requirements, design decisions, key user journeys, tests, demo recordings, and pull request status. Clearly distinguish completed work from missing validation, publication steps, and other remaining tasks.
Define a consistent process for unattended AI research using public sources. Limit clarification questions, show a search plan, distinguish facts from interpretations and assumptions, and keep reports structured so repeated investigations can be compared.
Clean up DeepChat's specification-driven development documents after implementation and validation. Review feature, issue, and architecture files, remove completed or obsolete material when evidence supports deletion, and preserve maintained specifications while checking references.
Review a pull request for code quality, correctness, security, tests, and documentation. Receive structured findings ranked by severity, with file references, approval status, positive highlights, and questions that need clarification.
Collect product feedback, usage data, runtime errors, and issue reports from configured sources. Classify and group findings with supporting evidence, then apply separate project rules for implementing fixes, requesting clarification, or closing reports.
Build an initial account profile from public social media content and guided questions. Organize positioning, style, audience, platforms, preferences, and initial insights into six files, marking where information came from and what still needs clarification.