Skill: hongyi3/asking-until-100 (version 0.1.0)

--- name: asking-until-100 description: Repo-aware questioning protocol for OpenClaw that increases clarification before acting on coding, project-build, architecture, debugging, and implementation tasks. Use when requirements, repo context, constraints, interfaces, success criteria, or execution rigor are ambiguous and the agent should ask higher-signal questions or generate a structured question report before implementation. --- # Asking Until 100 ## Overview Use this skill to slow down execution when the task is underspecified, risky, or expensive to get wrong. Treat "100" as target readiness to proceed, not literal certainty. ## Workflow 1. Load explicit instructions and repo-local config such as `.asking-until-100.yaml`. 2. Classify the task as `coding`, `build`, `architecture`, `debugging`, `discovery`, or `general`. 3. Inspect the repo when it looks relevant so repo-discoverable facts do not turn into avoidable questions. 4. Estimate readiness from the configured dimensions in `references/protocol.md`. 5. Choose a questioning mode: - `fast` for low ambiguity - `guided` for moderate ambiguity - `deep` for higher ambiguity or requested rigor - `report` for highest-rigor coding and build tasks with decision-critical gaps 6. Ask the highest-value questions before taking action. 7. Respect the execution gate: - highest-rigor `coding` and `build` tasks default to blocking clarification - other tasks default to explicit assumptions when gaps remain ## Questioning Style - Prefer structural, directional, and decision-shaping questions over generic filler. - Use a working hypothesis when it helps the user react to a proposed path. - Offer suggested answers when useful, but always leave a free-form path. - Do not ask for facts that can be inspected directly from the repo. ## High-Rigor Report For highest-rigor coding or build tasks, begin with `Provisional Project Structure`, then emit: `Working Hypothesis`, `Architecture Questions`, `Product Questions`, `Constraint Questions`, and `Decision-Critical Unknowns`. The working-hypothesis section must also summarize the execution gate and blocking dimensions. See `references/coding-report-format.md` for the required output order and `scripts/render_project_structure.py` for deterministic structure rendering. ## References - `references/protocol.md` for readiness, repo-aware escalation, and stop conditions - `references/config.md` for config fields, precedence, and asking-intensity behavior - `references/question-patterns.md` for question quality rules and option patterns - `references/coding-report-format.md` for the high-rigor report contract - `references/build-playbook.md` for build-specific gaps to check before acting ## Scripts And Assets - `scripts/validate_config.py` validates profile files - `scripts/preview_question_report.py` previews questioning output for a prompt - `scripts/render_project_structure.py` renders prompt-only or repo-aware provisional structures - `scripts/explain_profile_merge.py` shows the effective merged profile - `assets/` contains bundled profiles tuned for `gpt-5.4` with `xhigh` reasoning assumptions Keep this file concise. Use the references for detailed policy, config, and output examples.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)