Install
openclaw skills install asking-until-100Repo-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.
openclaw skills install asking-until-100Use 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.
.asking-until-100.yaml.coding, build, architecture, debugging, discovery, or general.references/protocol.md.fast for low ambiguityguided for moderate ambiguitydeep for higher ambiguity or requested rigorreport for highest-rigor coding and build tasks with decision-critical gapscoding and build tasks default to blocking clarificationFor 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/protocol.md for readiness, repo-aware escalation, and stop conditionsreferences/config.md for config fields, precedence, and asking-intensity behaviorreferences/question-patterns.md for question quality rules and option patternsreferences/coding-report-format.md for the high-rigor report contractreferences/build-playbook.md for build-specific gaps to check before actingscripts/validate_config.py validates profile filesscripts/preview_question_report.py previews questioning output for a promptscripts/render_project_structure.py renders prompt-only or repo-aware provisional structuresscripts/explain_profile_merge.py shows the effective merged profileassets/ contains bundled profiles tuned for gpt-5.4 with xhigh reasoning assumptionsKeep this file concise. Use the references for detailed policy, config, and output examples.