Use when a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions.
npx skills add https://github.com/agentlas-ai/Agentlas-OS --skill clarify-question-loop
Ask only questions that change the generated package, runtime adapter, safety
boundary, or public/private release decision.
For /hep-build creation or behavior-changing packaging, this is not a
substitute for the Builder Interview and Research Gate in
docs/builder-interview-research-gate.md. Run that gate first: ask an 8-12
question first batch, research similar agent repositories or comparables and
academic/professional theory, then use this clarify loop only for the remaining
narrow ambiguities.
independent ownership boundaries are unclear, ask before generation. The
first batch must include this plain-language question: "이 일을 한 명의
전문가가 처음부터 끝까지 맡으면 되나요, 아니면 조사/분석/검토처럼 여러
전문가가 나눠 맡고 마지막에 합쳐야 하나요?"
must be synthesized, and whether artifacts are sequential dependencies or
independent parallel packets.
functional-quality questions remain, return to the Builder Interview and
Research Gate instead of pretending the package is ready.
This loop shares the briefing interview engine's contract
(agentlas_cloud/interview/): a question is only worth asking if the answer
would change execution, not just its phrasing. Respect the surface budget
(chat 3-5 in one batch, stormbreaker <= 8 across two batches, build 8-12 plus
follow-ups). 'decide later' is always a valid answer — record it as deferred,
never re-ask. When answers you auto-confirmed from code/memory reach three in a
row, the next question must go to the human.
조사/분석/검토처럼 여러 전문가가 나눠 맡고 마지막에 합쳐야 하나요?
Never ask non-technical users to choose internal labels such as
single-agent, team-builder, ownership boundary, memory/context, synthesis,
or produces/consumes. Translate them before asking:
If a question still sounds technical, split it into two shorter everyday
questions and give examples such as 조사, 분석, 검토, 승인.
See docs/clarify-question-loop.md.
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take agentlas-ai/clarify-question-loop from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.