How do I install the Clarify skill?

Run npx skillmds@latest add agentii-ai/clarify in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Clarify safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Clarify?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Clarify free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Clarify?

agentii-ai (@agentii-ai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Clarify by agentii-ai — Agent Skill

Name: Clarify
Author: agentii-ai

Clarify

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).

agentii-ai 2a84ed5 2 files · 2.8 KB Updated Sep 17, 2026

File contents

agentii.clarify

The 8th kit command — closes the gap Q32 itself assumed: upstream speckit-clarify re-evaluates the quality checklist after every spec write, but spec 046 never named the command that performs the clarification.

Two phases

1. Analyze (deterministic)

python3 kit-scripts/agentii_cmd.py clarify --thesis theses/001-… --questions

Emits a JSON list of candidate questions, each with:

id (stable — content-derived from the target)
target (pillar id / field / universe row)
question (exact, in the spec's working language)
options (derived where possible; null = free-form)

Candidates come from a deterministic scanner (never vibes): prose wrong_if (missing metric=/threshold=/source=), universe rows without rationale, missing budget, missing expiry_triggers, subscription tokens not in TICKER × skill form, missing as_of/pins, pillars without priority.

2. Encode (after the human answers)

python3 kit-scripts/agentii_cmd.py clarify --thesis theses/001-… --answers '<json>'

Appends each answer to spec.md's ## Clarifications section (- [YYYY-MM-DD] Q: … → A: …).
Re-evaluates checklists/thesis-quality.md after the write (Q32: the machine-maintained checklist is bidirectional — report "N/M items passing" plus regressions).

Rules

Max 5 questions per round (upstream v1.0.4's tightened limit; spec recorded it as the convention to follow).
Only ask what blocks the next step — every question must name the field it unblocks.
Answers are encoded verbatim (professional English); a Chinese question is translated to English before encoding (the project's first language is English).
Never ask about facts obtainable from the platform (that's the skills' job); clarify intent only.

---name: clarifydescription: 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).---# agentii.clarifyThe 8th kit command — closes the gap Q32 itself assumed: upstream`speckit-clarify` re-evaluates the quality checklist after every spec write, butspec 046 never named the command that performs the clarification.## Two phases### 1. Analyze (deterministic)```bashpython3 kit-scripts/agentii_cmd.py clarify --thesis theses/001-… --questions```Emits a JSON list of candidate questions, each with:- `id` (stable — content-derived from the target)- `target` (pillar id / field / universe row)- `question` (exact, in the spec's working language)- `options` (derived where possible; null = free-form)Candidates come from a **deterministic scanner** (never vibes): prose `wrong_if`(missing `metric=`/`threshold=`/`source=`), universe rows without rationale,missing `budget`, missing `expiry_triggers`, subscription tokens not in`TICKER × skill` form, missing `as_of`/pins, pillars without priority.### 2. Encode (after the human answers)```bashpython3 kit-scripts/agentii_cmd.py clarify --thesis theses/001-… --answers '<json>'```- Appends each answer to `spec.md`'s `## Clarifications` section (`- [YYYY-MM-DD] Q: … → A: …`).- **Re-evaluates `checklists/thesis-quality.md` after the write** (Q32: the machine-maintained checklist is bidirectional — report "N/M items passing" plus regressions).## Rules- **Max 5 questions per round** (upstream v1.0.4's tightened limit; spec recorded it as the convention to follow).- Only ask what blocks the next step — every question must name the field it unblocks.- Answers are encoded verbatim (professional English); a Chinese question is translated to English before encoding (the project's first language is English).- Never ask about facts obtainable from the platform (that's the skills' job); clarify **intent** only.

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