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spec-kitty-clarify规格小猫澄清

Agent Skill

spec-kitty-clarify 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

470

周安装

20

GitHub Stars

2

下载量

165
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:spec-kitty-clarify(规格小猫澄清)
来源仓库:https://github.com/richfrem/agent-plugins-skills
仓库路径:skills/spec-kitty-clarify
安装命令:
npx skills add https://github.com/richfrem/agent-plugins-skills --skill spec-kitty-clarify
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/richfrem/agent-plugins-skills --skill spec-kitty-clarify

简介

spec-kitty-clarify 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中进行协作事项整理。

  • 适用于围绕仓库状态、代码变更或协作事项进行信息组织和梳理的场景。
  • 支持对 GitHub 相关协作内容进行分类、标记和状态跟踪。
  • 安装命令:npx skills add https://github.com/richfrem/agent-plugins-skills --skill spec-kitty-clarify
  • 建议确认权限范围和维护状态,注意可能触发联网或文件读写操作

SKILL.md

Dependencies

This skill requires Python 3.8+ and standard library only. No external packages needed.

To install this skill's dependencies:

pip-compile ./requirements.in
pip install -r ./requirements.txt

See ./requirements.txt for the dependency lockfile (currently empty — standard library only).


User Input

$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Outline

Goal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.

Note: This clarification workflow is expected to run (and be completed) BEFORE invoking /spec-kitty.plan. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases.

Execution steps:

  1. Run spec-kitty agent feature check-prerequisites --json --paths-only from the repository root and parse JSON for:

- feature_dir - Absolute path to feature directory (e.g., /path/to/kitty-specs/017-my-feature/) - FEATURE_SPEC - Absolute path to spec.md file - target_branch / base_branch (deterministic branch contract) - If command fails or JSON parsing fails, abort and instruct user to run /spec-kitty.specify first or verify they are in a spec-kitty-initialized repository.

  1. Load the current spec file. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing. Produce an internal coverage map used for prioritization (do not output raw map unless no questions will be asked). Functional Scope & Behavior: Domain & Data Model: Interaction & UX Flow: Non-Functional Quality Attributes: Integration & External Dependencies: Edge Cases & Failure Handling: Constraints & Tradeoffs: Terminology & Consistency: Completion Signals: Misc / Placeholders: For each category with Partial or Missing status, add a candidate question opportunity unless:

- Core user goals & success criteria - Explicit out-of-scope declarations - User roles / personas differentiation - Entities, attributes, relationships - Identity & uniqueness rules - Lifecycle/state transitions - Data volume / scale assumptions - Critical user journeys / sequences - Error/empty/loading states - Accessibility or localization notes - Performance (latency, throughput targets) - Scalability (horizontal/vertical, limits) - Reliability & availability (uptime, recovery expectations) - Observability (logging, metrics, tracing signals) - Security & privacy (authN/Z, data protection, threat assumptions) - Compliance / regulatory constraints (if any) - External services/APIs and failure modes - Data import/export formats - Protocol/versioning assumptions - Negative scenarios - Rate limiting / throttling - Conflict resolution (e.g., concurrent edits) - Technical constraints (language, storage, hosting) - Explicit tradeoffs or rejected alternatives - Canonical glossary terms - Avoided synonyms / deprecated terms - Acceptance criteria testability - Measurable Definition of Done style indicators - TODO markers / unresolved decisions - Ambiguous adjectives ("robust", "intuitive") lacking quantification - Clarification would not materially change implementation or validation strategy - Information is better deferred to planning phase (note internally)

  1. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:

- Maximum of 10 total questions across the whole session. - Each question must be answerable with EITHER: - A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR - A one-word / short‑phrase answer (explicitly constrain: "Answer in <=5 words"). - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation. - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved. - Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness). - Favor clarifications that reduce downstream rework risk or prevent misaligned acceptance tests. - Scale thoroughness to the feature’s complexity: a lightweight enhancement may only need one or two confirmations, while multi-system efforts warrant the full question budget if gaps remain critical. - If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic.

  1. Sequential questioning loop (interactive):

- Present EXACTLY ONE question at a time. - For multiple-choice questions, list options inline using letter prefixes rather than tables, e.g. Options: (A) describe option A · (B) describe option B · (C) describe option C · (D) short custom answer (<=5 words) Ask the user to reply with the letter (or short custom text when offered). - For short-answer style (no meaningful discrete options), output a single line after the question: Format: Short answer (<=5 words). - After the user answers: - Validate the answer maps to one option or fits the <=5 word constraint. - If ambiguous, ask for a quick disambiguation (count still belongs to same question; do not advance). - Once satisfactory, record it in working memory (do not yet write to disk) and move to the next queued question. - Stop asking further questions when: - All critical ambiguities resolved early (remaining queued items become unnecessary), OR - User signals completion ("done", "good", "no more"), OR - You reach 5 asked questions. - Never reveal future queued questions in advance. - If no valid questions exist at start, immediately report no critical ambiguities.

  1. Integration after EACH accepted answer (incremental update approach):

- Maintain in-memory representation of the spec (loaded once at start) plus the raw file contents. - For the first integrated answer in this session: - Ensure a ## Clarifications section exists (create it just after the highest-level contextual/overview section per the spec template if missing). - Under it, create (if not present) a ### Session YYYY-MM-DD subheading for today. - Append a bullet line immediately after acceptance: - Q: <question> → A: <final answer>. - Then immediately apply the clarification to the most appropriate section(s): - Functional ambiguity → Update or add a bullet in Functional Requirements. - User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario. - Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly. - Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target). - Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it). - Terminology conflict → Normalize term across spec; retain original only if necessary by adding (formerly referred to as "X") once. - If the clarification invalidates an earlier ambiguous statement, replace that statement instead of duplicating; leave no obsolete contradictory text. - Save the spec file AFTER each integration to minimize risk of context loss (atomic overwrite). - Preserve formatting: do not reorder unrelated sections; keep heading hierarchy intact. - Keep each inserted clarification minimal and testable (avoid narrative drift).

  1. Validation (performed after EACH write plus final pass):

- Clarifications session contains exactly one bullet per accepted answer (no duplicates). - Total asked (accepted) questions ≤ 5. - Updated sections contain no lingering vague placeholders the new answer was meant to resolve. - No contradictory earlier statement remains (scan for now-invalid alternative choices removed). - Markdown structure valid; only allowed new headings: ## Clarifications, ### Session YYYY-MM-DD. - Terminology consistency: same canonical term used across all updated sections.

  1. Write the updated spec back to FEATURE_SPEC.
  2. Report completion (after questioning loop ends or early termination):

- Number of questions asked & answered. - Path to updated spec. - Sections touched (list names). - Coverage summary listing each taxonomy category with a status label (Resolved / Deferred / Clear / Outstanding). Present as plain text or bullet list, not a table. - If any Outstanding or Deferred remain, recommend whether to proceed to /spec-kitty.plan or run /spec-kitty.clarify again later post-plan. - Suggested next command.

Behavior rules:

  • If no meaningful ambiguities found (or all potential questions would be low-impact), respond: "No critical ambiguities detected worth formal clarification." and suggest proceeding.
  • If spec file missing, instruct user to run /spec-kitty.specify first (do not create a new spec here).
  • Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).
  • Avoid speculative tech stack questions unless the absence blocks functional clarity.
  • Respect user early termination signals ("stop", "done", "proceed").
  • If no questions asked due to full coverage, output a compact coverage summary (all categories Clear) then suggest advancing.
  • If quota reached with unresolved high-impact categories remaining, explicitly flag them under Deferred with rationale.

Context for prioritization: User arguments from $ARGUMENTS section above (if provided). Use these to focus clarification on specific areas of concern mentioned by the user.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.78%
按下载量换算57

Claude

30.08%
按下载量换算50

Cursor

19.67%
按下载量换算32

Gemini CLI

9.23%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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