requirement-assessment_skill

This skill conducts requirement refinement and effort estimation from user input, clarifies ambiguities, and outputs a consolidated requirements list with work
  • Python

2.5k

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

3 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill requirement-assessment

  • _meta.json299 B
  • SKILL.md5.6 KB

Overview

This skill refines product requirements and produces labor estimates. It accepts textual or screenshot requirements, asks targeted clarification questions, analyzes feasibility and risks, and outputs a consolidated requirement list with a man-day estimation table. It can also create a Feishu Bitable multidimensional table and return the link for collaborative review.

How this skill works

After receiving a requirement (text or image), the skill immediately asks a preset set of clarifying questions covering customer background, system status, scope, technical constraints, timeline, and budget. It evaluates technical and business feasibility, breaks the requirement into modules and feature-level items, and produces per-role man-day estimates (product, UI, frontend, backend, test, ops, mobile platforms). When requested, it programmatically creates a Feishu Bitable with predefined fields and populates records, then returns the table link.

When to use it

  • Project kickoff to turn high-level requests into actionable tasks and estimates
  • Requirement review meetings to validate feasibility and identify gaps
  • Preparing resource and timeline estimates for planning or budgeting
  • Assessing scope changes or integrations with third-party systems
  • Creating a traceable, shareable Bitable for stakeholder collaboration

Best practices

  • Always answer the skill's mandatory clarification questions before asking for an estimate
  • Provide screenshots, existing docs, or code references when available to reduce uncertainty
  • Specify target platforms (Web/iOS/Android/etc.) to get per-platform estimates
  • Treat AI-assisted estimates as indicative; include a review buffer for QA and rework
  • Explicitly confirm Feishu space/folder and table name before Bitable creation

Example use cases

  • A product manager submits a feature description and receives a decomposed task list with man-day estimates and a Feishu Bitable link
  • A technical lead evaluates a proposed integration and gets a risk summary plus effort estimates for backend and ops
  • A PMO compares standard vs AI-assisted estimates across multiple features for capacity planning
  • A stakeholder requests a quick feasibility check and a prioritized feature breakdown for an upcoming sprint

FAQ

Provide the requirement text or screenshots, target platforms, existing system details, expected deadline, and any budget or compliance constraints.

How does the skill handle uncertainty in answers?

It flags uncertain items as risks, lists assumptions, and provides conservative estimates with recommended buffers.

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