Initialize constraint manifold scaffolds with JSON and Markdown artifacts.
npx skills add https://github.com/dhanesh/manifold --skill manifold-m0-init
Please help me install this Agent Skill. Skill: manifold-m0-init Source: https://github.com/dhanesh/manifold/tree/main/install/agents/codex/skills/manifold-m0-init Command: npx skills add https://github.com/dhanesh/manifold --skill manifold-m0-init
Managing feature initialization and constraint manifold setup is error-prone and repetitive. This skill automates the creation of a dedicated constraint manifold scaffold, ensuring consistent structure and metadata from the outset.
Run /m0-init <feature-name> to create the manifold scaffold and emit the two artifact files.
High-intent search queries and answers about installing and using this skill.
A constraint manifold is a structured scaffold for constraint-driven feature planning. You need one when onboarding a new feature or experiment to systematically track business, technical, UX, security, and operational constraints from the start.
To initialize a constraint manifold, run the /m0-init command with your feature name. This automatically generates a .manifold directory containing a machine-readable JSON structure and a human-readable Markdown rationale file.
Yes, the manifold initialization writes both a .manifold/<feature>.json file for machine-readable schema processing and a .manifold/<feature>.md file for human-readable rationale, ensuring compatibility across automated and manual workflows.
Yes, you can use this scaffold for feature onboarding and experimentation without manual setup. It automatically establishes the initial schema version, phase, timestamps, and empty constraint categories to eliminate repetitive configuration.
The scaffold initializes empty constraint categories and basic metadata, but it does not populate the actual constraints themselves. You must manually define the specific business, technical, and operational limits within the generated structure.
Manifold initialization enforces schema version compatibility to ensure the generated JSON structure remains consistent and valid across different processing stages. This prevents structural errors during downstream feature planning and constraint tracking.
yu13130122297Identify uncertainties in research framing, datasets, metrics, and baselines.
dhaneshConduct structured interviews to document constraints across five domains.
Devvraj10Automate email drafting, calendar scheduling, and task tracking.
dhaneshRead the .manifold directory and summarize manifold constraint state.
