Manifest AI
Official@mnfst-ai · United States of America
Offers rigorous specification analysis and assumption validation to eliminate unintended feature invention during product development cycles.
Agent Skills by Manifest AI
Showing 16 vetted skills indexed across 1 GitHub repositories.
mnfst-aism:shape:find-holes
Analyze specification documents to identify underspecified areas where coding tools will invent behavior.
mnfst-aism:stage:decision-capture
Generate a prioritized decisions-made manifest classifying choices as Specified, Invented, or Adapted.
mnfst-aism:invalidate-score
Score interview assumptions as validated, invalidated, or open with pull signals.
mnfst-aism:invalidate-interview
Generate read-aloud interview scripts with negative-framed questions and pause cues.
mnfst-aism:coherence-check
Detect intent drift in work-in-progress artifacts and recommend Continue or Realign.
mnfst-aism:stage:chunking
Chunk stories, features, or design docs into flow-cycle-sized increments sequenced by cost of delay.
mnfst-aism:stage:live-mirror
Compare specifications against produced code to identify unintended inventions.
mnfst-aism:shape:collapsed-options
Identify collapsed options in product specs and prototypes.
mnfst-aism:shape:brief
Rank top problems from shape skill outputs into a staged brief with inline proposals.
mnfst-aism:invalidate-prep
Surface and rank assumptions, personas, and problem statements for invalidation interviews.
mnfst-aism:shape:soul-check
Analyze design artifacts to detect drift from the original animating idea.
mnfst-aism:stage-manage
Reads repository context and monitors builder activity to suggest workflow skills.
mnfst-aism:shape:gate
Runs a five-question readiness check for shaped work before staging.
mnfst-aism:stage:prompt-craft
Convert vague stories or failed prompts into six-part structured prompts for coding tools.
mnfst-aism:yagni
Require documented user evidence before staging implementation.
mnfst-aism:shape:risk-sequence
Identify load-bearing assumptions in product and design documents and rank them by cost-if-wrong.
Frequently Asked Questions About Manifest AI
FAQPage SchemaWhat specific tasks does Manifest AI enable for product teams?▼
Manifest AI enables the identification of underspecified areas in documentation, the ranking of assumptions by risk, and the detection of intent drift between design artifacts and production code. It provides structured readiness checks and decision-capture manifests to ensure development remains aligned with original product intent.
Which personas benefit most from these technical capabilities?▼
Product managers, technical leads, and systems architects benefit most from these capabilities. These personas use the platform to validate product requirements, sequence development tasks based on cost-of-delay metrics, and ensure that engineering output strictly adheres to the core animating ideas of the product brief.
What are the prerequisites for integrating these capabilities into a development environment?▼
Integration requires access to existing product specifications, design artifacts, and repository context. Users must provide clear problem statements and feature documentation to enable the system to perform gap analysis, assumption ranking, and readiness gating before implementation begins.