mindfold-ai
Official@mindfold-ai
Offers specialized architectural documentation, frontend performance diagnostics, and cross-platform conversation retrieval for enterprise development environments.
Agent Skills by mindfold-ai
Showing 27 vetted skills indexed across 3 GitHub repositories.
mindfold-aitrellis-spec-bootstrap
Generates project-specific Trellis coding specs from real codebase analysis.
mindfold-aitrellis-before-dev
Loads project coding guidelines from .trellis/spec before writing code.
mindfold-aitrellis-brainstorm
Guides requirements discovery and produces PRD, design, and implementation planning artifacts.
mindfold-aitrellis-break-loop
Analyzes fixed bugs across five dimensions and captures prevention knowledge into spec files.
mindfold-aitrellis-session-insight
Searches and extracts past AI coding session dialogue via the trellis mem CLI.
mindfold-aitrellis-check
Verifies code changes against specs, lint, type checks, tests, and cross-layer consistency.
mindfold-aitrellis-meta
Guides AI agents in understanding and customizing local Trellis project architecture.
mindfold-aitrellis-channel
Coordinates multi-agent collaboration through Trellis channel event logs, workers, and forum threads.
mindfold-aitrellis-update-spec
Captures executable contracts and coding conventions into .trellis/spec/ markdown documents.
mindfold-aitrellis-start
Initializes AI coding sessions by loading project context, tasks, and guidelines from .trellis.
mindfold-aitrellis-finish-work
Archives completed tasks and records session journals to close out coding sessions.
mindfold-aifirst-principles-thinking
Decompose problems into axioms and derive solutions through structured first-principles reasoning.
mindfold-aitrellis-continue
Resumes in-progress Trellis tasks by loading workflow phase context and routing to the correct step.
mindfold-aits-sdk-author
Design, build, verify, and publish TypeScript SDKs as npm packages from pnpm monorepos.
mindfold-aicontribute
Guides contributions of spec templates, marketplace skills, and bilingual documentation to Trellis repositories.
mindfold-aipython-design
Applies software design principles to Python CLI scripts and utilities.
mindfold-aigitnexus-refactoring
Automates safe code renaming, extraction, and restructuring using GitNexus dependency graph analysis.
mindfold-aigitnexus-debugging
Traces bugs and errors through code execution flows using GitNexus graph queries.
mindfold-aigitnexus-cli
Runs GitNexus CLI commands to index repositories, check status, and generate wikis.
mindfold-aigitnexus-impact-analysis
Analyzes code change blast radius using GitNexus dependency graphs and execution flows.
mindfold-aicreate-manifest
Create Trellis release migration manifests and bilingual docs-site changelogs from git commits.
mindfold-aitrellis-spec-bootstarp
Generate project-specific Trellis coding specifications from repository architecture and source code patterns.
mindfold-aifrontend-fullchain-optimization
Diagnose and optimize frontend performance using core Web Vitals metrics.
mindfold-aitrellis-meta
Document Trellis workflow architecture, hooks, pipelines, and spec injection.
Frequently Asked Questions About mindfold-ai
FAQPage SchemaWhat specific tasks can be performed using mindfold-ai?▼
Users can generate project-specific coding specifications, optimize frontend performance via Web Vitals analysis, create social media content drafts for XiaoHongShu, and index historical conversation logs from multiple external platforms for centralized retrieval.
Which technical personas benefit from these capabilities?▼
Frontend engineers focused on performance optimization, technical architects managing project-wide coding standards, and content creators targeting the XiaoHongShu platform will find these capabilities most relevant to their daily technical requirements.
What are the prerequisites for implementing these capabilities?▼
Implementation requires an existing repository architecture for specification generation, access to frontend source code for performance diagnostics, and active historical logs from supported platforms like Claude Code, Codex, or Pi for the retrieval functionality.