trellis-meta
Trellis Meta
This skill is for local Trellis users who have already run trellis init in a project. After reading it, an AI should understand the Trellis architecture, operating model, and customization entry points inside that user project, then modify the generated .trellis/ and platform directory files according to the user's request.
The default operating scope is local files in the user project:
.trellis/: workflow, config, tasks, spec, workspace, scripts, and runtime state.- Platform directories:
.claude/,.codex/,.cursor/,.opencode/,.kiro/,.gemini/,.qoder/,.codebuddy/,.github/,.factory/,.pi/,.kilocode/,.agent/,.windsurf/, and similar directories. - Shared skill layer:
.agents/skills/.
Do not assume the user has the Trellis source repository. Do not default to modifying the global npm install directory or node_modules.
How To Use
- Read
references/local-architecture/overview.mdfirst to establish the local Trellis system model. - If the request involves a specific AI tool, read
references/platform-files/platform-map.mdand the relevant platform file notes. - If the user wants to change behavior, read
references/customize-local/overview.md, then open the specific customization topic. - Before editing, read the actual files in the user project and treat local content as authoritative.
More from mindfold-ai/trellis
cc-codex-spec-bootstrap
Claude Code + Codex parallel pipeline for bootstrapping Trellis coding specs. CC analyzes the repo with GitNexus (knowledge graph) + ABCoder (AST), creates Trellis task PRDs with full architectural context and MCP tool instructions, then Codex agents run those tasks in parallel to fill spec files. Use when: bootstrapping coding guidelines, setting up Trellis specs, 'bootstrap specs for codex', 'create spec tasks', 'CC + Codex spec pipeline', 'initialize coding guidelines with code intelligence'. Also triggers when user wants to set up GitNexus or ABCoder MCP for multi-agent spec generation.
89brainstorm
Collaborative requirements discovery session optimized for AI coding workflows. Creates task directories, seeds PRDs, runs codebase research, proposes concrete implementation approaches with trade-offs, and converges on MVP scope through structured Q&A. Use when requirements are unclear, multiple implementation paths exist, trade-offs need evaluation, or a complex feature needs scoping before development.
38break-loop
Deep post-fix bug analysis across five dimensions: root cause categorization, fix failure analysis, prevention mechanisms, systematic expansion, and knowledge capture. Updates .trellis/spec/ guides with lessons learned to prevent recurring bugs. Use when a debugging session completes, after fixing a tricky bug, when the same class of bug keeps recurring, or when you want to capture debugging insights into project documentation.
34record-session
Records completed work progress to .trellis/workspace/ journal files after human testing and commit. Captures session summaries, commit hashes, and updates developer index files for future session context. Use when a coding session is complete, after the human has committed code, or to persist session knowledge for future AI sessions.
32start
Initializes an AI development session by reading workflow guides, developer identity, git status, active tasks, and project guidelines from .trellis/. Classifies incoming tasks and routes to brainstorm, direct edit, or task workflow. Use when beginning a new coding session, resuming work, starting a new task, or re-establishing project context.
30finish-work
Pre-commit quality checklist covering lint, typecheck, tests, code-spec sync, API changes, database migrations, cross-layer verification, and manual testing. Blocks commit if infra or cross-layer specs lack executable depth. Use when code is written and tested but not yet committed, before submitting changes, or as a final review before git commit.
29