trellis-meta
Understand and customize the local Trellis architecture inside a user project. Use when modifying .trellis plus platform hooks, settings, agents, skills, commands, prompts, workflows, the channel runtime (trellis channel), bundled runtime agents under .trellis/agents/, selectable workflow templates, registry-backed spec refresh, cross-session memory (trellis mem) generated by trellis init, or AI-facing bundled skills (trellis-channel, trellis-session-insight, trellis-spec-bootstrap) and bundled-skill auto-dispatch flow.
git clone --depth 1 https://github.com/fy-agent/fyagent /tmp/trellis-meta && cp -r /tmp/trellis-meta/.cursor/skills/trellis-meta ~/.claude/skills/trellis-metaSKILL.md
# 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.
Trellis v0.6 adds three architectural surfaces on top of the pre-v0.6 workflow / persistence / platform model. First, a multi-agent collaboration runtime: `trellis channel` coordinates multiple AI worker processes through project-scoped JSONL event logs at `~/.trellis/channels/<project>/<channel>/events.jsonl`, with worker OOM guard, forum/thread channels, durable idempotency keys, and bundled `.trellis/agents/{check,implement}.md` runtime definitions. Second, cross-session memory: `trellis mem list | search | context | extract | projects` reads raw Claude Code, Codex, and Pi Agent JSONL already on disk, slices by `--phase brainstorm|implement|all`, and never uploads anything. Third, a dual-package npm release: `@mindfoldhq/trellis` (CLI) and `@mindfoldhq/trellis-core` (SDK with `/channel`, `/task`, `/mem`, `/testing` subpaths) ship in lockstep on one version. Treat these as first-class customization surfaces alongside the per-platform integration files.
The default operating scope is local files in the user project:
- `.trellis/`: workflow, config, tasks, spec, workspace, scripts, bundled runtime agents, and runtime state.
- Platform directories: `.claude/`, `.codex/`, `.cursor/`, `.opencode/`, `.kiro/`, `.gemini/`, `.qoder/`, `.codebuddy/`, `.github/`, `.factory/`, `.pi/`, `.reasonix/`, `.kilocode/`, `.agent/`, `.devin/`, `.kimi-code/`, and similar directories. Pi additionally exposes a native `trellis_subagent` tool with `single` / `parallel` / `chain` dispatch modes, throttled progress cards, and `isTrellisAgent()` validation on top of the file layout. Reasonix stores both workflow skills and subagent skills as `.reasonix/skills/<name>/SKILL.md`; subagent skills carry `runAs: subagent` frontmatter. Kimi Code keeps workflow skills in the shared `.agents/skills/` layer, delivers commands plus agent prompts as `.kimi-code/skills/<name>/SKILL.md`, and installs the same agent prompts as custom sub-agents under `.kimi-code/agents/<name>.md`.
- Shared skill layer: `.agents/skills/`.
- User-owned channel store outside the project tree: `~/.trellis/channels/<project>/<channel>/events.jsonl`.
- Raw platform conversation logs queryable via `trellis mem`: `~/.claude/projects/`, `~/.codex/sessions/`, and `~/.pi/agent/sessions/` (OpenCode adapter degraded for the v0.6 line).
Do not assume the user has the Trellis source repository. Do not default to modifying the global npm install directory or `node_modules` — both `@mindfoldhq/trellis` and `@mindfoldhq/trellis-core` ship as published packages sharing one version and one git tag per release.
## How To Use
1. Read `references/local-architecture/overview.md` first to establish the local Trellis system model.
2. If the request involves a specific AI tool, read `references/platform-files/platform-map.md` and the relevant platform file notes.
3. If the request involves multi-agent dispatch or channel workers, read `references/local-architecture/multi-agent-channel.md` and the bundled `.trellis/agents/` files.
4. If the user wants to change behavior, read `references/customize-local/overview.md`, then open the specific customization topic.
5. Before editing, read the actual files in the user project and treat local content as authoritative.
## References
### Local Architecture
- `references/local-architecture/overview.md`: The layered local Trellis architecture (workflow / persistence / platform / channel runtime) and customization principles.
- `references/local-architecture/generated-files.md`: Files generated by `trellis init` and their customization boundaries, including `.trellis/agents/`.
- `references/local-architecture/workflow.md`: Phases, routing, workflow-state blocks, and selectable workflow templates (`native`, `tdd`, `channel-driven-subagent-dispatch`, marketplace) in `.trellis/workflow.md`.
- `references/local-architecture/task-system.md`: Task directories, active task, JSONL context, parent/child task trees, and task runtime.
- `references/local-architecture/spec-system.md`: How `.trellis/spec/` is organized, injected, and refreshed from a `registry.spec` source.
- `references/local-architecture/workspace-memory.md`: `.trellis/workspace/` journals plus `trellis mem` cross-session recall and the `@mindfoldhq/trellis-core/mem` SDK.
- `references/local-architecture/context-injection.md`: Hooks, sub-agent preludes, and channel-runtime worker inbox routing.
- `references/local-architecture/multi-agent-channel.md`: `trellis channel` subcommands, project-scoped event store, forum/thread channels, worker OOM guard, durable idempotency, and bundled `.trellis/agents/` runtime agents.
- `references/local-architecture/bundled-skills.md`: Auto-dispatched bundled skills (`trellis-meta`, `trellis-spec-bootstrap`, `trellis-session-insight`) and how `getBundledSkillTemplates()` ships them to every platform skill root.
### Platform Files
- `references/platform-files/overview.md`: How shared `.trellis/` files relate to platform directories and the four platform integration modes (hook-driven, agent prelude, main-session workflow, channel runtime).
- `references/platform-files/platform-map.md`: Platform directories and paths for skills, agents, hooks, and extensions across all supported platforms including Reasonix and Pi's native `trellis_subagent` extension.
- `references/platform-files/hooks-and-settings.md`: How settings/config files, hooks, plugins, and extensions connect to Trellis; covers `channel.worker_guard.*` and `codex.dispatch_mode`.
- `references/platform-files/agents.md`: Per-platform `trellis-research` / `trellis-implement` / `trellis-check` sub-agent files plus bundled `.trellis/agents/{cDiscovers and injects project-specific coding guidelines from .trellis/spec/ before implementation begins. Reads spec indexes, pre-development checklists, and shared thinking guides for the target package. Use when starting a new coding task, before writing any code, switching to a different package, or needing to refresh project conventions and standards.
Guides collaborative requirements discovery before implementation. Creates task directory, seeds PRD, asks high-value questions one at a time, researches technical choices, and converges on MVP scope. Use when requirements are unclear, there are multiple valid approaches, or the user describes a new feature or complex task.
Deep bug analysis to break the fix-forget-repeat cycle. Analyzes root cause category, why fixes failed, prevention mechanisms, and captures knowledge into specs. Use after fixing a bug to prevent the same class of bugs.
Use Trellis channel for live multi-agent collaboration, spawned workers, cross-agent review, progress inspection, forum channels, and channel log debugging.
Comprehensive quality verification: spec compliance, lint, type-check, tests, cross-layer data flow, code reuse, and consistency checks. Use when code is written and needs quality verification, before committing changes, or to catch context drift during long sessions.
Resume work on the current task. Loads the workflow Phase Index, figures out which phase/step to pick up at, then pulls the step-level detail via get_context.py --mode phase. Use when coming back to an in-progress task and you need to know what to do next.
Wrap up the current session: verify quality gate passed, remind user to commit, archive completed tasks, and record session progress to the developer journal. Use when done coding and ready to end the session.
Reach into past AI conversation history through the `trellis mem` CLI. Use whenever the user asks 'how did we solve X last time', 'have we discussed this before', 'what was the decision on X', 'remind me what we did in this task', '上次怎么解的', '之前讨论过吗', '想起一段对话', or when starting a brainstorm that overlaps prior work, debugging a familiar bug, continuing a task across sessions, or doing a finish-work review. Returns raw past dialogue; decide for the moment whether to update spec, append to task notes, quote inline in the answer, or just internalize.