> Go 后端(`oci-portal/`)编码规范入口。规范提炼自 Google / Uber Go Style Guide、Effective Go 与 Go 官方模块布局指南,按本项目裁剪;条目与项目约定冲突时,以本 spec 为准。迁自 docs/开发指南.md 与根目录 AGENTS.md(2026-07)。
This is duplicated contract logic even when the code is only two lines. Each
consumer now has its own definition of what a valid payload means.
**Good**: Put the decoder, type guard, or projection next to the data owner:
```typescript
if(isThreadEvent(ev)){
renderThreadEvent(ev);
}
```
**Rule**: If the same untyped payload field is read in 2+ places, create a
shared type guard / normalizer / projection before adding a third reader.
---
## When to Abstract
**Abstract when**:
- Same code appears 3+ times
- Logic is complex enough to have bugs
- Multiple people might need this
**Don't abstract when**:
- Only used once
- Trivial one-liner
- Abstraction would be more complex than duplication
---
## After Batch Modifications
When you've made similar changes to multiple files:
1.**Review**: Did you catch all instances?
2.**Search**: Run grep to find any missed
3.**Consider**: Should this be abstracted?
### Reducers Should Use Exhaustive Structure
When state is derived from action-like values (`action`, `kind`, `status`,
`phase`), prefer a reducer with one `switch` over scattered `if/else` updates.
```typescript
// BAD - action-specific state transitions are hard to audit
if(action==="opened"){...}
elseif(action==="comment"){...}
elseif(action==="status"){...}
// GOOD - one reducer owns the transition table
switch(event.action){
case"opened":
...
return;
case"comment":
...
return;
}
```
This matters when the event log is the source of truth. A reducer is the
documented replay model; display code and commands should not duplicate pieces
of that replay model.
---
## Checklist Before Commit
- [ ] Searched for existing similar code
- [ ] No copy-pasted logic that should be shared
- [ ] No repeated untyped payload field extraction outside a shared decoder
- [ ] Constants defined in one place
- [ ] Similar patterns follow same structure
- [ ] Reducer/action transitions live in one reducer or command dispatcher
---
## Gotcha: Python if/elif/else Exhaustive Check
**Problem**: Python's if/elif/else chains have no compile-time exhaustive check. When you add a new value to a `Literal` type (e.g., `Platform`), existing if/elif/else chains silently fall through to `else` with wrong defaults.
**Symptom**: New platform works partially — some methods return Claude defaults instead of platform-specific values. No error is raised.
**Example** (`cli_adapter.py`):
```python
# BAD: "gemini" falls through to else, returns "claude"
@property
defcli_name(self)->str:
ifself.platform=="opencode":
return"opencode"
else:
return"claude"# gemini silently gets "claude"!
# GOOD: explicit branch for every platform
@property
defcli_name(self)->str:
ifself.platform=="opencode":
return"opencode"
elifself.platform=="gemini":
return"gemini"
else:
return"claude"
```
**Prevention**: When adding a new value to a Python `Literal` type, search for ALL if/elif/else chains that switch on that type and add explicit branches. Don't rely on `else` being correct for new values.
---
## Gotcha: Asymmetric Mechanisms Producing Same Output
**Problem**: When two different mechanisms must produce the same file set (e.g., recursive directory copy for init vs. manual `files.set()` for update), structural changes (renaming, moving, adding subdirectories) only propagate through the automatic mechanism. The manual one silently drifts.
**Symptom**: Init works perfectly, but update creates files at wrong paths or misses files entirely.
**Prevention**:
- **Best**: Eliminate the asymmetry — have the manual path call the automatic one (e.g., `collectTemplateFiles()` calls `getAllScripts()` instead of maintaining its own list)
- **If asymmetry is unavoidable**: Add a regression test that compares outputs from both mechanisms
- When migrating directory structures, search for ALL code paths that reference the old structure
**Real example**: `trellis update` had a manual `files.set()` list for 11 scripts that `getAllScripts()` already tracked. Fix: replaced the manual list with a `for..of getAllScripts()` loop. See `update.ts` refactor in v0.4.0-beta.3.
---
## Template File Registration (Trellis-specific)
When adding new files to `src/templates/trellis/scripts/`:
That's it. `commands/update.ts` uses `getAllScripts()` directly — no manual sync needed.
**Why this matters**: Without registration in `getAllScripts()`, `trellis update` won't sync the file to user projects. Bug fixes and features won't propagate.
**History**: Before v0.4.0-beta.3, `update.ts` had its own hand-maintained file list that frequently fell out of sync with `getAllScripts()`. This caused 11 Python files to be silently skipped during `trellis update`. The fix was to eliminate the duplicate list and use `getAllScripts()` as the single source of truth.
### Quick Checklist for New Scripts
```bash
# After adding a new .py file, verify it's in getAllScripts():
grep -l "newFileName" src/templates/trellis/index.ts # Should match
```
### Template Sync Convention
`.trellis/scripts/` (dogfooded) and `packages/cli/src/templates/trellis/scripts/` (template) must stay identical. After editing `.trellis/scripts/`, always sync:
`.trellis/spec/` holds coding guidelines organized by package and layer.
-`.trellis/spec/<package>/<layer>/index.md` — entry point with **Pre-Development Checklist** + **Quality Check**. Actual guidelines live in the `.md` files it points to.
python3 ./.trellis/scripts/get_context.py --mode packages # list packages / layers
```
**When to update spec**: new pattern/convention found · bug-fix prevention to codify · new technical decision.
### Task System
Every task has its own directory under `.trellis/tasks/{MM-DD-name}/` holding `task.json`, `prd.md`, optional `design.md`, optional `implement.md`, optional `research/`, and context manifests (`implement.jsonl`, `check.jsonl`) for sub-agent-capable platforms.
> Run `python3 ./.trellis/scripts/task.py --help` to see the authoritative, up-to-date list.
**Current-task mechanism**: `task.py create` creates the task directory and (when session identity is available) auto-sets the per-session active-task pointer so the planning breadcrumb fires immediately. `task.py start` writes the same pointer (idempotent if already set) and flips `task.json.status` from `planning` to `in_progress`. State is stored under `.trellis/.runtime/sessions/`. If no context key is available from hook input, `TRELLIS_CONTEXT_ID`, or a platform-native session environment variable, there is no active task and `task.py start` fails with a session identity hint. `task.py finish` deletes the current session file (status unchanged). `task.py archive <task>` writes `status=completed`, moves the directory to `archive/`, and deletes any runtime session files that still point at the archived task.
### Workspace System
Records every AI session for cross-session tracking under `.trellis/workspace/<developer>/`.
-`journal-N.md` — session log. **Max 2000 lines per file**; a new `journal-(N+1).md` is auto-created when exceeded.
-`index.md` — personal index (total sessions, last active).
Phase 1: Plan → classify, get task-creation consent, then write planning artifacts
Phase 2: Execute → implement only after task status is in_progress
Phase 3: Finish → verify, update spec, commit, and wrap up
```
### Request Triage
- Simple conversation or small task: ask only whether this turn should create a Trellis task. If the user says no, skip Trellis for this session.
- Complex task: ask whether you may create a Trellis task and enter planning. If the user says no, do not do broad inline implementation; explain, clarify scope, or suggest a smaller split.
- User approval to create a task is not approval to start implementation. Planning still happens first.
### Planning Artifacts
-`prd.md` — requirements, constraints, and acceptance criteria. Do not put technical design or execution checklists here.
-`design.md` — technical design for complex tasks: boundaries, contracts, data flow, tradeoffs, compatibility, rollout / rollback shape.
-`implement.md` — execution plan for complex tasks: ordered checklist, validation commands, review gates, and rollback points.
-`implement.jsonl` / `check.jsonl` — spec and research manifests for sub-agent context. They do not replace `implement.md`.
- Lightweight tasks may be PRD-only. Complex tasks must have `prd.md`, `design.md`, and `implement.md` before `task.py start`.
### Parent / Child Task Trees
Use a parent task when one user request contains several independently verifiable deliverables. The parent task owns the source requirement set, the task map, cross-child acceptance criteria, and final integration review; it normally should not be the implementation target unless it also has direct work.
Use child tasks for deliverables that can be planned, implemented, checked, and archived independently. Parent/child structure is not a dependency system: if one child must wait for another, write that ordering in the child `prd.md` / `implement.md` and keep each child's acceptance criteria testable.
Create new children with `task.py create "<title>" --slug <name> --parent <parent-dir>`. Link existing tasks with `task.py add-subtask <parent> <child>`, and unlink mistakes with `task.py remove-subtask <parent> <child>`.
<!-- Per-turn breadcrumb: shown when there is no active task (before Phase 1) -->
[workflow-state:no_task]
No active task. First classify the current turn and ask for task-creation consent before creating any Trellis task.
Simple conversation / small task: ask only whether this turn should create a Trellis task. If the user says no, skip Trellis for this session.
Complex task: ask the user if you can create a Trellis task and enter the planning phase. If the user says no, explain, clarify scope, or suggest a smaller split.
- 1.4 Activate task `[required · once]` (review gate, then `task.py start`; status → in_progress)
- 1.5 Completion criteria
<!-- Per-turn breadcrumb: shown throughout Phase 1 (status='planning') -->
[workflow-state:planning]
Load `trellis-brainstorm`; stay in planning.
Lightweight: `prd.md` can be enough. Complex: finish `prd.md`, `design.md`, and `implement.md`; ask for review before `task.py start`.
Multi-deliverable scope: consider a parent task plus independently verifiable child tasks; dependencies must be written in child artifacts, not implied by tree position.
Sub-agent mode: curate `implement.jsonl` and `check.jsonl` as spec/research manifests before start.
[/workflow-state:planning]
<!-- Per-turn breadcrumb: shown throughout Phase 1 when codex.dispatch_mode=inline.
Codex-only opt-in alternate to [workflow-state:planning]. The main agent
edits code directly in Phase 2, so jsonl curation is skipped —
the inline workflow loads `trellis-before-dev` instead of injecting JSONL
into a sub-agent. -->
[workflow-state:planning-inline]
Load `trellis-brainstorm`; stay in planning.
Lightweight: `prd.md` can be enough. Complex: finish `prd.md`, `design.md`, and `implement.md`; ask for review before `task.py start`.
Multi-deliverable scope: consider a parent task plus independently verifiable child tasks; dependencies must be written in child artifacts, not implied by tree position.
<!-- Per-turn breadcrumb: shown while status='in_progress'.
Scope: all of Phase 2 + Phase 3.2-3.4 (status stays 'in_progress' from
task.py start until task.py archive; only archive flips it). The body
therefore must cover every required step from implementation through
commit, including Phase 3.3 spec update and Phase 3.4 commit. -->
Sub-agent dispatch protocol applies to all platforms and all sub-agents, including class-2 Codex/Gemini/Qoder/Copilot/ZCode/Reasonix/Trae and `trellis-research`: every dispatch prompt starts with `Active task: <task path from task.py current>` before role-specific instructions.
[workflow-state:in_progress]
Tools: `trellis-implement` / `trellis-research` are sub-agent types only (Task/Agent tool, NOT Skill; there is no skill by these names). `trellis-update-spec` is a skill. `trellis-check` exists as both; prefer the Agent form when verifying after code changes.
Main-session default: dispatch implement/check sub-agents. Sub-agent self-exemption: if already running as `trellis-implement`, do NOT spawn another `trellis-implement` or `trellis-check`; if already running as `trellis-check`, do NOT spawn another `trellis-check` or `trellis-implement`. Dispatch is main session only.
Dispatch prompt starts with `Active task: <task path from task.py current>`. Read context: jsonl entries -> `prd.md` -> `design.md if present` -> `implement.md if present`.
[/workflow-state:in_progress]
<!-- Per-turn breadcrumb: shown while status='in_progress' when
codex.dispatch_mode=inline. Codex-only opt-in alternate to
[workflow-state:in_progress]. The main session edits code directly
Do not dispatch implement/check sub-agents in inline mode.
Read context: `prd.md` -> `design.md if present` -> `implement.md if present`, plus relevant spec/research loaded by skills.
[/workflow-state:in_progress-inline]
### Phase 3: Finish
- 3.2 Debug retrospective `[on demand]`
- 3.3 Spec update `[required · once]`
- 3.4 Commit changes `[required · once]`
- 3.5 Wrap-up reminder
> Note: step 3.1 was folded into 2.2 (last-iteration full-scope check) and 3.4 (commit preamble). Numbering kept stable to avoid breaking external references.
<!-- Per-turn breadcrumb: shown while status='completed'.
Currently DEAD in normal flow: cmd_archive writes status='completed' in
the same call that moves the task dir to archive/, so the active-task
resolver loses the pointer and the hook never fires on archived tasks.
Block preserved for a future status-transition redesign (e.g. an
explicit in_progress→completed command). Edit through the same spec
channel as the live blocks. -->
[workflow-state:completed]
Code committed. Run `/trellis:finish-work`; if dirty, return to Phase 3.4 first.
[/workflow-state:completed]
### Rules
1. Identify which Phase you're in, then continue from the next step there
2. Run steps in order inside each Phase; `[required]` steps can't be skipped
3. Phases can roll back (e.g., Execute reveals a prd defect → return to Plan to fix, then re-enter Execute)
4. Steps tagged `[once]` are skipped if the output already exists; don't re-run
5. Artifact presence informs the next step; missing `design.md` / `implement.md` is valid for lightweight tasks and incomplete planning for complex tasks.
### Active Task Routing
When a user request matches one of these intents inside an active task, route first, then load the detailed phase step if needed.
# e.g. python3 ./.trellis/scripts/get_context.py --mode phase --step 1.1
```
---
## Phase 1: Plan
Goal: classify the request, get task-creation consent when a task is needed, and produce the planning artifacts required before implementation.
#### 1.0 Create task `[required · once]`
Create the task directory only after task-creation consent. The command sets status to `planning`, writes `task.json`, creates a default `prd.md`, and auto-targets the new task when session identity is available:
`--slug` is the human-readable name only. Do **not** include the `MM-DD-` date prefix; `task.py create` adds that prefix automatically.
For task trees, create the parent task first and then create each child with `--parent <parent-dir>`. Do not start the parent just because children exist; start the child that owns the next independently verifiable deliverable.
After this command succeeds, the per-turn breadcrumb auto-switches to `[workflow-state:planning]`, telling the AI to stay in planning.
Run only `create` here — do not also run `start`. `start` flips status to `in_progress`, which switches the breadcrumb to the implementation phase before planning artifacts are reviewed. Save `start` for step 1.4.
Skip when `python3 ./.trellis/scripts/task.py current --source` already points to a task.
Load the `trellis-brainstorm` skill and explore requirements interactively with the user per the skill's guidance.
The brainstorm skill will guide you to:
- Ask one question at a time
- Prefer researching over asking the user
- Prefer offering options over open-ended questions
- Update `prd.md` immediately after each user answer
- Split large scopes into a parent task plus child tasks when the deliverables can be verified independently
- Keep `prd.md` focused on requirements and acceptance criteria
- For complex tasks, produce `design.md` and `implement.md` before implementation starts
When considering a parent/child split:
- Use a parent task when one request contains several independently verifiable deliverables.
- Parent tasks own source requirements, child-task mapping, cross-child acceptance criteria, and final integration review.
- Child tasks own actual deliverables that can be planned, implemented, checked, and archived independently.
- Parent/child structure is not a dependency system. If child B depends on child A, write that ordering in child B's `prd.md` / `implement.md`.
- Start the child task that owns the next deliverable. Do not start the parent unless the parent itself has direct implementation work.
Return to this step whenever requirements change and revise the relevant artifact.
#### 1.2 Research `[optional · repeatable]`
Research can happen at any time during requirement exploration. It isn't limited to local code — you can use any available tool (MCP servers, skills, web search, etc.) to look up external information, including third-party library docs, industry practices, API references, etc.
Do the research in the main session directly and write findings into `{TASK_DIR}/research/`. (For `codex-inline` this avoids the `fork_turns="none"` isolation that prevents `trellis-research` sub-agents from resolving the active task path.)
[/codex-inline, Kilo, Antigravity, Devin]
**Research artifact conventions**:
- One file per research topic (e.g. `research/auth-library-comparison.md`)
- Record third-party library usage examples, API references, version constraints in files
- Note relevant spec file paths you discovered for later reference
Brainstorm and research can interleave freely — pause to research a technical question, then return to talk with the user.
**Key principle**: Research output must be written to files, not left only in the chat. Conversations get compacted; files don't.
Curate `implement.jsonl` and `check.jsonl` so the Phase 2 sub-agents get the right spec/research context. These files were seeded on `task create` with a single self-describing `_example` line; your job here is to fill in real entries.
**Location**: `{TASK_DIR}/implement.jsonl` and `{TASK_DIR}/check.jsonl` (already exist).
**Format**: one JSON object per line — `{"file": "<path>", "reason": "<why>"}`. Paths are repo-root relative.
**What to put in**:
- **Spec files** — `.trellis/spec/<package>/<layer>/index.md` and any specific guideline files (`error-handling.md`, `conventions.md`, etc.) relevant to this task
- **Research files** — `{TASK_DIR}/research/*.md` that the sub-agent will need to consult
**What NOT to put in**:
- Code files (`src/**`, `packages/**/*.ts`, etc.) — those are read by the sub-agent during implementation, not pre-registered here
- Files you're about to modify — same reason
**Split between the two files**:
-`implement.jsonl` → specs + research the implement sub-agent needs to write code correctly
-`check.jsonl` → specs for the check sub-agent (quality guidelines, check conventions, same research if needed)
These manifests do not replace `implement.md`. `implement.md` is the human-readable execution plan for a complex task; jsonl files only list context files to inject or load.
Delete the seed `_example` line once real entries exist (optional — it's skipped automatically by consumers).
Ready gate: both `implement.jsonl` and `check.jsonl` must contain at least one real `{"file": "...", "reason": "..."}` entry before `task.py start`. The seed `_example` row alone is not ready.
Skip this step only when both files already have real curated entries.
For lightweight tasks, `prd.md` can be enough. For complex tasks, `prd.md`, `design.md`, and `implement.md` must exist and be reviewed before start. On sub-agent-dispatch platforms, `implement.jsonl` and `check.jsonl` must both have real curated entries before start. Runtime consumers tolerate missing or seed-only manifests for compatibility, but that tolerance is not a planning-ready state.
After this command succeeds, the breadcrumb auto-switches to `[workflow-state:in_progress]`, and the rest of Phase 2 / 3 follows.
If `task.py start` errors with a session-identity message (no context key from hook input, `TRELLIS_CONTEXT_ID`, or platform-native session env), follow the hint in the error to set up session identity, then retry.
#### 1.5 Completion criteria
| Condition | Required |
|------|:---:|
| `prd.md` exists | ✅ |
| User confirms task should enter implementation | ✅ |
| `task.py start` has been run (status = in_progress) | ✅ |
| `research/` has artifacts (complex tasks) | recommended |
- **Task description**: Implement the reviewed task artifacts, consulting materials under `{TASK_DIR}/research/`; finish by running project lint and type-check
- **Dispatch prompt guard**: Tell the spawned agent it is already the `trellis-implement` sub-agent and must implement directly, not spawn another `trellis-implement` / `trellis-check`.
The platform hook/plugin auto-handles:
- Reads `implement.jsonl` and injects referenced spec/research files into the agent prompt
- Injects `prd.md`, `design.md` if present, and `implement.md` if present
- **Task description**: Implement the reviewed task artifacts, consulting materials under `{TASK_DIR}/research/`; finish by running project lint and type-check
- **Dispatch prompt guard**: The prompt MUST start with `Active task: <task path>`, then explicitly say the spawned agent is already `trellis-implement` and must implement directly without spawning another `trellis-implement` / `trellis-check`.
The pull-based sub-agent definition auto-handles the context load requirement:
- Resolves the active task with `task.py current --source`, then reads `prd.md`, `design.md` if present, and `implement.md` if present
- Reads `implement.jsonl` and requires the agent to load each referenced spec/research file before coding
- **Task description**: Implement the reviewed task artifacts, consulting materials under `{TASK_DIR}/research/`; finish by running project lint and type-check
- **Dispatch prompt guard**: Tell the spawned agent it is already the `trellis-implement` sub-agent and must implement directly, not spawn another `trellis-implement` / `trellis-check`.
The platform prelude auto-handles the context load requirement:
- Reads `implement.jsonl` and injects referenced spec/research files into the agent prompt
- Injects `prd.md`, `design.md` if present, and `implement.md` if present
[/Kiro]
[codex-inline, Kilo, Antigravity, Devin]
1. Load the `trellis-before-dev` skill to read project guidelines
2. Read `{TASK_DIR}/prd.md`, then `design.md` if present, then `implement.md` if present
- **Task description**: Review all code changes against specs and task artifacts; fix any findings directly; ensure lint and type-check pass
- **Dispatch prompt guard**: Tell the spawned agent it is already the `trellis-check` sub-agent and must review/fix directly, not spawn another `trellis-check` / `trellis-implement`.
The check agent's job:
- Review code changes against specs
- Review code changes against `prd.md`, `design.md` if present, and `implement.md` if present
If issues are found → fix → re-check, until green.
[/codex-inline, Kilo, Antigravity, Devin]
**Final pass (before Phase 3.4 commit)**: the last 2.2 of a task must run full-scope, not just on the latest implement chunk. List all affected packages with `python3 ./.trellis/scripts/get_context.py --mode packages`, then load each package's spec index Quality Check section. This catches cross-layer / multi-package issues a mid-iteration local 2.2 cannot.
#### 2.3 Rollback `[on demand]`
-`check` reveals a prd defect → return to Phase 1, fix `prd.md`, then redo 2.1
- Implementation went wrong → revert code, redo 2.1
- Need more research → research (same as Phase 1.2), write findings into `research/`
---
## Phase 3: Finish
Goal: ensure code quality, capture lessons, record the work.
#### 3.2 Debug retrospective `[on demand]`
If this task involved repeated debugging (the same issue was fixed multiple times), load the `trellis-break-loop` skill to:
- Classify the root cause
- Explain why earlier fixes failed
- Propose prevention
The goal is to capture debugging lessons so the same class of issue doesn't recur.
#### 3.3 Spec update `[required · once]`
Load the `trellis-update-spec` skill and review whether this task produced new knowledge worth recording:
- Newly discovered patterns or conventions
- Pitfalls you hit
- New technical decisions
Update the docs under `.trellis/spec/` accordingly. Even if the conclusion is "nothing to update", walk through the judgment.
#### 3.4 Commit changes `[required · once]`
**Spec-sync preamble**: before drafting commits, ask: did this task fix a bug or surface non-obvious knowledge that should land in `.trellis/spec/` so future-you (or future-AI) doesn't repeat the mistake? If yes, return to Phase 3.3 first — spec writes belong in the same task's commit batch, not as a forgotten follow-up.
The AI drives a batched commit of this task's code changes so `/finish-work` can run cleanly afterwards. Goal: produce work commits FIRST, then bookkeeping (archive + journal) commits land after — never interleaved.
**Step-by-step**:
1.**Inspect dirty state**:
```bash
git status --porcelain
```
Snapshot every dirty path. If the working tree is clean, skip to 3.5.
2. **Learn commit style** from recent history (so drafted messages blend in):
```bash
git log --oneline -5
```
Note the prefix convention (`feat:` / `fix:` / `chore:` / `docs:` ...), language (中文/English), and length style.
3. **Classify dirty files into two groups**:
- **AI-edited this session** — files you wrote/edited via Edit/Write/Bash tool calls in this session. You know what changed and why.
- **Unrecognized** — dirty files you did NOT touch this session (could be the user's manual edits, leftover WIP from a previous session, or unrelated work). Do NOT silently include these.
4. **Draft a commit plan**. Group AI-edited files into logical commits (1 commit per coherent change unit, not 1 commit per file). Each entry: `<commit message>` + file list. List unrecognized files separately at the bottom.
5. **Present the plan once, ask for one-shot confirmation**. Format:
```
Proposed commits (in order):
1. <message>
- <file>
- <file>
2. <message>
- <file>
Unrecognized dirty files (NOT in any commit — confirm include/exclude):
- <file>
- <file>
Reply 'ok' / '行' to execute. Reply with edits, or '我自己来' / 'manual' to abort.
```
6. **On confirmation**: run `git add <files>` + `git commit -m "<msg>"` for each batch in order. Do not amend. Do not push.
7. **On rejection** (user replies "不行" / "我自己来" / "manual" / any pushback on the plan): stop. Do not attempt a second plan. The user will commit by hand; you skip ahead to 3.5 once they confirm.
- If the user wants different message wording but accepts the file grouping, edit the message and re-confirm once — but if they reject the grouping, exit to manual mode.
- The batched plan is one prompt; do not prompt per commit.
#### 3.5 Wrap-up reminder
After the above, remind the user they can run `/finish-work` to wrap up (archive the task, record the session).
---
## Customizing Trellis (for forks)
This section is for developers who want to modify the Trellis workflow itself. All customization is done by editing this file; the scripts are parsers only.
### Changing what a step means
Edit the corresponding step's walkthrough body in the Phase 1 / 2 / 3 sections above. Critical invariants:
- No active task must triage first and ask for task-creation consent before creating a Trellis task.
- Planning must distinguish lightweight PRD-only tasks from complex tasks that require `prd.md`, `design.md`, and `implement.md` before start.
- Every required execution path must keep the Phase 3.4 commit reminder reachable before `/trellis:finish-work`.
All tag blocks live in the `## Phase Index` section above, immediately after each phase summary:
| Scope | Corresponding tag |
|---|---|
| No active task (before Phase 1) | `[workflow-state:no_task]` (after the Phase Index ASCII art) |
| All of Phase 1 (task created → ready for implementation) | `[workflow-state:planning]` (after Phase 1 summary) |
Directly edit the body of the corresponding `[workflow-state:STATUS]` block. After editing, run `trellis update` (if you're a template maintainer) or restart your AI session (if you're customizing your own project) — no script changes required.
### Adding a custom status
Add a new block:
```
[workflow-state:my-status]
your per-turn prompt text
[/workflow-state:my-status]
```
Constraints:
- STATUS charset: `[A-Za-z0-9_-]+` (underscores and hyphens allowed, e.g. `in-review`, `blocked-by-team`)
- A lifecycle hook must write `task.json.status` to your custom value, otherwise the tag is never read
- Lifecycle hooks live in `task.json.hooks.after_*` and bind to one of `after_create / after_start / after_finish / after_archive`
### Adding a lifecycle hook
Add a `hooks` field to your `task.json`:
```json
{
"hooks": {
"after_finish": [
"your-script-or-command-here"
]
}
}
```
Supported events: `after_create / after_start / after_finish / after_archive`. Note that `after_finish` ≠ a status change (it only clears the active-task pointer); use `after_archive` for "task is done" notifications.
### Full contract
For the workflow state machine's runtime contract, the locations of all status writers, pseudo-statuses (`no_task` / `stale_<source_type>`), the hook reachability matrix, and other deep details, see:
- `.trellis/scripts/inject-workflow-state.py` — actual parser (reads workflow.md only, no embedded text)
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