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❯ New AI session
● Loaded: workflow, 3 active tasks, branch feat/v0.5.0-rc
● What would you like to work on?
❯ Add Gemini CLI support, similar to how Cursor is integrated
● Bash(task.py create “Gemini CLI support” —slug gemini-cli)
▶ research(Find platform integration specs and code patterns)
└ Initializing…
└ Done (36 tool uses · 86.5k tokens · 2m 25s) ✓
● Bash(task.py add-context … platform-integration.md, cursor.ts, ai-tools.ts)
└ 6 spec files added to implement.jsonl
● Bash(task.py start …): hooks will inject context into agents
▶ implement(Implement Gemini CLI platform)
└ Writing src/configurators/gemini.ts…
└ Done (99 tool uses · 162.5k tokens · 12m 3s) ✓
● TypeCheck ✓ · Lint ✓ · Tests: 337/337 ✓
▶ check(Review implementation against code-specs)
└ Reading diff… 14 files changed
└ Found 1 issue: missing EXCLUDE_PATTERNS entry
└ Fixed automatically ✓
❯ Capture Gemini CLI conventions in specs
● Read(.trellis/spec/backend/platform-integration.md)
● Update(platform-integration.md): added Gemini CLI conventions
❯ /trellis:finish-work
● Bash(task.py archive gemini-cli)
● Bash(add_session.py —title “feat: Gemini CLI support” —commit “ec6114a”)
└ Task archived. Session recorded to journal-4.md.
❯

Session loaded

AI reads your project context: workflow rules, active tasks, git status, and recent journal entries.

Natural language input

Describe your feature in plain language. Trellis creates a tracked task with a structured PRD.

Research & configure

trellis-research sub-agent finds relevant specs and code patterns. Context files configured in jsonl: hooks auto-inject them into agents.

Implement

Agent writes code across 5 layers following project conventions. 99 tool calls, all 337 tests pass on first try.

Quality check

trellis-check sub-agent reviews every changed file against code-specs. Issues found and fixed automatically.

Update specs

New patterns captured into the spec library: making future sessions even better.

Session archived

5 atomic commits, session recorded to journal. The branch is ready for review.

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What just happened?

This demo replays a real Trellis session where we added Gemini CLI platform support: a feature touching types, templates, configurators, CLI flags, Python adapters, and documentation.

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The workflow

1

Start session

SessionStart hook or extension loads your project context: workflow rules, active tasks, git status, and recent journal entries. The AI is immediately oriented.
2

Describe the feature

You describe what you want in natural language. Trellis creates a tracked task with a structured PRD.
3

Research & configure

trellis-research sub-agent reads 36 files to find relevant specs and code patterns. Context files are configured in jsonl so agents receive the right conventions via hooks.
4

Implement

trellis-implement sub-agent writes code across 5 layers (types → templates → configurator → CLI → Python). 99 tool calls. All 337 tests pass on first try.
5

Quality check

trellis-check sub-agent reviews every changed file against code-specs. Finds 1 missing EXCLUDE_PATTERNS entry and fixes it automatically.
6

Update specs

The trellis-update-spec skill captures new patterns learned from this session into the spec library: making future sessions even better.
7

Finish & ship

/trellis:finish-work archives the task and records the session to your journal. 5 atomic commits on the feature branch, ready for review.

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Key metrics

Try it yourself

Install Trellis, open a new AI session, and describe your feature request.