| name | close |
| description | Universal close skill. Just say "close" and it auto-routes to the right close skill
(thread-close, drop-close, or build-close) based on SESSION_STATE context.
Works in both native Zo and Claude Code environments.
|
| compatibility | Created for Zo Computer |
| metadata | {"author":"<YOUR_HANDLE>.zo.computer"} |
Close
Step 1: Detect Environment
Check if running in Claude Code:
python3 N5/scripts/cc_close_bridge.py detect
- If
CLAUDE_CODE=false → Native Zo path (Step 2a)
- If
CLAUDE_CODE=true → Claude Code path (Step 2b)
Step 2a: Native Zo
Run the router with the current conversation ID:
python3 Skills/thread-close/scripts/router.py --convo-id <CONVO_ID>
The router reads SESSION_STATE and picks the right skill.
Step 2b: Claude Code
Claude Code sessions don't have a Zo conversation ID or SESSION_STATE.
Use the bridge to create a synthetic workspace first.
You (the LLM) must determine these from conversation context:
- type — What kind of work was this? (
build, research, discussion, planning, debug)
- focus — One-line summary of what the session was about
- tier — Assess complexity:
- Tier 1: Simple discussion, < 3 artifacts
- Tier 2: Standard work, 3-10 artifacts, research
- Tier 3: Builds, complex multi-file work, orchestration
- build-slug (optional) — If this was work on a Pulse build
- artifacts (optional) — Key files created or modified
Then run:
python3 N5/scripts/cc_close_bridge.py init
--type <type>
--focus "<focus>"
--tier <tier>
Add --build-slug <slug> if build context applies.
Add --artifacts path1 path2 ... for key files.
Capture the CC_CONVO_ID from output, then run the normal router:
python3 Skills/thread-close/scripts/router.py --convo-id <CC_CONVO_ID>
Step 3: Follow Thread-Close Instructions
From here the flow is identical in both environments.
Read and follow file Skills/thread-close/SKILL.md (or whichever skill the router selected).
The script outputs JSON context → you do semantic analysis → you call write functions → you echo the title.
