shenxingy/iloop
configs/skills/iloop/SKILL.md
In-session iterative loop — keeps Claude running in the current session until a task is done. Unlike /loop (which spawns background workers), /iloop stays in the current session and re-prompts each iteration via Stop hook. Use for: 'keep fixing until tests pass', 'iterate until this feature works', autonomous debugging. Triggers on: '/iloop', 'in-session loop', 'keep iterating', 'loop until done'.
$ install --global
skillsauthnpx skillsauth add shenxingy/claude-code-kit iloopInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Jun 10, 2026, 5:51 AM27.5s1 file scanned
SKILL.md
- name:
- iloop
- description:
- In-session iterative loop — keeps Claude running in the current session until a task is done. Unlike /loop (which spawns background workers), /iloop stays in the current session and re-prompts each iteration via Stop hook. Use for: 'keep fixing until tests pass', 'iterate until this feature works', autonomous debugging. Triggers on: '/iloop', 'in-session loop', 'keep iterating', 'loop until done'.
- argument-hint:
- "task description" [--max-iterations N] [--completion-promise "TEXT"]
- user_invocable:
- true
iloop — In-Session Iterative Loop
Runs Claude in a self-contained loop within the current session — no background processes, no worktrees. Each iteration, Claude reads the last output, checks progress, makes changes, and verifies. The loop continues until the task is done or max iterations is reached.
How it works
User: /iloop "Fix all failing tests"
↓
Claude starts → works on task → tries to exit
↓
Stop hook detects .claude/iloop.local.md
↓
Feeds task prompt back → Claude continues
↓
Repeat until: <promise>DONE</promise> | <loop-abort> | max iterations
This is different from /loop (which uses background workers + worktrees). Use /iloop when:
- You want to stay in the current session and watch progress
- The task needs iterative self-correction within one context
- You want Claude to autonomously debug/fix until passing
Usage
/iloop "Fix all failing pytest tests"
/iloop "Implement the auth feature" --max-iterations 25
/iloop "Get CI green" --completion-promise "ALL TESTS PASSING" --max-iterations 30
Signals (output anywhere in response to control the loop)
| Signal | Effect |
|--------|--------|
| <loop-abort>reason</loop-abort> | Terminate immediately (e.g. impossible task, needs human decision) |
| <loop-pause>what needed</loop-pause> | Pause — state saved, resume by reopening session |
| <promise>TEXT</promise> | Signal completion (only when --completion-promise set and genuinely true) |
Startup
When /iloop is invoked:
Step 1: Run the setup script:
bash ~/.claude/scripts/setup-iloop.sh "$ARGUMENTS" --max-iterations 20 --completion-promise "LOOP_DONE"
Adjust --max-iterations and --completion-promise based on the task:
- Simple fix: 10-15 iterations
- Feature build: 25-30 iterations
- Open-ended improvement: omit
--completion-promise, use--max-iterations
Step 2: Tell the user:
▸ iloop started — iterating until done (max 20 iterations)
▸ Cancel: rm .claude/iloop.local.md
▸ Monitor: head -8 .claude/iloop.local.md
Step 3: Begin working on the task immediately. Per-iteration protocol:
- Read git log and key files to understand what was done last iteration
- Run verification (tests / build / lint) to check current state
- Make focused progress — fix one thing at a time, not multiple things
- Run verification again after changes — evidence required before marking done
- Scan for related issues (fix A → check B and C)
- Only output
<promise>LOOP_DONE</promise>when ALL criteria are genuinely met
Cancelling
rm .claude/iloop.local.md # Immediate cancel
Or output <loop-abort>reason</loop-abort> from within the session.
Difference from /loop
| | /loop | /iloop |
|--|---------|----------|
| Execution | Background process, parallel workers | Current session, sequential |
| Worktrees | Yes, isolated per worker | No, works on main tree |
| Visibility | Progress via --status | Real-time in session |
| Use case | Large parallel improvements | Focused iterative fixing |
| Context | Each worker starts fresh | Same context across iterations |
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