/geminion | /geminioff | /geministatus

Toggle auto-delegate mode — Gemini automatically handles all coding tasks without requiring /gemini each time.

Run the bash command, print one confirmation line, and stop.

/gemini-report

If the user invokes /gemini-report, run ~/tools/delegate-report with any flags extracted from the arguments, display output verbatim, and stop.

Gemini Orchestrator

When the user invokes /gemini <instruction>, Claude delegates the implementation to Gemini CLI via its headless mode (-p/--prompt), monitors in real time, and reports.

Known Limits

Hard constraints of the Gemini CLI — not config options.

1. No --max-turns flag

Vibe lets you cap turn count (--max-turns 8). Gemini CLI has no equivalent. Timeout is the only runaway-control lever. A stuck run burns the full timeout before dying. Set timeouts conservatively and decompose tasks.

2. High context overhead (~900–10k tokens before your task starts)

Gemini CLI loads a large default system prompt on every run:

• Simple prompt → ~883 tokens before the model responds
• File-read task → ~10k tokens of context before first tool call

This means:

• Each run costs more than token-naive estimates suggest
• Short timeouts can expire during context-loading on a slow connection
• The overhead is mostly cached on repeated calls to the same model in a session

3. 503 backoff eats your timeout silently

On the free-tier Gemini API, the model is frequently "under high demand." The CLI auto-retries with exponential backoff — observed taking 60–90s before work even begins. This is invisible until you see the first tool call.

Always add 90s buffer to your "real work" estimate:

Timeout budget = expected_work_secs + 90s backoff buffer + 30s context load

4. No --agent flag

Gemini CLI is single-mode only. There is no way to switch to a review-only or plan-only agent. Use plan mode (--approval-mode plan) as a partial substitute.

5. No --workdir flag

The delegate script handles this by cd-ing into the workdir before running.

6. No pseudo-TTY needed (positive difference vs Vibe)

Gemini CLI works fine in a plain pipe — no script -q -c wrapper needed.

7. Orchestration chain has 5 independent failure points

The delegation pipeline is: Gemini CLI -> plain pipe -> Python stream parser -> result event tokens -> git diff -> JSON log. Each link can fail independently:

When a run produces unexpected results, check these links top to bottom.

Step 1 — Detect workdir

1. git rev-parse --show-toplevel in the current directory.
2. If ambiguous or no git repo → ask with AskUserQuestion.

Step 2 — Choose mode

Use plan mode when you want Gemini to read the codebase and report back without touching any files. Proposed writes appear as [plan-write] and are blocked.

Step 3 — Decompose the task

Critical rule: Gemini works best on atomic, focused tasks. Given the context overhead and 503 risk, keep tasks smaller than you might expect.

Decide whether to delegate at all:

gemini-delegate has real overhead (503 backoff, context load, stream parser, git diff, JSON log). For trivial changes the setup cost exceeds the savings.

The sweet spot is medium to heavy tasks.

Decomposition for complex tasks:

Sub-task 1: Explore relevant files — plan mode, 120s
Sub-task 2: Implement change A in file X — impl mode, 180s
Sub-task 3: Implement change B in file Y — impl mode, 180s
Sub-task 4: Verify / test — plan mode, 120s

→ Check git diff between sub-tasks before launching the next.

Step 4 — Write the Gemini prompt

Gemini has no context from the parent conversation. The prompt must be self-contained.

Structure of a good Gemini prompt:

Stack: Python/Flask, SQLAlchemy, SQLite
Key files: app.py (routes + fetch), models.py (Entry)

TASK: [one single thing to do, stated as an imperative]

CONSTRAINTS:
- [what must not break]
- [expected format if relevant]

VERIFY: grep for "def function_name" in file.py and confirm it exists.

Formulation rules:

• One task per prompt — never "also do X and Y"
• Name the exact files to modify
• Include a grep-based verification criterion (not a file re-read)
• Language: English (better Gemini performance)
• Keep prompts under ~500 words — longer prompts increase context overhead

Prompt adaptations:

• Any task that defines or calls a specific function: include the exact signature — def validate(data: dict) -> tuple[bool, list[str]]:.
• No fixed signature, but conventions matter: point at the file to read first ("read app.py, follow its route/jsonify style") instead — don't do both, they're substitutes.

Verification — always use grep, not file re-read:

VERIFY: grep for "def extract_labels" in app.py and confirm it exists.

A grep is unambiguous. A file re-read can miss content outside the context window.

Examples:

❌ Bad (too vague, too wide):

Fix the API, add a signal classifier, update the UI with colored badges

✅ Good (atomic, verifiable):

Stack: Python/Flask. File: app.py

TASK: In fetch_data(), convert the date string (format "YYYY-MM-DD")
to datetime.date before returning, and convert id to str.

VERIFY: grep for "datetime.date" in app.py and confirm it exists.

Step 5 — Launch Gemini

~/tools/gemini-delegate "<workdir>" "<prompt>" [timeout-secs] [mode]

Recommended timeouts:

• Plan/explore only: 120
• Simple change (1 file): 180
• Medium change (2–3 files): 270
• Hard ceiling: 300 — decompose instead

Examples:

# Explore only — safe, no writes
~/tools/gemini-delegate "/path/to/project" "Read app.py and describe the route structure" 120 plan

# Implement a single-file change
~/tools/gemini-delegate "/path/to/project" "Stack: Flask. File: app.py. TASK: ..." 180 impl

# Background run
~/tools/gemini-delegate "/path/to/project" "..." 240 impl > /tmp/gemini_out.txt 2>&1 &
# Monitor with: tail -f /tmp/gemini_out.txt

Step 6 — Supervise in real time

The script prints live:

=== GEMINI START ===
Workdir : /path/to/project
Mode    : impl (yolo)
Timeout : 180s
Prompt  : Stack: Python/Flask. File: app.py ...
====================
  [init]   model=gemini-2.5-flash
  [read]   app.py
  [write]  app.py
  [gemini] Done. Converted date to datetime.date in fetch_data().
Tool calls: 3
Gemini tokens: 1,234  (900 in + 334 out, 0 cached)  |  ~$0.0003  (8.2s)
Claude Sonnet 4.6 eq: same tokens ~$0.0077  (ratio x25.7)
=== GEMINI DONE (exit: 0) ===
=== SYNTAX OK (1 file(s) checked) ===

=== UNCOMMITTED CHANGES ===
 app.py | 4 ++--
[log] → ~/.local/share/delegate-runs.jsonl  (1234 tokens, exit 0, 42.1s)

In plan mode, proposed writes are blocked and shown as [plan-write]:

  [read]        app.py
  [plan-write]  app.py   ← proposed but blocked
  [gemini]      Here is what I would change: ...
=== PLAN MODE — no files written ===

Event types emitted by the parser:

Gemini never commits. All changes are left unstaged — git checkout . reverts everything if needed.

Red flags to act on immediately:

• [WARN] → Gemini hit a tool error
• exit: 1 or non-zero → Gemini failed or left verification incomplete
• No [write] after 120s → looping or task too vague
• === SYNTAX ERRORS === → fix before committing
• === GEMINI TIMEOUT === → check what was done before retrying
• Same file read 5+ times → Gemini is circling; run likely lost

Common issues and workarounds:

Step 7 — Iteration

• Max 3 attempts per sub-task before escalating to the user.
• Between attempts, read the git diff to avoid doubling partial work.
• If Gemini did 50% and timed out: complete the rest manually rather than relaunching.
• If 503s are eating all attempts: pause and retry in 10+ minutes.

Step 7b — Log manual completion

When you finish a task manually (after Gemini failures), run this:

python3 -c "
import json, datetime, subprocess, os
workdir = subprocess.run(['git','rev-parse','--show-toplevel'], capture_output=True, text=True).stdout.strip() or os.getcwd()
project = os.path.basename(workdir.rstrip('/'))
stat = subprocess.run(['git','-C',workdir,'diff','--stat'], capture_output=True, text=True).stdout
lines_added = sum(int(l.split('+')[1].split()[0]) for l in stat.splitlines() if '|' in l and '+' in l) if stat else 0
files_changed = len([l for l in stat.splitlines() if '|' in l])
tokens_out = lines_added * 10
tokens_in  = lines_added * 40
cost = (tokens_in * 3.0 + tokens_out * 15.0) / 1_000_000
entry = {'ts': datetime.datetime.utcnow().isoformat() + 'Z', 'delegate': 'claude-manual', 'workdir': workdir, 'project': project, 'exit_code': 0, 'files_changed': files_changed, 'tokens_in': tokens_in, 'tokens_out': tokens_out, 'tokens_total': tokens_in + tokens_out, 'cost_usd': round(cost, 6), 'cost_estimated': True, 'lines_added': lines_added}
log = os.path.expanduser('~/.local/share/delegate-runs.jsonl')
open(log, 'a').write(json.dumps(entry) + '\n')
print(f'[log] claude-manual -> {project}  ~{lines_added} lines  est. cost ${cost:.4f}')
"

Run from anywhere inside the project. Flagged cost_estimated true in the log.

Step 8 — Report to the user

✓ Gemini finished — <1-line summary>

Files modified:
  - path/to/file.ext (+X / -Y lines)

[If problem]:
⚠ <description> — completing manually / retrying?

Ready to commit?

Orchestration rules

• Decompose before delegating — one giant prompt + high context overhead = timeout.
• Use plan mode first for any task touching >2 files — safer, free exploration.
• Check diff between sub-tasks — never launch the next step blind.
• Don't code in Gemini's place unless Gemini did ≥50% and timed out.
• Timeout is the only turn limit — set it conservatively; decompose rather than extending.
• VERIFY with grep, not re-read — grep -n "def foo" file.py is unambiguous.
• Add 90s to every timeout — 503 backoff is invisible and frequent.

Token economics

Gemini's tool calls (file reads, writes) consume Gemini tokens, not Claude tokens. Claude receives only the compressed final output (~200–800 tokens/run).

Approximate pricing (Gemini 2.5 Flash):

• ~$0.15/M input tokens, ~$0.60/M output tokens
• Claude Sonnet 4.6: ~$3/M input, ~$15/M output
• Typical cost ratio: ~20–30x cheaper per token than Claude
• Note: context overhead (~900–10k tokens/run) makes per-run cost higher than token math suggests

Free-tier caps (Google AI Studio, no billing):

• ~60 requests/minute
• ~1,000 requests/day
• Model availability varies (503 = cap or demand spike)

Real token counts and cost are printed after every run and appended to the run log.

Run Log

Every run appends one JSON entry to ~/.local/share/delegate-runs.jsonl.

Fields logged:

Useful queries:

# All recent runs
cat ~/.local/share/delegate-runs.jsonl | python3 -m json.tool | less

# Success rate
jq -r '[.exit_code] | @tsv' ~/.local/share/delegate-runs.jsonl | sort | uniq -c

# Timed-out runs
jq 'select(.timed_out == true)' ~/.local/share/delegate-runs.jsonl

# Total cost
jq -r '.cost_usd' ~/.local/share/delegate-runs.jsonl \
  | awk '{sum+=$1} END {printf "Total: $%.4f\n", sum}'

See Also

A sister delegate using Mistral Vibe exists: vibe-skill. Both write to the same delegate-runs.jsonl log, making runs comparable across delegates.

This skill is improved regularly — run update-skills to pull the latest version of this skill, as well as all your other skills!