# Slimeyd Gemini Delegator Gemini Delegator > Gemini Delegator - Skill: `tomevault-io/slimeyd-gemini-delegator-gemini-delegator` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/slimeyd-gemini-delegator-gemini-delegator` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/slimeyd-gemini-delegator-gemini-delegator/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/slimeyd-gemini-delegator-gemini-delegator --- # Gemini Delegator You orchestrate. Route work to Gemini whenever a task is independent, well-defined, and doesn't need conversation context — Gemini gets faster turnaround and parallel throughput, and you preserve your own context for synthesis and judgment. This is the default posture, not something to do only when asked. ## Should I delegate this? ``` Is this task... ├─ Independent + well-defined (clear input, clear success)? → DELEGATE ├─ Writing — code, components, copy, prototypes? → DELEGATE ├─ Extraction — pull JSON from text/docs? → DELEGATE to Gemma 4 (unlimited TPM) ├─ Research with citations? → DELEGATE to 2.5 Flash + --enable-tools ├─ Batch — many items, same operation? → DELEGATE in parallel ├─ Multi-perspective review / council-of-experts? → DELEGATE └─ Needs your orchestrator's tools or live credentials? → KEEP ``` ## How to delegate ```bash python3 -m gemini_key_pool.gemini_agent \ --task "Your detailed prompt here" \ --output /tmp/result.md ``` ### Picking a model — quick rules - **Default code/writing workhorse:** `gemini-3.5-flash` — 20 RPD per project, good thinking depth. - **Batch + structured extraction:** `gemma-4-31b` (unlimited TPM, 1500 RPD). Cheap to stuff giant prompts in. - **Research with citations:** `gemini-2.5-flash` + `--enable-tools` (Search grounding is paid on Gemini 3.x — 2.5 is the free path). - **High volume / small tasks:** `gemini-3.1-flash-lite` — 500 RPD is the best free quota. - **Paid models** (Pro / image / video / agentic): not callable from a free-tier key. Use a paid Google Cloud key if you need them. - Full quota numbers and 429 diagnosis: [references/quotas.md](references/quotas.md). ### Useful flags | Flag | Purpose | Example | |------|---------|---------| | `--task-file path` | Read the prompt from a file (better than inline `--task` for multi-line) | `--task-file /tmp/task.md` | | `--quality draft\|standard\|production\|research` | Thinking depth | `--quality production` | | `--context-file path` | Feed additional text context. Repeat for multiple files. | `--context-file a.md --context-file b.md` | | `--context-glob 'pattern'` | Glob-expanded context files (repeatable) | `--context-glob 'docs/**/*.md'` | | `--image-file path` | Image understanding input | `--image-file screenshot.png` | | `--image-output path` | Image generation output (paid) | `--image-output /tmp/logo.png` | | `--enable-tools` | Enable Search + code execution | Research tasks, data analysis | | `--json` | Output result as structured JSON | Machine-parseable | | `--capture-thinking` | Return model's reasoning trace | Debugging | ## Stack-aware delegation Before writing the task prompt, think about what context Gemini will need — Gemini is stateless and has no access to your project, conventions, or domain-specific rules. 1. **Identify which existing rule files apply.** If you have project-level instructions for your orchestrator (e.g. `CLAUDE.md`, `GEMINI.md`, `AGENTS.md`), gather the relevant excerpts. 2. **Identify which existing skills encode the relevant patterns.** For example, React/Next.js skills, Postgres skills, brand-voice skills. If you have skill `SKILL.md` files locally, include them. 3. **Pass everything via repeated `--context-file`** (or `--context-glob`). The agent aggregates them into a single context block with `## Context: ` headers: ```bash python3 -m gemini_key_pool.gemini_agent \ --task-file /tmp/my-task.md \ --context-file path/to/CLAUDE.md \ --context-file path/to/relevant-skill/SKILL.md \ --context-file path/to/code-file.tsx \ --output /tmp/out.md ``` 4. **Write a sharp task prompt** that points to the context and names the desired output. **Don't curate "the relevant subset" of skill content.** Gemini's TPM is generous within a single request (250K on flash models, unlimited on Gemma). Dump the relevant context in full; spend effort on making the **task prompt** sharp instead. ## Shell escaping when prompts get long Inlining a multi-paragraph `--task` string into bash is fragile (quote breakage, backtick interpolation, dollar-sign expansion). Prefer: ```bash python3 -m gemini_key_pool.gemini_agent \ --task-file /tmp/my-task.md \ --context-file /tmp/ctx.md \ --output /tmp/out.md ``` If you're piping or composing on the fly, this also works: ```bash python3 -m gemini_key_pool.gemini_agent \ --task "$(cat /tmp/my-task.md)" --context-file /tmp/ctx.md --output /tmp/out.md ``` ## Crafting delegation prompts Gemini agents have no conversation context — everything they need must be in the prompt or context file. Delegation-specific things to remember: - **Self-contained.** No references to "the file I just read" or "as we discussed." Gemini hasn't read or discussed anything. - **Name the output format explicitly.** Your orchestrator is going to parse the result, so spell out the structure: fenced code blocks with `// FILE:` markers, strict JSON shape, markdown with H2/H3 sections, etc. - **State anti-patterns + project quirks explicitly.** Gemini doesn't know them. "Use shadcn/ui primitives in `src/components/ui/`, do not roll a new component" beats "follow project conventions." - **Watch shell escapes.** Use `--task-file` for anything over a few lines. ## Sensitive paths are filtered `--context-file` and `--context-glob` paths are filtered against a built-in deny-list of substrings (`.env`, `.ssh`, `.aws`, `credentials`, `secrets`, `id_rsa`, `id_ed25519`, `private_key`, `service-account`, etc.). A matching path is refused with a `🛡️` log line. This is a guardrail against accidentally sending a `.env` to Gemini, not a substitute for thinking about what you pass in. ## Validating Gemini output Non-negotiable. Gemini agents are fast but can hallucinate — especially on: - File paths and directory structures (may invent paths) - Feature descriptions (may describe features the product doesn't have) - Code details (verify against actual source) - **CLI flags** (may invent plausible-looking argparse flags that don't exist — grep the source to confirm before relying on them) Always read the output file and cross-check key claims before presenting to the user. For code suggestions, verify they compile / run. For factual claims, spot-check against source material. ## Parallel dispatch Launch multiple agents in parallel by running them as background processes. **Throughput ceiling** = (number of GCP projects in your pool) × (per-model RPM). With a single project's 8 keys, peak is the per-model RPM (5 or 15 depending on the model); with N projects, multiply by N. ## Orchestration patterns The basics: - **Parallel-Aggregate**: dispatch 3–5 agents on the same input from different angles (security / performance / quality), synthesize their reports yourself. - **Council of Experts**: a specialized Parallel-Aggregate where each agent adopts a domain-expert persona (UX, SEO, accessibility, etc.). - **Sequential Pipeline**: stages where each step's output feeds the next (research → analyze → write). - **Batch Processing**: same operation across many items; use Gemma 4 for the unlimited TPM. For advanced patterns (Hierarchical, Double-Diamond, Handoff Chain, Structured Debate), see [references/patterns.md](references/patterns.md). ## Key audit (June 19, 2026 deadline) **Unrestricted keys stop working June 19, 2026.** Every key in `.env` must be restricted to "Generative Language API" in AI Studio before then. The package ships an audit tool: ```bash python3 -m gemini_key_pool.audit_keys ``` Run weekly. Writes a report to `logs/key-audit-YYYY-MM-DD.md` with per-project status and a checklist for keys that need action. ## What Gemini agents cannot do - Access your conversation history (everything must be in the prompt) - Interact with the user mid-task - Maintain state between separate calls - Use your orchestrator's tools (Read, Edit, etc.) — pass file contents via `--context-file` - Install custom Python packages (only ~25 pre-installed: numpy, pandas, scikit-learn, scipy, opencv, tensorflow, geopandas, matplotlib, ...) - Code execution sandbox has a 30-second timeout ## References | File | When to read | |------|-------------| | [references/quotas.md](references/quotas.md) | Hit a 429, picking a model under quota pressure, full RPM/TPM/RPD tables | | [references/cost-mechanics.md](references/cost-mechanics.md) | Tasks involve tools / function-calling; budgeting requests | | [references/patterns.md](references/patterns.md) | Need patterns beyond the basics (debate, double-diamond, handoff chain) | | [references/model-capabilities.md](references/model-capabilities.md) | Detailed model specs, fallback chains | | [references/claude-integration.md](references/claude-integration.md) | Using Claude Code subagents alongside Gemini delegation | | [references/key-configuration.md](references/key-configuration.md) | API key setup | --- > Source: [SlimeyD/gemini-delegator](https://github.com/SlimeyD/gemini-delegator) — distributed by [TomeVault](https://tomevault.io).