agentleFS
alinaqialinaqi/claude-bootstrap/skills/external-model-delegation/SKILL.md

external-model-delegation

Skill708 starsChanged 5 months agoMITView on GitHub

UserPromptSubmit hook pattern that classifies prompts into six cost tiers and delegates to external model CLIs

  • Reads credentials

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Write response to stdout, errors to stderr

    line 41

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

External Model Delegation Pattern

A UserPromptSubmit hook classifies every user prompt into one of six cost/performance tiers. The hook injects additionalContext instructing Claude to run a specific delegation script and return the output.

Tier routing table

TierDelegation commandCost
QWENqwen3 "prompt"$0 (local Ollama)
DEEPSEEK_FLASHdeepseek --flash "prompt"$0.14 / $0.28 per M tokens
DEEPSEEK_PROdeepseek --pro "prompt"$0.44 / $0.87 per M tokens
KIMIkimi --quiet -p "prompt"$0.60 / $2.50 per M tokens
CODEXcodex execvaries
CLAUDEhandle natively$3-5 / $15-25 per M tokens

Delegation script pattern

Each script is a self-contained executable in ~/bin/ that accepts a prompt and writes the response to stdout:

~/bin/
├── qwen3      # Shell: curl to local Ollama API
├── kimi       # Shell: execs Kimi CLI binary
├── deepseek   # Python: httpx to DeepSeek Anthropic-compat API
└── route-task # Shell + qwen3: classifies prompt into tier

Script contract

  1. Accept prompt as first argument: qwen3 "what is 2+2"
  2. Support --flash / --pro model flags (deepseek)
  3. Support --quiet mode flag (kimi)
  4. Write response to stdout, errors to stderr
  5. Exit 0 on success, non-zero on error

Writing a new delegation script

#!/bin/bash
# Minimal delegator template
PROMPT="$1"
API_KEY="${EXTERNAL_API_KEY:-}"
# Call external API, write result to stdout
curl -s https://api.example.com/chat \
  -H "Authorization: Bearer $API_KEY" \
  -d "$(jq -n --arg p "$PROMPT" '{prompt: $p}')" \
  | jq -r '.response'

Routing hook flow

User types prompt
    ↓
UserPromptSubmit hook fires
    ↓
qwen3 classifies into tier (QWEN|DEEPSEEK_FLASH|DEEPSEEK_PRO|KIMI|CODEX|CLAUDE)
    ↓
Hook injects additionalContext: "Run: <delegation-command>"
    ↓
Claude reads context, spawns delegation script, returns output
    ↓
User sees response from the delegated model

Classification tiers

TierTask types
QWENgrep, find, regex, shell, syntax lookups, log reading, short summaries
DEEPSEEK_FLASHSimple code, boilerplate, CRUD, test writing, small fixes, config
DEEPSEEK_PROMulti-file features, refactors, debugging, medium coding, docs
KIMISingle-file review, medium reasoning, commit messages, diff summaries
CODEXBulk generation, mechanical changes across many files
CLAUDEArchitecture, security, complex debugging, system design, quality-critical

Environment

# Required env vars (set in ~/.zshrc)
export DEEPSEEK_API_KEY="sk-..."      # For deepseek delegator
export OPENAI_API_KEY="sk-..."         # For codex CLI
# Ollama must be running locally for qwen3 classification + delegation

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.