How do I install the Deepseek Cost Router skill?

Run npx skillmds@latest add zhangliang0115/deepseek-cost-router in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Deepseek Cost Router safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Deepseek Cost Router?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Deepseek Cost Router free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Deepseek Cost Router?

zhangliang0115 (@zhangliang0115) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Deepseek Cost Router by zhangliang0115

Name: Deepseek Cost Router
Author: zhangliang0115

Deepseek Cost Router

Route coding tasks between DeepSeek chat and reasoning models (deepseek-chat vs deepseek-reasoner) to minimize API cost while keeping quality. Use when configuring an agent to use DeepSeek, choosing a model for a task batch, or optimizing token spend.

File contents

Routing work to the right DeepSeek model

DeepSeek exposes two behaviors through its OpenAI-compatible API:

deepseek-chat — the fast, non-thinking path. Cheap, low latency.
deepseek-reasoner — the thinking path (chain-of-thought before the final answer). Slower and pricier per call, dramatically better on hard reasoning.

The routing skill: run routine volume on chat, escalate only reasoning-heavy work.

Escalation heuristics

Default to chat for: file edits driven by clear instructions, renames, boilerplate, doc updates, formatted output, summarization, simple lookups, bulk repetitive transformations.

Escalate to reasoner for: non-obvious bugs (state/caching/async/race), algorithm design, architecture tradeoffs, tricky merges, math, contract or spec violations, "the tests fail and the cause is unclear".

A cheap escalation ladder for agent harnesses that support it:

Try the task on chat with a tight instruction.
If the result fails review/tests, re-run just that unit on reasoner.
Feed the reasoner's answer back as the accepted fix; continue on chat.

Escalating units of failure instead of whole sessions keeps the premium model's token share small.

Cost mechanics worth knowing

Context caching: repeated identical prefixes (system prompt, long file context) hit DeepSeek's automatic prefix cache and are billed at the much cheaper cache-hit rate. Keep your system prompt and repo map stable and first in the prompt; don't shuffle dynamic content in front of them.
Reasoner output is token-hungry: thinking tokens bill as output. A reasoner call can cost many multiples of a chat call — reserve it.
Batch offline work (code review of a branch, doc generation) into dedicated sessions where the shared context prefix is reused.

Wiring DeepSeek into coding agents

DeepSeek's API is OpenAI-compatible, and Anthropic-compatible endpoints are documented for harnesses like Claude Code — see the official integration hub: https://api-docs.deepseek.com/guides/coding_agents (per-tool guides for Claude Code, OpenCode, OpenClaw, Codex and more). You need:

DEEPSEEK_API_KEY from https://platform.deepseek.com
The base URL (https://api.deepseek.com) and model id set in your tool's provider config — follow the per-tool guide for exact env vars.

DeepSeek Harness (dsh) reads DEEPSEEK_API_KEY directly; if aipx doctor warns it's missing, export it before launching.

Verify

Spot-check 10 recent tasks: would reasoner have changed the outcome? If no, your routing is right.
Watch spend per model in the platform dashboard; a healthy setup keeps reasoner tokens a small fraction of total while review pass-rates hold.

---name: deepseek-cost-routerdescription: Route coding tasks between DeepSeek chat and reasoning models (deepseek-chat vs deepseek-reasoner) to minimize API cost while keeping quality. Use when configuring an agent to use DeepSeek, choosing a model for a task batch, or optimizing token spend.---# Routing work to the right DeepSeek modelDeepSeek exposes two behaviors through its OpenAI-compatible API:- **`deepseek-chat`** — the fast, non-thinking path. Cheap, low latency.- **`deepseek-reasoner`** — the thinking path (chain-of-thought before the final answer). Slower and pricier per call, dramatically better on hard reasoning.The routing skill: run routine volume on chat, escalate only reasoning-heavywork.## Escalation heuristicsDefault to **chat** for: file edits driven by clear instructions, renames,boilerplate, doc updates, formatted output, summarization, simple lookups,bulk repetitive transformations.Escalate to **reasoner** for: non-obvious bugs (state/caching/async/race),algorithm design, architecture tradeoffs, tricky merges, math, contract orspec violations, "the tests fail and the cause is unclear".A cheap escalation ladder for agent harnesses that support it:1. Try the task on chat with a tight instruction.2. If the result fails review/tests, re-run just that unit on reasoner.3. Feed the reasoner's answer back as the accepted fix; continue on chat.Escalating *units of failure* instead of whole sessions keeps the premiummodel's token share small.## Cost mechanics worth knowing- **Context caching**: repeated identical prefixes (system prompt, long file context) hit DeepSeek's automatic prefix cache and are billed at the much cheaper cache-hit rate. Keep your system prompt and repo map *stable and first* in the prompt; don't shuffle dynamic content in front of them.- **Reasoner output is token-hungry**: thinking tokens bill as output. A reasoner call can cost many multiples of a chat call — reserve it.- **Batch offline work** (code review of a branch, doc generation) into dedicated sessions where the shared context prefix is reused.## Wiring DeepSeek into coding agentsDeepSeek's API is OpenAI-compatible, and Anthropic-compatible endpoints aredocumented for harnesses like Claude Code — see the official integration hub:https://api-docs.deepseek.com/guides/coding_agents (per-tool guides forClaude Code, OpenCode, OpenClaw, Codex and more). You need:- `DEEPSEEK_API_KEY` from https://platform.deepseek.com- The base URL (`https://api.deepseek.com`) and model id set in your tool's provider config — follow the per-tool guide for exact env vars.DeepSeek Harness (`dsh`) reads `DEEPSEEK_API_KEY` directly; if aipx doctorwarns it's missing, export it before launching.## Verify- Spot-check 10 recent tasks: would reasoner have changed the outcome? If no, your routing is right.- Watch spend per model in the platform dashboard; a healthy setup keeps reasoner tokens a small fraction of total while review pass-rates hold.

Frequently asked questions