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VickyXAI merged 1 commit into from
Jul 27, 2026
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fix: the skill shipped to agents was the stalest surface of all #224

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -657,7 +657,7 @@ You're here. <!-- br:models.chatVisible -->66<!-- /br:models.chatVisible --> mod

**BlockRun for Claude Code**

Run Claude Code with 50+ models, no rate limits, no Anthropic account, no phone verification. Pay per request with USDC — your wallet is your identity.
Run Claude Code with <!-- br:models.chatVisible -->66<!-- /br:models.chatVisible --> models, no rate limits, no Anthropic account, no phone verification. Pay per request with USDC — your wallet is your identity.

`curl -fsSL https://blockrun.ai/brcc-install | bash`

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20 changes: 13 additions & 7 deletions skills/clawrouter/SKILL.md
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@@ -1,6 +1,6 @@
---
name: clawrouter
description: Hosted-gateway LLM router — save 67% on inference costs. A local proxy that forwards each request to the blockrun.ai gateway, which routes to the cheapest capable model across 55+ models from OpenAI, Anthropic, Google, DeepSeek, xAI, NVIDIA, and more. 8 free NVIDIA models included. Also exposes realtime market data (global stocks, crypto, FX, commodities), Twitter/X intelligence, prediction-market data across Polymarket, Kalshi, Limitless, Opinion, Predict.Fun, dFlow + UMA oracle resolution + wallet identity & clustering, phone-number intelligence (carrier + SIM-swap fraud detection) plus AI-powered outbound voice calls (Twilio + Bland.ai), AND the Surf unified crypto data API (84 endpoints — CEX/DEX, on-chain SQL over 80+ ClickHouse tables, 100M+ labeled wallets, prediction markets, social/CT mindshare, news, VC fund intel) as built-in agent tools. Not a local-inference tool — prompts are sent to the blockrun.ai gateway.
description: Hosted-gateway LLM router — save 87% on inference costs. A local proxy that forwards each request to the blockrun.ai gateway, which routes to the cheapest capable model across 66 models from OpenAI, Anthropic, Google, DeepSeek, xAI, NVIDIA, and more. 8 free NVIDIA models included. Also exposes realtime market data (global stocks, crypto, FX, commodities), Twitter/X intelligence, prediction-market data across Polymarket, Kalshi, Limitless, Opinion, Predict.Fun, dFlow + UMA oracle resolution + wallet identity & clustering, phone-number intelligence (carrier + SIM-swap fraud detection) plus AI-powered outbound voice calls (Twilio + Bland.ai), AND the Surf unified crypto data API (84 endpoints — CEX/DEX, on-chain SQL over 80+ ClickHouse tables, 100M+ labeled wallets, prediction markets, social/CT mindshare, news, VC fund intel) as built-in agent tools. Not a local-inference tool — prompts are sent to the blockrun.ai gateway.
triggers:
- "clawrouter"
- "claw router"
Expand Down Expand Up @@ -53,7 +53,7 @@ metadata:

# ClawRouter

Hosted-gateway LLM router that saves 67% on inference costs by forwarding each request to the blockrun.ai gateway, which picks the cheapest model capable of handling it across 55+ models from 9 providers (8 free NVIDIA models). All billing flows through one USDC wallet; you do not hold provider API keys.
Hosted-gateway LLM router that saves <!-- br:savings.autoVsBaselinePct -->87<!-- /br:savings.autoVsBaselinePct -->% on inference costs by forwarding each request to the blockrun.ai gateway, which picks the cheapest model capable of handling it across <!-- br:models.chatVisible -->66<!-- /br:models.chatVisible --> models from 9 providers (<!-- br:models.free -->8<!-- /br:models.free --> free NVIDIA models). All billing flows through one USDC wallet; you do not hold provider API keys.

**This is not a local-inference tool.** ClawRouter is a thin local proxy. Your prompts are sent over HTTPS to the blockrun.ai gateway for model execution. If your workload requires inference that never leaves your machine, use a local runtime like Ollama — ClawRouter is not the right tool for that use case.

Expand Down Expand Up @@ -126,16 +126,22 @@ openclaw models set openai/gpt-4o

ClawRouter classifies each request into one of four tiers:

- **SIMPLE** (40% of traffic) — factual lookups, greetings, translations → Gemini Flash ($0.60/M, 99% savings)
- **MEDIUM** (30%) — summaries, explanations, data extraction → DeepSeek Chat ($0.42/M, 99% savings)
- **COMPLEX** (20%) — code generation, multi-step analysis → Claude Opus ($75/M, best quality)
- **REASONING** (10%) — proofs, formal logic, multi-step math → o3 ($8/M, 89% savings)
- **SIMPLE** — factual lookups, greetings, translations → gemini-2.5-flash ($0.30/$2.50)
- **MEDIUM** — summaries, explanations, data extraction → kimi-k2.7 ($0.95/$4.00)
- **COMPLEX** — code generation, multi-step analysis → gemini-3.1-pro ($2/$12)
- **REASONING** — proofs, formal logic, multi-step math → grok-4-1-fast-reasoning ($0.20/$0.50)

Prices are per 1M input/output tokens, on the default `auto` profile. Per-tier
savings percentages are deliberately not quoted here: the published figure is
blended across a stated workload mix, and a per-tier number invites comparing
it against a baseline nobody wrote down. See
[savings-mix.json](https://github.com/BlockRunAI/blockrun/blob/main/src/brand/savings-mix.json).

Rules handle ~~80% of requests in <1ms. Only ambiguous queries hit the LLM classifier (~~$0.00003 per classification).

## Available Models

55+ models including: gpt-5.6-terra [balanced, stable default], gpt-5.6-sol [flagship], gpt-5.6-luna [cost-efficient], gpt-5.5, gpt-5.5-pro [max compute], chat-latest [ChatGPT Instant], gpt-5.4, gpt-4o, o3, claude-fable-5, claude-opus-5 [Anthropic flagship, 1M ctx], claude-opus-4.8, claude-opus-4.7, claude-opus-4.5, claude-sonnet-5, claude-sonnet-4.6, gemini-3.1-pro, gemini-2.5-flash, deepseek-v4-pro, deepseek-chat, grok-4.5, grok-4.3, grok-build-0.1, kimi-k3 [1M ctx flagship], kimi-k2.7, qwen3.7-max [Qwen flagship, 1M ctx], and 8 free NVIDIA models (mistral-large-3-675b [default, 675B flagship], deepseek-v4-flash [1M context], seed-oss-36b [coding], nemotron-3-nano-omni-30b-a3b-reasoning [vision], mistral-nemotron, step-3.7-flash, nemotron-nano-9b-v2, nemotron-nano-12b-v2-vl [vision]).
<!-- br:models.chatVisible -->66<!-- /br:models.chatVisible --> models including: gpt-5.6-terra [balanced, stable default], gpt-5.6-sol [flagship], gpt-5.6-luna [cost-efficient], gpt-5.5, gpt-5.5-pro [max compute], chat-latest [ChatGPT Instant], gpt-5.4, gpt-4o, o3, claude-fable-5, claude-opus-5 [Anthropic flagship, 1M ctx], claude-opus-4.8, claude-opus-4.7, claude-opus-4.5, claude-sonnet-5, claude-sonnet-4.6, gemini-3.1-pro, gemini-2.5-flash, deepseek-v4-pro, deepseek-chat, grok-4.5, grok-4.3, grok-build-0.1, kimi-k3 [1M ctx flagship], kimi-k2.7, qwen3.7-max [Qwen flagship, 1M ctx], and 8 free NVIDIA models (mistral-large-3-675b [default, 675B flagship], deepseek-v4-flash [1M context], seed-oss-36b [coding], nemotron-3-nano-omni-30b-a3b-reasoning [vision], mistral-nemotron, step-3.7-flash, nemotron-nano-9b-v2, nemotron-nano-12b-v2-vl [vision]).

## Built-in Agent Tools

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20 changes: 20 additions & 0 deletions src/router/brand-numbers.test.ts
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Expand Up @@ -135,3 +135,23 @@ describe("README tier table", () => {
}
}
});

describe("skill frontmatter", () => {
// A skill's YAML frontmatter is the description an agent reads when deciding
// whether to load it. A marker there is not inert — it becomes part of the
// string — so it holds literals, asserted here like package.json is.
const skill = readFileSync("skills/clawrouter/SKILL.md", "utf8");
const frontmatter = skill.slice(0, skill.indexOf("\n---", 4));
const n = JSON.parse(readFileSync("brand-numbers.json", "utf8")) as {
models: { chatVisible: number };
savings: { autoVsBaselinePct: number };
};

it("quotes the published savings figure", () => {
expect(frontmatter).toContain(`save ${n.savings.autoVsBaselinePct}% on inference costs`);
});

it("quotes the published model count", () => {
expect(frontmatter).toContain(`${n.models.chatVisible} models`);
Comment on lines +143 to +155

@coderabbitai coderabbitai Bot Jul 27, 2026

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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win

Validate the frontmatter boundary before asserting values.

If the closing \n--- delimiter is missing, indexOf returns -1, so slice(0, -1) searches almost the entire markdown file. Matching text in the body could then make this test pass without valid frontmatter.

Add an explicit delimiter assertion before slicing.

🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@src/router/brand-numbers.test.ts` around lines 143 - 155, Validate that the
closing "\n---" delimiter was found before slicing frontmatter in the shared
test setup. Add an explicit assertion on the delimiter index, then preserve the
existing frontmatter extraction and savings/model assertions.

});
});
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