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clawrouter

Hosted-gateway LLM router — save 84% on inference costs. A local proxy that forwards each request to the blockrun.ai gateway, which routes to the cheapest capable model across 90 models from OpenAI, Anthropic, Google, DeepSeek, xAI, Z.AI, and more. Free open-weight 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) as built-in agent tools. Not a local-inference tool — prompts are sent to the blockrun.ai gateway.

Quality

68%

Does it follow best practices?

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SecuritybySnyk

Medium

Suggest reviewing before use

Fix and improve this skill with Tessl

tessl review fix ./skills/clawrouter/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a well-organized, highly actionable reference with concrete commands, honest security/privacy disclosure, and strong guardrails on the real-money tools. Its main weaknesses are the inline bulk content that belongs in reference files (the model catalog, full pricing taxonomy) and time-sensitive details that will rot, plus no bundle structure to progressively disclose into.

Suggestions

Move the 90-model catalog into a references/ file (e.g. references/models.md), keeping only the per-tier curated primaries inline — it is the single largest block of content and churns with every catalog update.

Move or remove time-sensitive details (Seedance token rates, the 'since 2026-06-01' note, 'Router Core V3.4') into a reference doc so the main body stays stable; per the judging guidelines, time-sensitive inline content penalizes conciseness.

Fix the routing-profiles link: 'docs/routing-profiles.md' is referenced with a '../../docs/' relative path that escapes the skill bundle — either ship it under references/ within the skill or link the absolute GitHub URL as the other references do.

DimensionReasoningScore

Conciseness

Sections like Data Flow, Credentials & Local Key Storage, Install, and Setup are tight and high-signal, but the ~60-model inline catalog and time-sensitive minutiae ('~20,256 tokens/sec at the 720p+audio default — 2× the prior 480p rate', 'since 2026-06-01', 'Router Core V3.4') are padding that churns and belongs in a reference file. Mostly efficient but should be tightened — not a 2 because most sections earn their tokens.

3 / 5

Actionability

Concrete, executable commands throughout: 'openclaw plugins install @blockrun/clawrouter', 'openclaw models set blockrun/auto', the curl commands for the live model catalog, '/cr-call +1<E.164> "<task>"', 'clawrouter phone numbers list/buy/renew/release', and the 'npm pack @blockrun/clawrouter@<version>' verification, plus concrete pricing formulas and tool parameters. Minor gap: no example of an actual LLM call made through the localhost proxy, so it stops short of fully covering the most common case.

4 / 5

Workflow Clarity

Install → setup → routing is unambiguous, and the real-money operations carry explicit validation checkpoints: Polymarket requires 'confirm:true' to place/sign anything (omit → dry-run preview) with 'POLYMARKET_MAX_BET_USD' caps, voice calls are flagged as real-world side effects with 'only call blockrun_voice_call when the user has explicitly asked', and the voice polling loop (poll every 10–30s) is stated. Not a 5: some flows (e.g. first wallet funding, x402 setup) defer to other skills without an inline checkpoint summary.

4 / 5

Progressive Disclosure

Section headers are clear and references are signaled (links to the phone, predexon, and polymarket-trading skills and to GitHub files), but no bundle files exist — the 90-model catalog and the full 8-mode billing table are inlined content that clearly belongs in separate reference files. The 'docs/routing-profiles.md' link uses a '../../docs/' path that points outside the skill bundle, and the referenced sibling skills are not part of this bundle.

3 / 5

Total

14

/

20

Passed

Description

62%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description clearly communicates what the tool does with a dense enumeration of concrete capabilities, but it is overlong and padded, reads partly as marketing ('save 84% on inference costs'), and lacks an explicit 'Use when...' clause. Its multi-domain breadth raises conflict risk with dedicated market-data, phone, and prediction-market skills.

Suggestions

Add an explicit 'Use when...' clause to the description itself (e.g. 'Use when routing LLM requests to cheaper models or when the user mentions LLM costs, OpenRouter alternatives, or blockrun') — its absence currently caps completeness at 3.

Trim the long capability enumeration and drop the unsubstantiated 'save 84%' claim or qualify its basis; concise and clear beats long and padded, and the multi-domain sprawl (stocks, crypto, phone, prediction markets) raises conflict risk with dedicated domain skills.

Fold a few of the natural trigger variations from the triggers field ('openrouter alternative', 'free nvidia models', 'ai phone call') into the description text so keyword coverage is comprehensive within the description itself.

DimensionReasoningScore

Specificity

The description lists many concrete capabilities ('forwards each request to the blockrun.ai gateway, which routes to the cheapest capable model', 'realtime market data (global stocks, crypto, FX, commodities)', 'SIM-swap fraud detection', 'AI-powered outbound voice calls (Twilio + Bland.ai)'), but it is padded and includes the over-claim 'save 84% on inference costs'. Not a 5: verbosity is penalized and minor coverage gaps exist (e.g. 'Twitter/X intelligence' is claimed but not substantiated in the skill body).

4 / 5

Completeness

The 'what' is clearly answered ('a local proxy that forwards each request to the blockrun.ai gateway, which routes to the cheapest capable model'), but there is no 'Use when...' clause or equivalent explicit trigger guidance inside the description — the boundary note 'Not a local-inference tool — prompts are sent to the blockrun.ai gateway' only weakly implies when to use it, capping completeness at 3 per the judging guidelines.

3 / 5

Trigger Term Quality

Natural domain terms are present ('LLM router', 'cheapest capable model', 'free open-weight models', 'prediction-market', 'SIM-swap fraud detection', 'outbound voice calls'), giving good keyword coverage. Not a 5: common variations users would actually say (e.g. 'OpenRouter alternative', 'cheap LLM API') appear only in the separate triggers field, not in the description itself.

4 / 5

Distinctiveness Conflict Risk

The blockrun.ai gateway niche is distinct, but the description sprawls across stocks, crypto, FX, commodities, Twitter/X intelligence, prediction markets, phone-number verification, SIM-swap detection, and voice calls — each a domain where a dedicated skill would create real trigger overlap. It fits 'somewhat specific but could still overlap with similar skills' better than the minor-overlap anchor.

3 / 5

Total

14

/

20

Passed

Validation

75%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 12 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 1 suspicious

Warning

Total

12

/

16

Passed

Repository

BlockRunAI/ClawRouter

Path

skills/clawrouter/SKILL.md

Reviewed

Table of Contents

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