SKILLS.WTF — INTELLIGENCE LAYER

The Intelligence Layer for All Agents

Centralized GraphRAG Skills Registry + Intelligent Routing. Give your agents the right capabilities at the right millisecond — automatically.

skills.wtf — because your agents need to know wtf skill to use

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Flat SKILL.md files are breaking production agents

Your agents are guessing. Your context windows are overflowing. Your tool registry is a mess of unstructured markdown that no runtime can reliably parse.

Context Window Overload

Every agent call drags the entire skill catalog into the prompt. Token counts explode. Latency spikes. Costs multiply.

TOKEN_USAGE MAX: 128,000

[░░░░░░░░░░░░░░░░░░░░] 0%

0 tokens

Agents Guess Which Tool to Use

Without structured capability graphs, agents randomly pick tools. Wrong function calls. Infinite retry loops. Runtime exceptions in production.

> agent.execute("deploy hotfix")
ERROR: Tool 'deploy_hotfix' not found
> agent.retry()
ERROR: Context exceeded
> agent.fallback()
ERROR: Hallucinated tool name
_

No Single Source of Truth

SkillsliveinGitHubrepos,documentation,andtribalknowledge.Nosinglesourceoftruth.Updatespropagateunevenly.Agentbehaviordrifts.

These problems exist whether you're building coding agents or autonomous business agents.

One unified API. Intelligent retrieval. Production-grade routing.

From natural language intent to executed skill in under 12 milliseconds. Here's how the engine works.

01.

Intent

The client agent issues a natural language intent string. Our engine parses intent, extracts entities, and classifies the request domain.

02.

GraphRAG Retrieval

The engine dives into the 3D network topology. Irrelevant nodes fade to zero. Matching tool nodes illuminate with violet energy. The graph prunes itself in real time.

03.

Intelligent Routing

The viewport transitions into a holographic routing grid. Latency, permissions, and cost variables calculate in real time. The router commits to the optimal native MCP server connection.

04.

Self-Optimizing Loop

The skill executes successfully. An inverse energy wave travels back through the network, dynamically updating edge weights and logging performance metrics for future optimization.

01.

Intent

The client agent issues a natural language intent string. Our engine parses intent, extracts entities, and classifies the request domain.

02.

GraphRAG Retrieval

The engine dives into the 3D network topology. Irrelevant nodes fade to zero. Matching tool nodes illuminate with violet energy. The graph prunes itself in real time.

03.

Intelligent Routing

The viewport transitions into a holographic routing grid. Latency, permissions, and cost variables calculate in real time. The router commits to the optimal native MCP server connection.

04.

Self-Optimizing Loop

The skill executes successfully. An inverse energy wave travels back through the network, dynamically updating edge weights and logging performance metrics for future optimization.

Everything your agents need. Nothing they don't.

A modular intelligence layer that adapts to any agent architecture.

Built for all agents — from coding agents in Cursor and Claude Code to autonomous operational agents.

Hybrid GraphRAG Memory Space

Vector + graph hybrid retrieval. Skills are stored as dense embeddings AND connected knowledge graph nodes. Retrieval accuracy exceeds 99% even with 10,000+ skills.

query: 'deploy docker container'

Intelligent MCP Router Engine

Real-time latency optimization with permission-aware routing. Selects the optimal MCP server based on cost, latency, and capability match.

Unified Minimalist Gateway API

One endpoint. Infinite capabilities. POST /v1/execute with your intent. The engine handles the rest.

// POST /v1/execute
{
  "intent": "deploy hotfix to production",
  "context": { "service": "api-gateway" },
  "constraints": {
    "max_latency_ms": 100,
    "required_permissions": ["deploy"]
  }
}

Production Governance & Tracing

Full audit trails. Descending latency graphs. Climbing accuracy metrics. Complete observability.

Latency (ms) 3ms

Accuracy (%) 99.2%

Try the Engine. Live.

Watch how skills.wtf routes a real request through the entire pipeline.

Request Presets

Select a preset and click Run Simulation

Why skills.wtf

What you get. What you avoid.

Seven concrete problems skills.wtf solves — with metrics that matter.

The Problem	How skills.wtf Solves It	Impact
✗ Every agent call loads the entire skill catalog into the prompt	✓ GraphRAG pruning keeps context under 1.2k tokens regardless of registry size	Up to 8× fewer tokens, 3× lower latency
Instead of dumping all SKILL.md files into the prompt, skills.wtf builds a dense vector graph and retrieves only the relevant 3-5 skills per query. Token count stays flat even with 10,000+ registered capabilities.
✗ Agents randomly pick tools and hallucinate function names	✓ Structured capability graph with semantic similarity scoring	99%+ routing accuracy, zero hallucinated calls
Each skill is a typed node in a knowledge graph with explicit input/output schemas. The engine scores intent-to-skill match using vector + graph traversal, never guesses.
✗ Skills live in GitHub repos, docs, and tribal knowledge — no single source of truth	✓ Centralized Postgres-native registry with version pinning and audit trails	One registry. One API. One source of truth.
All skills, MCP servers, and harnesses are registered in a single schema-driven Postgres database. Updates propagate atomically. Rollback any version in under 100ms.
✗ MCP server selection is manual — no latency or cost awareness	✓ Intelligent MCP router with real-time latency optimization + permission checks	<12ms p99 routing, automatic failover
The router maintains a live health matrix of all MCP servers. It picks the optimal server based on latency, cost, permissions, and load — and fails over instantly if a server drops.
✗ No feedback loops — agent performance degrades over time silently	✓ Real-time edge weight updates from every execution	Self-improving over time
Every successful execution strengthens the route edge. Every failure triggers re-evaluation. The graph learns which skills work best for which intents and auto-ranks them.
✗ Integrating a new skill requires code changes in every agent	✓ Single /v1/execute endpoint — register once, use everywhere	New skill live in 30 seconds, zero agent redeploys
Register a skill via the API or CLI. Every agent in your fleet instantly gains access through the same /v1/execute endpoint. No code changes, no redeploys, no drift.
✗ No observability into which skills are used, how often, or their cost	✓ Built-in tracing, cost tracking, and evaluation metrics	Full audit trail per skill, per agent, per user
Every skill call is traced with latency, cost, input/output, and success status. Dashboard shows top skills, expensive routes, and accuracy trends in real time.

✗

The Problem

Every agent call loads the entire skill catalog into the prompt

✓

The Solution

GraphRAG pruning keeps context under 1.2k tokens regardless of registry size

Impact: Up to 8× fewer tokens, 3× lower latency

Instead of dumping all SKILL.md files into the prompt, skills.wtf builds a dense vector graph and retrieves only the relevant 3-5 skills per query. Token count stays flat even with 10,000+ registered capabilities.

✗

The Problem

Agents randomly pick tools and hallucinate function names

✓

The Solution

Structured capability graph with semantic similarity scoring

Impact: 99%+ routing accuracy, zero hallucinated calls

Each skill is a typed node in a knowledge graph with explicit input/output schemas. The engine scores intent-to-skill match using vector + graph traversal, never guesses.

✗

The Problem

Skills live in GitHub repos, docs, and tribal knowledge — no single source of truth

✓

The Solution

Centralized Postgres-native registry with version pinning and audit trails

Impact: One registry. One API. One source of truth.

All skills, MCP servers, and harnesses are registered in a single schema-driven Postgres database. Updates propagate atomically. Rollback any version in under 100ms.

✗

The Problem

MCP server selection is manual — no latency or cost awareness

✓

The Solution

Intelligent MCP router with real-time latency optimization + permission checks

Impact: <12ms p99 routing, automatic failover

The router maintains a live health matrix of all MCP servers. It picks the optimal server based on latency, cost, permissions, and load — and fails over instantly if a server drops.

✗

The Problem

No feedback loops — agent performance degrades over time silently

✓

The Solution

Real-time edge weight updates from every execution

Impact: Self-improving over time

Every successful execution strengthens the route edge. Every failure triggers re-evaluation. The graph learns which skills work best for which intents and auto-ranks them.

✗

The Problem

Integrating a new skill requires code changes in every agent

✓

The Solution

Single /v1/execute endpoint — register once, use everywhere

Impact: New skill live in 30 seconds, zero agent redeploys

Register a skill via the API or CLI. Every agent in your fleet instantly gains access through the same /v1/execute endpoint. No code changes, no redeploys, no drift.

✗

The Problem

No observability into which skills are used, how often, or their cost

✓

The Solution

Built-in tracing, cost tracking, and evaluation metrics

Impact: Full audit trail per skill, per agent, per user

Every skill call is traced with latency, cost, input/output, and success status. Dashboard shows top skills, expensive routes, and accuracy trends in real time.

Segments

Built for teams that ship.

Who needs skills.wtf?

AI Platform Teams

Centralized policies, governance, and access token management. One registry to rule every agent in your fleet. Works for both coding agents and autonomous agents across your organization.

RBAC per skill
Audit logging
Version pinning

Enterprise AI Engineering

Strict latency SLAs, token economics, and security bounds. Enterprise-grade reliability without enterprise-grade bloat.

<12ms p99 latency
Token cost optimization
SOC-2 compliant routing

SaaS Product Companies

Ship new agent capabilities instantly without refactoring code. Your product team defines intents, the engine handles the rest.

No-code skill registration
A/B test routing strategies
Instant feature flags

Elite Studios & Agencies

Ultra-fast system deployment and resilient architectures. Build custom agent experiences for clients in days, not months.

White-label routing
Multi-tenant isolation
Custom skill DSL

Stop making your agents guess.

Get early access to the intelligence layer your agents deserve.