skillengine 0.2.1

A standalone skills execution engine for LLM agents

SkillEngine

A framework-agnostic skills execution engine for LLM agents

---

Build Claude Code-like experiences with automatic skill discovery, on-demand loading, tool execution, and multi-provider support.

pip install skillengine

Highlights

• Progressive Disclosure — Only skill names + descriptions in the system prompt; full content loaded on-demand via the skill tool
• Multi-Provider — OpenAI, Anthropic, Google, DeepSeek, MiniMax + any OpenAI-compatible endpoint
• Plugin Everything — Loaders, filters, runtimes, adapters, extensions — all swappable
• Production Ready — Sessions, context compaction, cost tracking, prompt caching, sandbox isolation, event hooks, hot-reload

Quick Start

import asyncio
from pathlib import Path
from skillengine import create_agent

async def main():
    agent = await create_agent(
        skill_dirs=[Path("./skills")],
        system_prompt="You are a helpful assistant.",
    )

    response = await agent.chat("Help me create a PDF report")
    print(response.content)

    # Slash commands invoke skills directly
    response = await agent.chat("/pdf extract text from invoice.pdf")
    print(response.content)

asyncio.run(main())

Installation

pip install skillengine                # Core
pip install skillengine[openai]        # + OpenAI adapter
pip install skillengine[anthropic]     # + Anthropic adapter
pip install skillengine[openai,anthropic,websockets,memory,web,sandbox]  # Everything

Architecture

                        ┌─────────────────────────────┐
                        │         AgentRunner          │
                        │  on-demand skills · slash    │
                        │  commands · fork isolation   │
                        │  per-skill model & tools     │
                        └──────────┬──────────────────┘
                                   │
                        ┌──────────▼──────────────────┐
                        │        SkillsEngine          │
                        │                              │
                        │  Loader → Filter → Runtime   │
                        │         ↓                    │
                        │    SkillSnapshot             │
                        └──────────┬──────────────────┘
                                   │
          ┌────────┬───────┬───────┼───────┬──────────┐
          │        │       │       │       │          │
       OpenAI  Anthropic Google DeepSeek MiniMax   Custom

Layer	Base Class	Built-in	Purpose
Loader	SkillLoader	MarkdownSkillLoader	Parse SKILL.md (Markdown + YAML frontmatter)
Filter	SkillFilter	DefaultSkillFilter	Eligibility: bins, env vars, OS, config
Runtime	SkillRuntime	BashRuntime CodeModeRuntime BoxLiteRuntime	Execute with timeout, streaming, sandboxing
Adapter	LLMAdapter	OpenAIAdapter AnthropicAdapter	Provider integration with tool calling

Defining Skills

Create skills/my-skill/SKILL.md:

---
name: my-skill
description: "Does useful things"
model: claude-sonnet-4-20250514   # Per-skill model override
context: fork                      # Isolated subagent
allowed-tools: [Read, Grep, Glob]  # Tool restrictions
user-invocable: true               # Enable /my-skill slash command
argument-hint: "<query>"

metadata:
  emoji: "🔧"
  primary_env: "API_KEY"
  requires:
    bins: ["git"]              # ALL must exist
    any_bins: ["npm", "pnpm"]  # At least ONE
    env: ["GITHUB_TOKEN"]
    os: ["darwin", "linux"]
---

# My Skill

Instructions for the LLM when this skill is loaded.

Process: $ARGUMENTS
Current branch: !`git branch --show-current`

Placeholder	Replaced with
$ARGUMENTS	Full argument string
$1 $2 ... $N	Positional arguments
${CLAUDE_SESSION_ID}	Session ID
!`cmd`	Command stdout (before sending to LLM)

Core API

AgentRunner (high-level)

from skillengine import AgentRunner, AgentConfig, create_agent

config = AgentConfig(
    model="gpt-4o",
    api_key="...",
    max_turns=20,
    thinking_level="medium",        # off / minimal / low / medium / high / xhigh
    skill_description_budget=16000, # Max chars for skill metadata in system prompt
)
agent = AgentRunner(engine, config)

response = await agent.chat("Hello")                        # Single turn
async for chunk in agent.chat_stream("Explain this"):       # Streaming
    print(chunk, end="")
await agent.run_interactive()                                # REPL

SkillsEngine (low-level)

from skillengine import SkillsEngine, SkillsConfig

engine = SkillsEngine(config=SkillsConfig(
    skill_dirs=[Path("./skills")],
    watch=True,
    prompt_format="xml",  # xml / markdown / json
))

snapshot = engine.get_snapshot()
print(snapshot.prompt)                           # Inject into your system prompt
result = await engine.execute("echo hello")      # Execute a command

Key Features

Model Registry & Cost Tracking

Built-in catalog for 12+ models across 5 providers with pricing, context windows, and capabilities.

from skillengine import ModelRegistry, TokenUsage

registry = ModelRegistry()
model = registry.get("gpt-4o")
# model.context_window → 128000
# model.cost.input → 2.5 ($/M tokens)

cost = registry.calculate_cost("gpt-4o", TokenUsage(input_tokens=1000, output_tokens=500))

Extended Thinking

Maps thinking budgets across providers transparently.

AgentConfig(model="claude-opus-4-20250514", thinking_level="high")

Level	Anthropic budget	OpenAI effort
minimal	1,024	low
low	2,048	low
medium	4,096	medium
high	8,192	high
xhigh	16,384	high

Events

13 lifecycle events for observability and control flow.

from skillengine import EventBus, BEFORE_TOOL_CALL

bus = EventBus()

@bus.on(BEFORE_TOOL_CALL)
async def guard(event):
    if "rm -rf" in event.arguments.get("command", ""):
        return ToolCallEventResult(block=True, message="Blocked")

Events: AGENT_START AGENT_END TURN_START TURN_END BEFORE_TOOL_CALL AFTER_TOOL_RESULT INPUT CONTEXT_TRANSFORM TOOL_EXECUTION_UPDATE SESSION_START SESSION_END MODEL_CHANGE COMPACTION

Context Compaction

from skillengine import TokenBudgetCompactor, SlidingWindowCompactor

compactor = TokenBudgetCompactor(max_tokens=100_000)  # Token budget
compactor = SlidingWindowCompactor(window_size=50)     # Keep N recent messages

Sessions

JSONL append-only tree with branching.

from skillengine.session import SessionManager

mgr = SessionManager(store_dir=Path("./sessions"))
session = mgr.create_session()
branch = mgr.branch(session.id, entry_index=5)  # Fork from earlier point

CodeModeRuntime

Cloudflare code-mode-mcp pattern: 2 tools (search + execute) instead of N. Token cost O(1).

from skillengine import CodeModeRuntime

runtime = CodeModeRuntime(spec=openapi_spec, ctx={"client": httpx.Client()})
await runtime.search("[p for p in spec['paths'] if '/users' in p]")
await runtime.run("result = ctx['client'].get('/users')")

Sandbox Execution

VM-level isolation via BoxLite micro-VMs.

from skillengine import BoxLiteRuntime, SecurityLevel, SandboxedAgentRunner

runtime = BoxLiteRuntime(security_level=SecurityLevel.STANDARD)
agent = SandboxedAgentRunner(engine, config)  # Full agent in sandbox

Built-in Tools

Tool	Purpose	Tool	Purpose
bash	Shell commands	read	Read files
write	Write files	edit	Text replacement
grep	Pattern search	find	File discovery
ls	Directory listing	skill	On-demand skill loading

Extensions

# ~/.skillengine/extensions/my_ext.py
def extension(api):
    api.on("turn_end", my_hook)
    api.register_command("/my-cmd", handler, description="...")
    api.register_tool("my_tool", schema, handler)
    api.register_adapter("my-provider", MyAdapter)

Execution Modes

Mode	Protocol	Use case
Interactive	TUI (stdin/stdout)	Terminal REPL
JSON	JSONL to stdout	Pipelines
RPC	JSON-line stdin/stdout	Programmatic control

CLI

skills list -d ./skills           # List skills
skills show pdf -d ./skills       # Show detail
skills prompt -d ./skills -f xml  # Generate prompt
skills exec "echo hello"          # Run command
skills validate -d ./skills       # Validate
skills watch -d ./skills          # Watch + hot-reload

Extending

Every layer is pluggable. Implement the base class and pass to the engine:

from skillengine.loaders import SkillLoader
from skillengine.filters import SkillFilter
from skillengine.runtime import SkillRuntime

class MyLoader(SkillLoader):       # Custom file format
    def can_load(self, path): ...
    def load_skill(self, path, source): ...

class MyFilter(SkillFilter):       # Custom eligibility
    def filter(self, skill, config, context): ...

class MyRuntime(SkillRuntime):     # Custom execution
    async def execute(self, command, cwd, env, timeout): ...

Development

git clone https://github.com/sawzhang/skillengine.git
cd skillengine
uv sync

uv run pytest                # 1083 tests
uv run ruff check src/       # Lint
uv run ruff format --check src/  # Format check
uv run mypy src/             # Type check

CI runs on every push and PR: lint + format + mypy on Python 3.12, tests on 3.10/3.11/3.12.

License

MIT