The system prompt contains the skill catalog;
load_skillreturns the fullSKILL.md.Harness Layer: Knowledge loading — show the model which skills exist, then load one by name.
The Problem
Suppose a project has a React component specification, a SQL style guide, and an API design document. We want the Agent to follow these rules during development, so the most direct approach is to put all of them into the system prompt:
SYSTEM = (
f"You are a coding agent. "
+ open("docs/react-style.md").read()
+ open("docs/sql-style.md").read()
+ open("docs/api-design.md").read()
)
This approach lets the Agent read every specification, but it fixes all three documents in the system prompt instead of selecting only the one needed for the current task. Every LLM call sends the full text of all three documents to the model. When the task only changes React components, only the React specification is relevant; the SQL style guide and API design document still consume input tokens and context-window space that could hold code, conversation, and tool results.
The Solution
At startup, SkillLoader scans skills/*/SKILL.md, reads name and description from YAML frontmatter, and adds that catalog to the system prompt. When the model needs the full instructions, it calls load_skill(name); the returned SKILL.md is appended to the message list as a tool_result.
| Content | Model input | Added |
|---|---|---|
| Skill name and description | system prompt | At startup |
Full SKILL.md | tool_result | When load_skill is called |
How It Works
Each skill is a directory containing SKILL.md:
skills/
agent-builder/SKILL.md
code-review/SKILL.md
mcp-builder/SKILL.md
pdf/SKILL.md
Scan Skills
class SkillLoader:
def scan(self):
self.skills.clear()
skills_root = self.skills_dir.resolve()
for manifest in sorted(self.skills_dir.glob("*/SKILL.md")):
if (not manifest.is_file()
or not manifest.resolve().is_relative_to(skills_root)):
continue
content = manifest.read_text(encoding="utf-8")
metadata, body = self.parse_frontmatter(content)
raw_name = metadata.get("name")
name = raw_name.strip() if isinstance(raw_name, str) else ""
name = name or manifest.parent.name
raw_description = metadata.get("description")
description = (raw_description.strip()
if isinstance(raw_description, str) else "")
description = description or body.split("\n", 1)[0]
description = " ".join(str(description).lstrip("# ").split())
self.skills[name] = {
"name": name,
"description": description,
"content": content,
}
catalog() returns only names and descriptions:
- code-review: Perform thorough code reviews...
- pdf: Process PDF files...
Build the System Prompt
ENVIRONMENT_PROMPT = (
"Windows: the bash tool runs through cmd.exe; use cmd.exe syntax, not Unix "
"Bash or PowerShell syntax, and prefer dedicated file tools for file operations"
if os.name == "nt"
else "Unix-like: the bash tool runs the system shell"
)
def build_system_prompt() -> str:
return (
f"You are a coding agent at {WORKDIR}. Environment: {ENVIRONMENT_PROMPT}. "
"Use tools to solve tasks. "
"Act, don't explain.\n\n"
f"Skills available:\n{SKILL_LOADER.catalog()}\n\n"
"Use load_skill to read the full instructions when a skill applies."
)
This function combines the fixed Agent instructions with the catalog found at startup.
Load Full Content
def load(self, name: str) -> str:
skill = self.skills.get(name)
if skill:
return skill["content"]
available = ", ".join(self.skills) or "none"
return f"Error: Unknown skill '{name}'. Available: {available}"
name looks up the startup registry; it is not interpreted as a file path. After the tool returns, the existing Agent Loop appends its content as a new tool_result message.
Try It
cd learn-claude-code
python s07_skill_loading/code.py
Try these prompts:
What skills are available?Load the code-review skill and follow its instructionsReview README.md and load the relevant skill first
Check that the system prompt contains only the catalog and that the full SKILL.md appears after load_skill is called.
#!/usr/bin/env python3
"""
s07_skill_loading.py - Skill Loading
The system prompt contains a catalog of skill names and descriptions.
The model loads the full SKILL.md only when it calls load_skill.
skills/ Startup
+------------------+ +------------------+
| code-review/ | ----> | SkillLoader |
| SKILL.md | | name + summary |
| pdf/ | +--------+---------+
| SKILL.md | |
+------------------+ v
system prompt catalog
LLM -- load_skill(name) --> full SKILL.md
^ |
+--------- tool_result --------+
"""
import os
import re
import subprocess
from pathlib import Path
import yaml
try:
import readline
readline.parse_and_bind('set bind-tty-special-chars off')
readline.parse_and_bind('set input-meta on')
readline.parse_and_bind('set output-meta on')
readline.parse_and_bind('set convert-meta off')
except ImportError:
pass
from anthropic import Anthropic
from dotenv import load_dotenv
load_dotenv(override=True)
if os.getenv("ANTHROPIC_BASE_URL"):
os.environ.pop("ANTHROPIC_AUTH_TOKEN", None)
WORKDIR = Path.cwd()
SKILLS_DIR = WORKDIR / "skills"
client = Anthropic(base_url=os.getenv("ANTHROPIC_BASE_URL"))
MODEL = os.environ["MODEL_ID"]
ENVIRONMENT_PROMPT = (
"Windows: the bash tool runs through cmd.exe; use cmd.exe syntax, not Unix "
"Bash or PowerShell syntax, and prefer dedicated file tools for file operations"
if os.name == "nt"
else "Unix-like: the bash tool runs the system shell"
)
# -- Skill catalog --
class SkillLoader:
def __init__(self, skills_dir: Path):
self.skills_dir = skills_dir
self.skills: dict[str, dict[str, str]] = {}
self.scan()
@staticmethod
def parse_frontmatter(text: str) -> tuple[dict, str]:
lines = text.splitlines(keepends=True)
if not lines or lines[0].rstrip("\r\n") != "---":
return {}, text
closing_index = next(
(index for index, line in enumerate(lines[1:], start=1)
if line.rstrip("\r\n") == "---"),
None,
)
if closing_index is None:
return {}, text
frontmatter = "".join(lines[1:closing_index])
body = "".join(lines[closing_index + 1:]).strip()
try:
metadata = yaml.safe_load(frontmatter) or {}
except yaml.YAMLError:
metadata = {}
if not isinstance(metadata, dict):
metadata = {}
return metadata, body
def scan(self):
self.skills.clear()
if not self.skills_dir.exists():
return
skills_root = self.skills_dir.resolve()
for manifest in sorted(self.skills_dir.glob("*/SKILL.md")):
if (not manifest.is_file()
or not manifest.resolve().is_relative_to(skills_root)):
continue
content = manifest.read_text(encoding="utf-8")
metadata, body = self.parse_frontmatter(content)
raw_name = metadata.get("name")
name = raw_name.strip() if isinstance(raw_name, str) else ""
name = name or manifest.parent.name
raw_description = metadata.get("description")
description = (raw_description.strip()
if isinstance(raw_description, str) else "")
description = description or body.split("\n", 1)[0]
description = " ".join(str(description).lstrip("# ").split())
self.skills[name] = {
"name": name,
"description": description,
"content": content,
}
def catalog(self) -> str:
if not self.skills:
return "(no skills found)"
return "\n".join(
f"- {skill['name']}: {skill['description']}"
for skill in self.skills.values()
)
def load(self, name: str) -> str:
skill = self.skills.get(name)
if skill:
return skill["content"]
available = ", ".join(self.skills) or "none"
return f"Error: Unknown skill '{name}'. Available: {available}"
SKILL_LOADER = SkillLoader(SKILLS_DIR)
def build_system_prompt() -> str:
return (
f"You are a coding agent at {WORKDIR}. Environment: {ENVIRONMENT_PROMPT}. "
"Use tools to solve tasks. "
"Act, don't explain.\n\n"
f"Skills available:\n{SKILL_LOADER.catalog()}\n\n"
"Use load_skill to read the full instructions when a skill applies."
)
SYSTEM = build_system_prompt()
# -- Tools --
def run_bash(command: str) -> str:
try:
result = subprocess.run(
command, shell=True, cwd=WORKDIR,
capture_output=True, text=True, errors="replace", timeout=120,
)
output = (result.stdout + result.stderr).strip()
return output[:50000] if output else "(no output)"
except subprocess.TimeoutExpired:
return "Error: Timeout (120s)"
def run_read(path: str, limit: int | None = None) -> str:
try:
lines = (WORKDIR / path).resolve().read_text(encoding="utf-8").splitlines()
if limit and limit < len(lines):
lines = lines[:limit] + [f"... ({len(lines) - limit} more lines)"]
return "\n".join(lines)
except Exception as e:
return f"Error: {e}"
def run_write(path: str, content: str) -> str:
try:
file_path = (WORKDIR / path).resolve()
file_path.parent.mkdir(parents=True, exist_ok=True)
file_path.write_text(content, encoding="utf-8")
return f"Wrote {len(content)} bytes to {path}"
except Exception as e:
return f"Error: {e}"
def run_edit(path: str, old_text: str, new_text: str) -> str:
try:
file_path = (WORKDIR / path).resolve()
text = file_path.read_text(encoding="utf-8")
if old_text not in text:
return f"Error: text not found in {path}"
file_path.write_text(text.replace(old_text, new_text, 1), encoding="utf-8")
return f"Edited {path}"
except Exception as e:
return f"Error: {e}"
def run_glob(pattern: str) -> str:
import glob
try:
matches = sorted({
match for match in glob.glob(
pattern, root_dir=WORKDIR, recursive=True)
if (WORKDIR / match).resolve().is_relative_to(WORKDIR)
})
shown = matches[:200]
if len(matches) > 200:
shown.append("... (more matches omitted; narrow the pattern)")
return "\n".join(shown) if shown else "(no matches)"
except Exception as e:
return f"Error: {e}"
TOOLS = [
{"name": "bash", "description": "Run a shell command.",
"input_schema": {"type": "object", "properties": {"command": {"type": "string"}}, "required": ["command"]}},
{"name": "read_file", "description": "Read file contents.",
"input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "limit": {"type": "integer"}}, "required": ["path"]}},
{"name": "write_file", "description": "Write content to a file.",
"input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, "required": ["path", "content"]}},
{"name": "edit_file", "description": "Replace exact text in a file once.",
"input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "old_text": {"type": "string"}, "new_text": {"type": "string"}}, "required": ["path", "old_text", "new_text"]}},
{"name": "glob", "description": "Find files matching a glob pattern; ** matches recursively.",
"input_schema": {"type": "object", "properties": {"pattern": {"type": "string"}}, "required": ["pattern"]}},
{"name": "load_skill", "description": "Load the full SKILL.md content by skill name.",
"input_schema": {"type": "object", "properties": {"name": {"type": "string"}}, "required": ["name"]}},
]
TOOL_HANDLERS = {
"bash": run_bash,
"read_file": run_read,
"write_file": run_write,
"edit_file": run_edit,
"glob": run_glob,
"load_skill": SKILL_LOADER.load,
}
# -- Hooks --
HOOKS = {"UserPromptSubmit": [], "PreToolUse": [], "PostToolUse": [], "Stop": []}
def register_hook(event: str, callback):
HOOKS[event].append(callback)
def trigger_hooks(event: str, *args):
for callback in HOOKS[event]:
result = callback(*args)
if result is not None:
return result
return None
DENY_LIST = ["rm -rf /", "sudo", "shutdown", "reboot", "mkfs", "dd if="]
DESTRUCTIVE_COMMAND_WORD = re.compile(
r"(?i)(?:^|[;&|()\n])\s*(?:rm|del)(?=\s|$|[;&|()])"
)
DESTRUCTIVE = ["rm ", "> /etc/", "chmod 777"]
def contains_destructive_command(command: str) -> bool:
return bool(DESTRUCTIVE_COMMAND_WORD.search(command))
def permission_hook(block):
"""PreToolUse: block denied operations and ask about risky ones."""
if block.name == "bash":
command = block.input.get("command", "")
for pattern in DENY_LIST:
if pattern in command:
print(f"\n\033[31m[blocked] '{pattern}'\033[0m")
return "Permission denied by deny list"
if contains_destructive_command(command) or any(
keyword in command for keyword in DESTRUCTIVE
):
print("\n\033[33m[permission] Potentially destructive command\033[0m")
print(f" Tool: {block.name}({block.input})")
choice = input(" Allow? [y/N] ").strip().lower()
if choice not in ("y", "yes"):
return "Permission denied by user"
if block.name in ("read_file", "write_file", "edit_file"):
path = block.input.get("path", "")
if not (WORKDIR / path).resolve().is_relative_to(WORKDIR):
print("\n\033[33m[permission] Access outside workspace\033[0m")
print(f" Tool: {block.name}({block.input})")
choice = input(" Allow? [y/N] ").strip().lower()
if choice not in ("y", "yes"):
return "Permission denied by user"
return None
def log_hook(block):
"""PreToolUse: log every tool call."""
args_preview = str(list(block.input.values())[:2])[:60]
print(f"\033[90m[HOOK] {block.name}({args_preview})\033[0m")
return None
def large_output_hook(block, output):
"""PostToolUse: warn on large output."""
if len(str(output)) > 100000:
print(f"\033[33m[HOOK] Large output from {block.name}: {len(str(output))} chars\033[0m")
return None
def context_inject_hook(query: str):
"""UserPromptSubmit: log the working directory."""
print(f"\033[90m[HOOK] UserPromptSubmit: working in {WORKDIR}\033[0m")
return None
def summary_hook(messages: list):
"""Stop: print the number of tool results in this message list."""
tool_count = sum(
1
for message in messages
for block in (
message.get("content")
if isinstance(message.get("content"), list)
else []
)
if isinstance(block, dict) and block.get("type") == "tool_result"
)
print(f"\033[90m[HOOK] Stop: session used {tool_count} tool calls\033[0m")
return None
register_hook("UserPromptSubmit", context_inject_hook)
register_hook("PreToolUse", permission_hook)
register_hook("PreToolUse", log_hook)
register_hook("PostToolUse", large_output_hook)
register_hook("Stop", summary_hook)
def execute_tool(block) -> str:
blocked = trigger_hooks("PreToolUse", block)
if blocked:
return str(blocked)
handler = TOOL_HANDLERS.get(block.name)
try:
output = handler(**block.input) if handler else f"Unknown: {block.name}"
except Exception as e:
output = f"Error: {e}"
trigger_hooks("PostToolUse", block, output)
return str(output)
def agent_loop(messages: list):
while True:
response = client.messages.create(
model=MODEL,
system=SYSTEM,
messages=messages,
tools=TOOLS,
max_tokens=8000,
)
messages.append({"role": "assistant", "content": response.content})
tool_calls = [
block for block in response.content if block.type == "tool_use"
]
if not tool_calls:
force = trigger_hooks("Stop", messages)
if force:
messages.append({"role": "user", "content": force})
continue
return
results = []
for block in tool_calls:
output = execute_tool(block)
results.append({
"type": "tool_result",
"tool_use_id": block.id,
"content": output,
})
messages.append({"role": "user", "content": results})
if __name__ == "__main__":
print("s07: Skill Loading - catalog first, full content on demand")
print("Enter a question, press Enter to send. Type q to quit.\n")
history = []
while True:
try:
# \001/\002 tell Readline the ANSI escapes have zero display width.
query = input("\001\033[36m\002s07 >> \001\033[0m\002")
except (EOFError, KeyboardInterrupt):
break
if query.strip().lower() in ("q", "exit", ""):
break
trigger_hooks("UserPromptSubmit", query)
history.append({"role": "user", "content": query})
agent_loop(history)
for block in history[-1]["content"]:
if getattr(block, "type", None) == "text":
print(block.text)
print()
What's Next
As tool calls accumulate, messages[] retains earlier file contents and tool results.
Источник и лицензия
- Адаптировано из shareAI-lab/learn-claude-code / s07_skill_loading/README.md (shareAI-lab contributors, Лицензия MIT, ce8f9f1)