Files
zk-data-agent/src/bundled_skills.py
T
wangsenhao a09d4afacd fix: pipeline排序/runDic映射/label-master repeatable/达标优先级
- pipeline items 按 first_ts 自然时间排序,去掉 hardcoded section_order
- augment step-detail 去掉 R{n-1} 偏移,直接用本轮 runDic(与 resolve_run_ids.sh 实际行为对齐)
- 加 BundledSkill.repeatable 字段,label-master 标记 repeatable:true 跳过 duplicate detection
- SKILL.md/program.md: 目标集≥95%最高优先级,未达标前其他集合轻微下降不回滚
- SKILL.md/program.md: 禁止正则脚本替代 label-master 语义复核
- program.md: 每轮 SFT 前必须展示训练数据组成
- resolve_run_ids.sh: 修正注释(SFT_RUNDIC = 当轮 eval workflow id)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-26 14:10:32 +08:00

305 lines
9.9 KiB
Python

"""Skill definitions — prompt-type skills invocable via the Skill tool.
Directory skills differ from slash commands:
- They generate AI prompts sent to the model (prompt-type).
- They carry ``when_to_use`` guidance for model auto-invocation.
- They can restrict ``allowed_tools`` during execution.
- They appear in system-reminder skill listings for model discovery.
Project-maintained skills live under the repository root ``skills/`` directory.
"""
from __future__ import annotations
import subprocess
from dataclasses import dataclass, field
from pathlib import Path
from typing import TYPE_CHECKING, Callable
if TYPE_CHECKING:
from .agent_runtime import LocalCodingAgent
ALWAYS_ENABLED_HIDDEN_SKILL_NAMES = frozenset(
{'update-config', 'verify', 'simplify', 'debug'}
)
@dataclass(frozen=True)
class BundledSkill:
"""A bundled skill definition."""
name: str
description: str
when_to_use: str = ''
aliases: tuple[str, ...] = ()
allowed_tools: tuple[str, ...] = ()
user_invocable: bool = True
repeatable: bool = False
source: str = 'python'
path: str | None = None
get_prompt: Callable[['LocalCodingAgent', str], str] = lambda _a, _args: ''
# ---------------------------------------------------------------------------
# Skill prompt generators
# ---------------------------------------------------------------------------
def _simplify_prompt(agent: 'LocalCodingAgent', args: str) -> str:
"""Generate the simplify review prompt."""
cwd = str(agent.runtime_config.cwd)
diff = ''
try:
proc = subprocess.run(
['git', 'diff', 'HEAD'],
cwd=cwd, capture_output=True, text=True, timeout=30,
)
diff = proc.stdout.strip()
except Exception:
pass
if not diff:
try:
proc = subprocess.run(
['git', 'diff'],
cwd=cwd, capture_output=True, text=True, timeout=30,
)
diff = proc.stdout.strip()
except Exception:
pass
diff_section = f'```diff\n{diff}\n```' if diff else 'No changes detected — run `git diff` to verify.'
return f"""Review the changed code for reuse, quality, and efficiency, then fix any issues found.
## Changed Code
{diff_section}
## Review Checklist
### Code Reuse
- Duplicated functions or logic that should be consolidated
- Redundant utilities or helpers
- Patterns that should use existing abstractions
### Code Quality
- Redundant state or unnecessary variables
- Parameter sprawl (too many function arguments)
- Copy-paste code that should be refactored
- Leaky abstractions or wrong abstraction level
- Stringly-typed code that should use enums/types
- Overly nested conditionals
### Efficiency
- N+1 query or call patterns
- Missed concurrency opportunities (parallel I/O)
- Hot-path bloat (unnecessary work in tight loops)
- Memory leaks or unbounded growth
## Instructions
1. Read the full diff above
2. For each category, identify concrete issues with file paths and line numbers
3. Fix each issue directly — do not just report them
4. After fixing, verify the changes compile/run correctly
{f'Additional context: {args}' if args.strip() else ''}"""
def _debug_prompt(agent: 'LocalCodingAgent', args: str) -> str:
"""Generate the debug prompt."""
import os
lines = ['## Debug Session Info', '']
lines.append(f'Working directory: {agent.runtime_config.cwd}')
lines.append(f'Model: {agent.model_config.model}')
lines.append(f'Base URL: {agent.model_config.base_url}')
session = agent.last_session
if session:
lines.append(f'Messages in session: {len(session.messages)}')
tool_count = sum(1 for m in session.messages if m.role == 'tool')
lines.append(f'Tool results: {tool_count}')
usage = agent.cumulative_usage
lines.append(f'Total tokens used: {usage.total_tokens:,}')
lines.append(f'Cost: ${agent.cumulative_cost_usd:.4f}')
# Check for debug log
debug_log = os.environ.get('CLAUDE_CODE_DEBUG_LOG', '')
if debug_log:
lines.append(f'Debug log: {debug_log}')
else:
lines.append('Debug logging: not enabled (set CLAUDE_CODE_DEBUG_LOG to enable)')
return '\n'.join(lines)
def _directory_skill_prompt(body: str) -> Callable[['LocalCodingAgent', str], str]:
def _prompt(_agent: 'LocalCodingAgent', args: str) -> str:
if args.strip():
return f'{body.rstrip()}\n\n## Invocation Arguments\n\n{args.strip()}'
return body
return _prompt
def _parse_front_matter(text: str) -> tuple[dict[str, str], str]:
if not text.startswith('---\n'):
return {}, text
end_marker = text.find('\n---\n', 4)
if end_marker == -1:
return {}, text
raw_meta = text[4:end_marker]
body = text[end_marker + len('\n---\n') :]
metadata: dict[str, str] = {}
for line in raw_meta.splitlines():
if not line.strip() or line.lstrip().startswith('#') or ':' not in line:
continue
key, value = line.split(':', 1)
metadata[key.strip()] = value.strip()
return metadata, body
def _split_csv(value: str | None) -> tuple[str, ...]:
if not value:
return ()
return tuple(item.strip() for item in value.split(',') if item.strip())
def _parse_bool(value: str | None, *, default: bool = True) -> bool:
if value is None or not value.strip():
return default
return value.strip().lower() not in {'0', 'false', 'no', 'off'}
def _load_directory_skill(path: Path, *, source: str = 'directory') -> BundledSkill | None:
try:
text = path.read_text(encoding='utf-8')
except OSError:
return None
metadata, body = _parse_front_matter(text)
name = metadata.get('name') or path.parent.name
description = metadata.get('description', '').strip()
if not name.strip() or not description:
return None
return BundledSkill(
name=name.strip(),
description=description,
when_to_use=metadata.get('when_to_use', '').strip(),
aliases=_split_csv(metadata.get('aliases')),
allowed_tools=_split_csv(metadata.get('allowed_tools')),
user_invocable=_parse_bool(metadata.get('user_invocable'), default=True),
repeatable=_parse_bool(metadata.get('repeatable'), default=False),
source=source,
path=str(path),
get_prompt=_directory_skill_prompt(body.strip()),
)
def load_directory_skills(
root: Path | None = None,
*,
source: str = 'directory',
) -> tuple[BundledSkill, ...]:
skill_root = root or (Path(__file__).resolve().parent.parent / 'skills')
if not skill_root.exists() or not skill_root.is_dir():
return ()
skills: list[BundledSkill] = []
for path in sorted(skill_root.glob('*/SKILL.md')):
skill = _load_directory_skill(path, source=source)
if skill is not None:
skills.append(skill)
return tuple(skills)
def load_project_skills(cwd: Path | str | None) -> tuple[BundledSkill, ...]:
if cwd is None:
return ()
return load_directory_skills(Path(cwd).resolve() / 'skills', source='project')
# ---------------------------------------------------------------------------
# Skill registry
# ---------------------------------------------------------------------------
PYTHON_BUNDLED_SKILLS: tuple[BundledSkill, ...] = (
BundledSkill(
name='simplify',
description='Review changed code for reuse, quality, and efficiency, then fix issues.',
when_to_use='When the user asks to review, simplify, or clean up recent code changes.',
allowed_tools=('read_file', 'edit_file', 'write_file', 'bash', 'grep_search', 'glob_search'),
get_prompt=_simplify_prompt,
),
BundledSkill(
name='debug',
description='Debug the current session — show diagnostics, token usage, and config.',
when_to_use='When the user asks to debug the agent session or check diagnostics.',
user_invocable=True,
get_prompt=_debug_prompt,
),
)
def get_bundled_skills(cwd: Path | str | None = None) -> tuple[BundledSkill, ...]:
"""Return registered skills, with project skills taking precedence."""
skills = [
*load_project_skills(cwd),
*load_directory_skills(),
*PYTHON_BUNDLED_SKILLS,
]
seen: set[str] = set()
deduped: list[BundledSkill] = []
for skill in skills:
lowered = skill.name.lower()
if lowered in seen:
continue
seen.add(lowered)
deduped.append(skill)
return tuple(deduped)
def find_bundled_skill(name: str, cwd: Path | str | None = None) -> BundledSkill | None:
"""Look up a bundled skill by name or alias."""
lowered = name.lower()
for skill in get_bundled_skills(cwd):
if lowered == skill.name or lowered in skill.aliases:
return skill
return None
def format_skills_for_system_prompt(
max_chars: int = 8000,
cwd: Path | str | None = None,
enabled_skill_names: tuple[str, ...] | set[str] | None = None,
) -> str:
"""Format bundled skills for inclusion in system-reminder messages.
The model discovers available skills through this listing.
"""
lines = ['Available skills (invoke via Skill tool):']
char_count = len(lines[0])
enabled_names = (
{name.strip().lower() for name in enabled_skill_names if name.strip()}
if enabled_skill_names is not None
else None
)
for skill in get_bundled_skills(cwd):
if not skill.user_invocable:
continue
if enabled_names is not None and skill.name.lower() not in enabled_names:
continue
entry = f'- {skill.name}: {skill.description}'
if skill.when_to_use:
entry += f' When to use: {skill.when_to_use}'
if len(entry) > 250:
entry = entry[:247] + '...'
if char_count + len(entry) + 1 > max_chars:
break
lines.append(entry)
char_count += len(entry) + 1
return '\n'.join(lines)