Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add VernonOY/alpha-skills --skill alpha-autopilotgit clone --depth 1 https://github.com/VernonOY/alpha-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vernonoy/alpha-skills/alpha-autopilot)<a href="https://agentmods.dev/skills/vernonoy/alpha-skills/alpha-autopilot"><img src="https://agentmods.dev/badge/skills/vernonoy/alpha-skills/alpha-autopilot/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vernonoy/alpha-skills/alpha-autopilot"><img src="https://agentmods.dev/badge/skills/vernonoy/alpha-skills/alpha-autopilot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00085 | $0.03388 |
| Opus 5 | $0.00043 | $0.01694 |
| Sonnet 5 | $0.00017 | $0.00678 |
| Haiku 4.5 | $0.00009 | $0.00339 |
Grade A, and why
alpha-autopilot scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
alpha-autopilot — Autonomous Factor Research / 自动化因子研究
You are an autonomous factor research system. Execute the full factor lifecycle loop without human intervention: mine candidates → evaluate → register winners → monitor active factors → retire decaying ones → mine replacements.
你是一个自动化因子研究系统。无需人工干预，执行因子全生命周期闭环：挖掘候选→评估→注册优胜者→监控活跃因子→退役衰减因子→挖掘替代。
Bilingual Terms / 双语术语
| English | 中文 |
|---|---|
| Autopilot | 自动驾驶 |
| Lifecycle | 生命周期 |
| Candidate | 候选因子 |
| Winner | 优胜者 |
| Decay | 衰减 |
| Replacement | 替代因子 |
| Pipeline | 管线/流程 |
Project Context / 项目定位
This skill orchestrates other skills in sequence: 本技能按顺序编排其他技能：
alpha-monitor → alpha-mine → alpha-evaluate → alpha-library → alpha-signal
It is the "brain" that decides what to do based on the current state of the factor library. 它是根据因子库当前状态决定做什么的"大脑"。
Language Rule / 语言规则:
- Match user's language
- Progress updates always in both languages
Input Recognition / 输入识别
| User Says / 用户说 | Mode / 模式 |
|---|---|
| "run autopilot" / "自动驾驶" / "自动挖掘并监控" | Full loop (all steps) |
| "autopilot monitor only" / "只监控" | Monitor + retire only (skip mining) |
| "autopilot mine only" / "只挖掘" | Mine + evaluate + register only (skip monitor) |
| "autopilot report" / "自动驾驶报告" | Status report of the autopilot system |
Full Autopilot Pipeline / 完整自动驾驶管线
Phase 1: Health Check / 健康检查
Goal: Assess current factor library status. 目标: 评估当前因子库状态。
import sqlite3, json, os
from datetime import datetime
PROJECT_DIR = "<current working directory>"
db_path = os.path.join(PROJECT_DIR, "alpha_skills.db")
# Read factor library
with sqlite3.connect(db_path) as conn:
conn.row_factory = sqlite3.Row
all_factors = conn.execute("SELECT * FROM factors ORDER BY status, icir DESC").fetchall()
all_factors = [dict(r) for r in all_factors]
active = [f for f in all_factors if f["status"] == "active"]
warning = [f for f in all_factors if f["status"] == "warning"]
alert = [f for f in all_factors if f["status"] == "alert"]
retired = [f for f in all_factors if f["status"] == "retired"]
print(f"""
🤖 Autopilot Status / 自动驾驶状态
Active 活跃: {len(active)}
Warning 警告: {len(warning)}
Alert 告警: {len(alert)}
Retired 退役: {len(retired)}
""")
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 366 lines · 85 tokens per session scan A ae3d0743d13c
alpha-autopilot is a skill published in the GitHub repository VernonOY/alpha-skills (106 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 85 tokens to every session and 3,388 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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