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 agentmods add skills/fize/mmtickerlab/first-board-overnightnpx skills add Fize/mmtickerlab --skill first-board-overnightgit clone --depth 1 https://github.com/Fize/mmtickerlabWrote 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/fize/mmtickerlab/first-board-overnight)<a href="https://agentmods.dev/skills/fize/mmtickerlab/first-board-overnight"><img src="https://agentmods.dev/badge/skills/fize/mmtickerlab/first-board-overnight.svg" alt="Measured on agentmods" 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 | $0.00116 | $0.01113 |
| Opus 5 | $0.00058 | $0.00557 |
| Sonnet 5 | $0.00023 | $0.00223 |
| Haiku 4.5 | $0.00012 | $0.00111 |
Grade A, and why
first-board-overnight 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 today.
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.
What it actually says
First Board Overnight
把本技能作为策略决策层。让 market 提供数据，让 sim-trade 执行和记账，让 plan-review 提供通用盘前、盘中、盘后证据。不要在本技能中猜测行情、伪造成交或绕过任何数据门禁。
执行工作流
- 明确任务阶段：
candidate_review、entry_decision、exit_plan或trade_review。 - 读取 references/strategy.md，确认策略目标、边界和市场环境门禁。
- 读取 references/data-acquisition.md，盘点现有证据并主动获取缺失的可恢复数据。缓存不存在不等于数据不可用。
- 读取 references/evidence-contract.md，区分直接证据、可重建证据和不可重建证据。
- 获取完成后再定级：执行级证据完整为
READY；历史量价研究可完成但不能证明当时盘口或成交为RESEARCH_ONLY；获取失败或连研究结论也无法支持才返回BLOCKED。 - 分析入场时读取 references/entry-policy.md；分析 T+1 退出时读取 references/exit-policy.md。
- 涉及仓位或委托时读取 references/risk-policy.md。只有用户明确要求模拟下单且
status: READY、decision: BUY时才调用sim-trade；分析请求以及BLOCKED、RESEARCH_ONLY、WATCH、NO_TRADE结论不得创建委托。 - 完成交易后读取 references/review-schema.md，按决策时证据复盘，不使用结果反向改写原始理由。
决策纪律
- 始终先判断策略环境，再判断板块和个股；不要因单票盘口强而跳过环境门禁。
- 允许并优先保留
NO_TRADE。候选数量不是交易目标。 - 把板块地位、换手质量和资金合力标记为有证据的解释，不冒充可直接观测的事实。
- 不把固定时刻、市值、换手率、封单比例或止损比例视为所谓北京炒家的原始规则。未经样本验证的数值只能作为实验参数。
- 不宣称复刻任何个人的盘感或真实收益。本技能定义的是可验证的“首板隔夜实验策略”。
- 普通 A 股当日买入后次一交易日才可卖出；不得称为 T+0。
- 主动获取历史日线、分钟 K 线和技术指标来判断趋势、位置与量价状态；必要时使用
scripts/render_chart.py绘制图表。 - 不基于收盘快照生成盘中可成交的入场决定，不使用当前数据回填历史盘口或成交事实。
- 不把涨停视为必然可买。以
sim-trade返回的实际订单状态为准。
输出契约
每次策略判断必须输出：
as_of：交易日期、阶段和数据时间戳；status：READY、RESEARCH_ONLY或BLOCKED；market_regime：FAVORABLE、MIXED、UNFAVORABLE或INSUFFICIENT_EVIDENCE；decision：BUY、WATCH、NO_TRADE或EXIT_PLAN；evidence：逐条区分事实、解释和假设；counterevidence：最强反证与失效条件；risk_plan：仓位上限、最坏可接受损失和退出触发条件；execution：NOT_REQUESTED、NOT_ATTEMPTED及原因，或sim-trade的真实订单 ID、状态与未成交数量；RESEARCH_ONLY永远不得执行；unknowns：当前未采集或系统不支持的字段。
不得只给总分。若使用评分，必须同时展示构成证据、反证和未知项。
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 261 B
- references/data-acquisition.md 3.5 KB
- references/entry-policy.md 2.2 KB
- references/evidence-contract.md 2.6 KB
- references/exit-policy.md 1.3 KB
- references/review-schema.md 1.7 KB
- references/risk-policy.md 1.6 KB
- references/strategy.md 1.9 KB
- scripts/render_chart.py 10 KB runs code
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.
- today Changed · +1 lines 445a69329789
- 5d ago First seen · 48 lines · 116 tokens per session scan A e8d84258ae99
first-board-overnight is a skill published in the GitHub repository Fize/mmtickerlab (5 stars, last pushed yesterday), licensed MIT. It adds 116 tokens to every session and 1,113 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
sector-rotation
行业轮动分析——申万行业景气度评分、行业动量排名、产业链传导、估值/盈利/资金流多维比较框架.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
Use when evaluating A-share limit-up (涨停板) setups through Chen Hao's sentiment and momentum lens: market emotion cycles, board strength, follow-through, and short-term aggressive momentum trading.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
vectorbt
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics.