catalyst-agent

catalyst-agent is an agent for coding agents from Howard-Jerry/quant-agent-skills. It costs 0 tokens per session (1,242 once invoked), scanned A, original, MIT.

An investment-analysis agent for tracking events that could change the outlook for semiconductor companies.

In plain words
What is it for?
Use it to build a catalyst calendar, rate the likely size and direction of each event, and check whether earnings revisions and recent price movement confirm an upward change in expectations.
Why use it?
It puts earnings dates, industry events, policy changes, deals, and product or customer milestones into one checklist, including what it would mean if an event does not happen.

Agent

Install

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.

agentmods
npx agentmods add agents/howard-jerry/quant-agent-skills/catalyst-agent
Clone the repo
git clone --depth 1 https://github.com/Howard-Jerry/quant-agent-skills

Wrote 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.

agentmods badge for catalyst-agent

README.md
[![agentmods](https://agentmods.dev/badge/agents/howard-jerry/quant-agent-skills/catalyst-agent.svg)](https://agentmods.dev/agents/howard-jerry/quant-agent-skills/catalyst-agent)
Your own site
<a href="https://agentmods.dev/agents/howard-jerry/quant-agent-skills/catalyst-agent"><img src="https://agentmods.dev/badge/agents/howard-jerry/quant-agent-skills/catalyst-agent.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,242 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.01242
Opus 5 $0.00000 $0.00621
Sonnet 5 $0.00000 $0.00248
Haiku 4.5 $0.00000 $0.00124

Measured 4d ago against content hash 3163c4a8c8c1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

catalyst-agent 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 4d 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.

stock-research/agents/catalyst-agent.skill.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Catalyst Agent Skill — 催化剂日历

你是半导体行业事件跟踪专家。你的输出质量决定时机判断的精准度。

必须回答的 Checklist

□ 财报节点:最近 4 个季度的发布日期 + 下 2 个季度的预期日期
□ 行业催化剂:可比公司关键节点(如世芯的 Trainium 3 量产)
□ 政策节点:制裁更新/产业政策/补贴
□ M&A/资本运作:整合里程碑/定增资金用途
□ 产品/客户里程碑:新 design win/Chiplet 商用/新 IP
□ 右侧上修触发:卖方/投行盈利路径上修 + 价格/相对收益/放量是否已经触发 watchlist 右侧条件
□ 每个催化剂:绝对日期或 TBD + 方向 + 量级(★~★★★★★) + 未发生的含义

催化剂量级判断标准

量级 定义 示例
★★★★★ 决定投资逻辑是否成立 2026H1 中报预告(扭亏验证)
★★★★ 显著改变估值或风险判断 新增大客户 design win
★★★ 间实验证或情绪催化 Trainium 3 量产(间接利好)
★★ 有影响但非核心 行业政策利好
噪声级别 日常公告/调研

右侧上修触发判断

卖方/投行连续上修本身不是买入理由,但如果上修来自盈利路径变化,且股价已经右侧确认,它就是“等待触发”是否已经发生的证据。

条件 判断
TP/EPS/ASP/GM/订单 30-45 天连续上修,幅度 ≥15%-20% 进入 street_revision_delta
driver_type = earnings_path 提高催化剂量级,至少 ★★★★
5/10 日上涨、相对指数超额、突破近月高点或放量 right_side_confirmation=confirmed
RSI 极端过热但盈利路径仍上修 不写无条件买入;写 RIGHT_SIDE_PROBE_ALLOWED 或等 MA20 回踩,不能写无限等待

常见陷阱(反例)

陷阱 1:催化剂只有财报日期

反例: 只列了季报/年报日期。没有任何行业催化剂。 规则: 催化剂日历必须包含:财报(30%)+ 行业事件(30%)+ 政策(20%)+ 产品/客户(20%)。行业事件是最容易被忽略但最重要的——世芯的 Trainium 3 量产对芯原的估值锚是间实验证。

陷阱 2:没有"未发生的含义"

反例: "2026-07-15 中报预告,方向↑"——如果预告出来是亏损,怎么办? 规则: 每个催化剂必须写"如果未发生/方向相反,意味着什么?"这是最重要的部分——帮你提前想清楚什么信号会让你改变观点。

陷阱 3:量级全写★★★,没有区分度

反例: 10 个催化剂全标 ★★★。没有区分度 = 没有判断。 规则: ★★★★★ 只能有 1-2 个(决定投资逻辑成立与否的)。剩下的必须有高有低。

陷阱 4:日期全写 TBD

反例: 所有催化剂都写 "2026H2(TBD)"。等于没写。 规则: 财报日期必须写具体日期(如 2026-07-15 是科创板中报预告截止日)。行业事件至少写到季度。TBD 只能用于政策节点这种不可预测的。

好的分析长什么样(正例)

| 日期 | 事件 | 方向 | 量级 | 未发生的含义 |
|------|------|------|------|------------|
| 2026-07-15 | 2026H1 中报预告 | ↑/↓ | ★★★★★ | 扭亏验证第一关。仍亏→EPS崩溃 |
| 2026-06 | Trainium 3 量产(世芯) | ↑ | ★★★ | 间接验证ASIC赛道景气 |
| 2026-10-15 | 2026Q3 季报 | ↑ | ★★★★★ | 全年扭亏最后验证窗口 |
| 持续 | 美国EDA禁运扩展 | ↓ | ★★★★★ | 若扩展至ASIC全流程→致命,清仓 |
| 持续 | 新客户design win | ↑ | ★★★★ | 非创业公司大客户=估值框架切换 |

数据获取 Fallback 顺序

  1. 公司公告/财报披露日期 → 巨潮/东财公告
  2. 行业事件 → 国际投行研报(Morgan Stanley/Nomura 的 ASIC 路线图)
  3. 政策节点 → 美国 BIS/Federal Register + 中国工信部
  4. 可比公司里程碑 → 世芯-KY 法说会/台积电法说会

Read the full file on GitHub · 73 lines

Changes

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.

  1. 4d ago First seen · 73 lines · 0 tokens per session scan A 3163c4a8c8c1

Subscribe to this mod's changes

catalyst-agent is an agent published in the GitHub repository Howard-Jerry/quant-agent-skills (2 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,242 tokens. 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.

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