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 Travisun/Opptrix --skill industry-funnelgit clone --depth 1 https://github.com/Travisun/OpptrixWrote 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/travisun/opptrix/industry-funnel)<a href="https://agentmods.dev/skills/travisun/opptrix/industry-funnel"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/industry-funnel.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 5.1 | $0.00084 | $0.01301 |
| Opus 5 | $0.00042 | $0.00651 |
| Sonnet 5 | $0.00017 | $0.00260 |
| Haiku 4.5 | $0.00008 | $0.00130 |
Grade A, and why
industry-funnel 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 3d 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.
What it actually says
行业漏斗筛选
从行业/方向扫描 → 硬指标粗筛 ≤10 → 结构化短评 → 终选 3 家四大师深度短文 + 仓位建议。每层淘汰必须留理由。
何时使用 / 非目标
| 使用 | 不要用 |
|---|---|
| 「AI 算力里帮我筛到三只值得深挖的」 | 只要产业链地图 → @skill:industry-research |
| 需要可复盘的留/弃标准 | 只要 7 条去劣硬门槛 → @skill:quality-screen |
量化因子宇宙 → @skill:universe-screen |
研究质量（硬性）
- 四大师框架覆盖终选 3 家（段/巴/芒/李）；数据不足须声明无法评分
- 强制结论：激进/稳健/保守分层 + 条件区间；好生意 ≠ 好价格下的好投资
- 镜子测试（买入建议前 ≤5 句）；A/B/C 信息丰富度；快速否决不可被分数对冲
get_current_time；署名 Opptrix · AI Berkshire 分析；免责声明
漏斗结构
全市场扫描 30–60 → 5 硬指标 ≤10 → 精细分析 → 终选 3 → 建议
第一层：扫描池
A 成交活跃 ∪ B 涨幅榜 ∪ C 市值前部；覆盖 A/港/美及关键国际；未上市单列「未来 IPO 候选」。工具：get_sector_constituents / get_index_constituents / search_instruments / batch_instrument_snapshots。
第二层：5 硬指标
| # | 指标 | 通过 |
|---|---|---|
| 1 | PE | 合理；高成长可 PEG<1.5 |
| 2 | ROE | >15% 或改善（重资产可放宽） |
| 3 | 经营现金流/净利 | >70% |
| 4 | 资产负债率 | <60%（公用事业 <70%） |
| 5 | 护城河快评 | ★★★+ |
5 全过直接留；4 过+1 接近标黄；不足 4 条淘汰写理由。过多则抬高护城河再筛。
python scripts/quality_gate.py --input candidates.json --output gate.json
第三层：精细分析（每家 300–500 字）
商业模式一句 / 财务质量 / 护城河 / 前 3 风险 / 估值快评 / 是否进终选。
终选按组合互补而非纯打分：≥1 高确定性、≥1 成长弹性、可选 1 高弹性卫星。
第四层：四大师短评（每家 800–1200 字）
段永平生意本质 → 巴菲特财务与安全边际 → 芒格逆向失败路径 → 李录长期确定性与能力圈。估值用：
python scripts/financial_rigor.py verify-valuation ...
python scripts/financial_rigor.py three-scenario ...
（脚本不联网；Agent 先取数写入 workspace。）
data_mode
完整财务+护城河可评 → full；缺字段粗筛 → proxy；宇宙都组不出 → insufficient（灰色地带，禁止拼凑终选）。
步骤
- 确认行业/方向与市场范围
- 建扫描池并标注纯正度
workspace_write→quality_gate.py- 精细分析 → 终选 3 → 四大师短评 + 仓位
create_web交付
网页目录建议
- 漏斗总览与数据截止
- 扫描池与淘汰日志
- 粗筛表
- 精细分析
- 终选三大师短评与仓位
- 风险与免责声明
禁止
- 黑箱淘汰；用故事股叙事跳过硬指标
- 依赖源仓库绝对路径或脚本联网取数
- 无交付网页（默认）
What ships with it
3 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.
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.
- 3d ago First seen · 109 lines · 84 tokens per session scan A 7fda8d9f59c1
industry-funnel is a skill published in the GitHub repository Travisun/Opptrix (230 stars, last pushed today), licensed Apache-2.0. It adds 84 tokens to every session and 1,301 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 2026-09-03.
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