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 quality-screengit 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/quality-screen)<a href="https://agentmods.dev/skills/travisun/opptrix/quality-screen"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/quality-screen/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/travisun/opptrix/quality-screen"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/quality-screen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00110 | $0.01398 |
| Opus 5 | $0.00055 | $0.00699 |
| Sonnet 5 | $0.00022 | $0.00280 |
| Haiku 4.5 | $0.00011 | $0.00140 |
Grade A, and why
quality-screen 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 5d 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
去劣筛选（7 条硬指标）
目标：不错杀一流好公司，但排除确定的非一流公司。通过筛选 ≠ 确定好——仍须商业模式、管理层与估值研究。
何时使用 / 非目标
| 使用 | 不要用 |
|---|---|
| 个股/行业/指数/主题批量去劣 | 要找「动量最强 / 因子 Top」→ @skill:universe-screen 等量化筛 |
| 宁可漏网不可误杀的硬门槛 | 完整四大师深度研究 → @skill:investment-research |
| 交付去劣结果网页 | 行业漏斗终选 3 家 → @skill:industry-funnel |
研究质量（硬性）
- 报告头：
get_current_time+ 数据截止日期；信息丰富度 A/B/C - 强制结论：每家 通过 / 豁免通过 / 排除 / 数据不足（灰色）——禁止打太极
- 资料多 ≠ 确定性高；本产品为学习辅助，不是投资建议
- 署名：Opptrix · AI Berkshire 分析
7 条去劣指标
| # | 指标 | 排除条件 |
|---|---|---|
| 1 | 10 年平均 ROE | < 8% |
| 2 | 5 年累计自由现金流 | 为负 |
| 3 | 利息覆盖（EBIT/利息） | < 2×（银行/保险跳过） |
| 4 | 长期毛利率 | < 15% |
| 5 | 经营现金流/净利润（5 年均值） | < 0.7 |
| 6 | 长期净利率 | < 5% |
| 7 | 5 年总股本膨胀 | > 20%（非并购） |
豁免
- A（战略投入期→第 1 条）：上市 <10 年 + 毛利率 >30% + 近 2 年经营现金流为正
- B（主动低利润→第 6 条）：毛利率 >30% + 近 2 年净利率 ≥5% 或明确回升
- C（高周转薄利→第 4/6 条）：ROE >20% + OCF/NI >1.0 + 会员/平台/高周转模式（须在输入标注
business_model）
Opptrix 取数
| 宇宙 | 工具 |
|---|---|
| 个股 | search_instruments |
| 行业 | get_sector_list / get_sector_constituents |
| 指数 | get_index_constituents |
| 主题 | search_instruments + 资讯补洞后列清单 |
| 财务 | get_instrument_financial_indicators / get_instrument_financials / get_instrument_cash_flow；批量 batch_instrument_snapshots |
取数后 workspace_write 为 panels.financials 或 instruments[].metrics（禁止脚本联网；禁止原仓库路径）。
脚本
python scripts/quality_screen.py --input data.json --output result.json
meta.data_mode：七项齐全 → full；部分字段 → proxy（degraded: true）；无法打分 → insufficient。禁止写死降级。
步骤
- 解析输入模式（个股 / 行业 / 指数 / 主题）；大宇宙可
run_subagent并行取数，结束后reclaim_subagent - 写证据 JSON →
opptrix_runquality_screen.py - 汇总表：通过 / 排除 / 豁免通过 / 边界争议；行业模式加通过率与选股结论
list_web_vendor→create_web交付（署名 + 免责声明）
网页目录建议
- 筛选日期与宇宙说明
- 汇总表（7 列 + 结果）
- 排除/豁免明细
- 板块总结（若批量）
- 局限：去劣是第一步
- 免责声明
禁止
- 用训练记忆填「完整 10 年 ROE」冒充已取数
- 数据不足却标「通过」
- 用户可见文案堆工具名/脚本路径
- 无 web 交付就结束（除非用户只要口头要点）
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.
- 5d ago First seen · 100 lines · 110 tokens per session scan A 98c52b6e9dd5
quality-screen is a skill published in the GitHub repository Travisun/Opptrix (231 stars, last pushed yesterday), licensed Apache-2.0. It adds 110 tokens to every session and 1,398 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 2026-09-03.
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