Borrowing it
Nothing to install: this file belongs to spy007111/Vibe-Research. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/spy007111/Vibe-Research/main/.agents/skills/valuation/SKILL.mdgit clone --depth 1 https://github.com/spy007111/Vibe-ResearchWrote 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/spy007111/vibe-research/valuation)<a href="https://agentmods.dev/skills/spy007111/vibe-research/valuation"><img src="https://agentmods.dev/badge/skills/spy007111/vibe-research/valuation/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/spy007111/vibe-research/valuation"><img src="https://agentmods.dev/badge/skills/spy007111/vibe-research/valuation.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 | $0.00187 | $0.02747 |
| Opus 5.5 | $0.00075 | $0.01099 |
| Sonnet 5.5 | $0.00037 | $0.00549 |
| Haiku 4.5 | $0.00019 | $0.00275 |
Grade A, and why
valuation 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 26d 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.
This is a copy
97% identical to valuation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
成长股估值口径(valuation)
本 skill 是 company-research SOP 第 4 阶段(valuation)的口径说明书,也适用于任何"这家公司贵不贵"的讨论。原则:数字出自 calc,判读出自本手册,结论只到"情景 / 裁决点"为止(研究宪法 §0 第 2、3 条)。
0. 四条铁律
- 每一个 PE / CAGR / PEG / 年数都必须是一次
calc/cli.py调用的输出(带 calculation_id 与输入证据 id);没有对应证据就写"未获取:原因",不拿记忆或别的口径顶替。 - 不输出价格锚(目标价 / 合理价 / 买入价 / 止损价)。消化年数的"锚"是 PE 倍数情景,不是价格;报告里只给"情景 → 年数"表和裁决点。
- PEG 低 ≠ 安全:PEG 的分母(前瞻 CAGR)是整条计算里唯一的预测,周期一转 E 被下修、PEG 跳升。任何 PEG 判读必须同时给出"前瞻 vs TTM 事实是否对得上"。
- 单位与报告期随证据原样带入 calc,由 calc 归一;不在提示词里换算万元 / 亿元、不自己年化。
1. 分子:PE 的三个口径(并列报,主用第一个)
| 口径 | 公式(calc 函数) | 用途 | 已知的坑 |
|---|---|---|---|
| 扣非×4 年化 PE(主) | 总市值 ÷ (最新单季扣非净利润 × 4) → pe_deducted_annualized(total_market_cap, cap_unit, latest_quarter_deducted_profit, profit_unit) |
抓"当前运行速率",对快速成长股比 TTM 更不滞后 | 必用扣非、不用归母(单季×4 会把投资收益 / 补助 / 减值等一次性损益放大 4 倍);季节性:淡季单季×4 会高估 PE、旺季单季×4 会低估——淡旺季方向只能从该公司自己的单季序列判断(earnings-analysis §3),不得套用行业印象;判读时必须写明最新单季是哪个季度与方向性偏差 |
| 前瞻 PE | 现价 ÷ 一致预期 EPS(FY T 均值)→ forward_pe(price, eps_forecast) |
分析师已处理季节性;与扣非×4 对照看"市场在为哪一年定价" | 依赖一致预期质量(机构数 ≥ 3;见 §3) |
| TTM PE + 历史分位 | 总市值 ÷ 近 4 季净利和 → pe_ttm_from_parts(...),并与数据源 pe_ttm 交叉;分位 percentile_rank(history={"history_csv": {"raw_ref": <PE 历史 raw 文件>, "column": "peTTM", "where": {"tradestatus": "1"}, "date_column": "date"}}, current=pe_ttm)(CLI 序列输入形式见 calc/SPEC.md §3;不是把 history_csv / where 当顶层参数) |
历史位置参考;分位 < 20 个有效样本 → not_meaningful | 对快速成长股滞后偏高(把利润低的旧季度算进去);只作参考列,不作主判 |
T 的定义:T = 当前财年(Asia/Shanghai 当日所在年)。
2. 分母:可持续增速(主用前瞻 CAGR,强制交叉验证)
- 前瞻 CAGR = (一致预期 FY T+2 EPS ÷ FY T EPS)^(1/2) − 1 →
forward_cagr(eps_t, eps_t_plus_n, years=2)。最平滑、去基数去季节性;但它是最软的一环:卖方对热门赛道系统性乐观、远年样本薄、分歧大、会被持续修正。必须同时报 min / max 与机构数(consensus_dispersion(low, mean, high),看 max ÷ min 与 (max − min) ÷ mean)。 - TTM 同比(事实,去季节性去单季基数)= 近 4 季净利和 ÷ 前 4 季净利和 − 1 →
ttm_yoy(single_quarters, end_period, unit, money=true);主用归母口径(与一致预期 EPS 同口径),扣非口径并列交叉。 - 交叉验证判读 →
forward_vs_ttm_judgement(forward_cagr_value, ttm_yoy_value, tolerance_pp=10):approx(前瞻 ≈ TTM:增长已兑现,前瞻可信)/forward_below(前瞻远低于 TTM:市场在赌大幅减速,要问为什么)/forward_above(前瞻远高于 TTM:分析师在画饼,PEG 失真风险最高)。 - 两个禁用项:❌ 单季同比做分母(去年同期低基数会假性吹大);❌ 环比做分母(季节性把增长公司算成负,年化更爆炸)。✅ 环比(
qoq)只作"拐点 / 动量"温度计,报告里标注为信号而非增速。
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.
- 26d ago Changed 63fa8165ec92
- 1mo ago First seen · 83 lines · 187 tokens per session scan A 1599b3368650
valuation is a skill published in the GitHub repository spy007111/Vibe-Research (8 stars, last pushed 8d ago), licensed MIT. It adds 187 tokens to every session and 2,747 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to valuation, differing in 0 lines, and is treated as a copy.
valuation compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| valuationspy007111/Vibe-Research | Skill | 8 | 8d ago | MIT | A | 187 | Claude Code, Codex |
| sector-rotationHKUDS/Vibe-Trading | Skill | 38k | yesterday | MIT | A | 39 | Claude Code, Codex |
| twitter-readerhimself65/finance-skills | Skill | 3.5k | 2d ago | MIT | A | 110 | Claude Code |
| strategy-pivot-designertradermonty/claude-trading-skills | Skill | 3.7k | 2d ago | MIT | A | 28 | Claude Code, Codex |
| energy-procurementhashgraph-online/awesome-codex-plugins | Skill | 1.5k | yesterday | Apache-2.0 | A | 93 | Claude Code, Codex |
| airdrop-hunting-guidenirholas/three.ws | Skill | 269 | yesterday | Apache-2.0 | A | 54 | Claude Code, Codex |
| multi-expert-analyzerdigoal/blog | Skill | 8.6k | 2d ago | GPL-2.0 | A | 292 | Codex |