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 serejaris/kimi-skills --skill equity-researchergit clone --depth 1 https://github.com/serejaris/kimi-skillsWrote 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/serejaris/kimi-skills/equity-researcher)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/equity-researcher"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/equity-researcher/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/serejaris/kimi-skills/equity-researcher"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/equity-researcher.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 | $0.00249 | $0.04905 |
| Opus 5 | $0.00125 | $0.02452 |
| Sonnet 5 | $0.00050 | $0.00981 |
| Haiku 4.5 | $0.00025 | $0.00490 |
Grade A, and why
equity-researcher 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 9d 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
89% identical to equity-research — 4 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 — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Equity Research Skill
This skill generates institutional-grade investment research in two modes: Tear Sheet (3-5 page PDF, single session) and Equity Report (≥25 page PDF, 3-task architecture with financial model). Both modes share the same analytical philosophy — the difference is depth, scope, and delivery structure.
Your first job: figure out what the user wants. Then carry the Core Principles into the next file.
Phase 0.0: Router — Intent Clarification + Output Type Detection
Step 1: Detect Language
Detect the user's language from their message. Use that language for ALL follow-up questions and the final report.
| User Language | report_language |
|---|---|
| Chinese (any) | zh |
| English | en |
| Mixed / unclear | Match the dominant language in user's message |
Step 2: Classify Intent (3 Tiers)
Not every company analysis request needs a full report. Before committing resources, determine what the user actually wants.
| Tier | User Signal Examples | Action |
|---|---|---|
| Tier A: Explicit report keyword | "tear sheet", "one pager", 投资速览, 投资简报, "research report", "deep dive", "equity report", 研报, 深度研究, 深度分析 | → Skip to Step 3 (output type is clear) |
| Tier B: Company analysis — ambiguous depth | "帮我分析一下[公司]", "analyze [company]", "帮我看看[股票]", "look into [stock]", "了解一下[公司]", "what do you think of [company]", 个股分析, 公司分析, or just a stock code (e.g. AAPL, 600519) | → Ask user (Step 2a) |
| Tier C: Simple question | "XX公司是做什么的", "what's [company]'s market cap", "when is [stock]'s next earnings" | → Do NOT trigger this skill. Answer conversationally. No report generation. |
What ships with it
34 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.
- analysis/investment-logic.md 24 KB
- analysis/projection-assumptions.md 240 B
- analysis/revenue-model.md 13 KB
- analysis/risk-framework.md 7.4 KB
- analysis/scenario-deep-dive.md 6.0 KB
- analysis/six-dimension-analysis.md 11 KB
- modules/company-overview.md 10 KB
- modules/equity-report-charts.md 17 KB
- modules/industry-chain.md 32 KB
- modules/stock-chart.md 14 KB
- modules/tables.md 16 KB
- modules/valuation.md 6.0 KB
- output/report-layout.md 49 KB
- output/report-qa.md 17 KB
- output/report.css 38 KB
- output/tearsheet-layout.md 17 KB
- output/tearsheet-qa.md 21 KB
- output/tearsheet.css 29 KB
- references/analysis-brief-template.md 20 KB
- references/data-sources-detail.md 262 B
- references/data-sources.md 22 KB
- references/financial-model-spec.md 28 KB
- references/output-schema.md 10 KB
- references/research-document-template.md 28 KB
- scripts/chart_generator.py 5.7 KB runs code
- scripts/embed_charts.py 3.7 KB runs code
- scripts/report_validator.py 4.8 KB runs code
- scripts/stock_chart_generator.py 3.7 KB runs code
- SKILL-equity-task1.md 21 KB
- SKILL-task2-model.md 15 KB
- SKILL-task3-report.md 22 KB
- SKILL-tearsheet.md 17 KB
- valuation/comparable.md 8.5 KB
- valuation/dcf-and-sensitivity.md 19 KB
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.
- 9d ago First seen · 328 lines · 249 tokens per session scan A 96e6dae183e7
equity-researcher is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 249 tokens to every session and 4,905 once invoked, about $0.0012 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to equity-research, differing in 4 lines, and is treated as a copy.
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