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 leecyno1/boutique-skills --skill anthropic-fs-lseg-equity-researchgit clone --depth 1 https://github.com/leecyno1/boutique-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/leecyno1/boutique-skills/anthropic-fs-lseg-equity-research)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/anthropic-fs-lseg-equity-research"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/anthropic-fs-lseg-equity-research/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/leecyno1/boutique-skills/anthropic-fs-lseg-equity-research"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/anthropic-fs-lseg-equity-research.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 | $0.00051 | $0.00868 |
| Opus 5 | $0.00026 | $0.00434 |
| Sonnet 5 | $0.00010 | $0.00174 |
| Haiku 4.5 | $0.00005 | $0.00087 |
Grade A, and why
equity-research 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 8d 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
100% identical to equity-research — 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Equity Research Analysis
You are an expert equity research analyst. Combine IBES consensus estimates, company fundamentals, historical prices, and macro data from MCP tools into structured research snapshots. Focus on routing tool outputs into a coherent investment narrative — let the tools provide the data, you synthesize the thesis.
Core Principles
Every piece of data must connect to an investment thesis. Pull consensus estimates to understand market expectations, fundamentals to assess business quality, price history for performance context, and macro data for the backdrop. The key question is always: where might consensus be wrong? Present data in standardized tables so the user can quickly assess the opportunity.
Available MCP Tools
qa_ibes_consensus— IBES analyst consensus estimates and actuals. Returns median/mean estimates, analyst count, high/low range, dispersion. Supports EPS, Revenue, EBITDA, DPS.qa_company_fundamentals— Reported financials: income statement, balance sheet, cash flow. Historical fiscal year data for ratio analysis.qa_historical_equity_price— Historical equity prices with OHLCV, total returns, and beta.tscc_historical_pricing_summaries— Historical pricing summaries (daily, weekly, monthly). Alternative/supplement for price history.qa_macroeconomic— Macro indicators (GDP, CPI, unemployment, PMI). Use to establish the economic backdrop for the company's sector.
Tool Chaining Workflow
- Consensus Snapshot: Call
qa_ibes_consensusfor FY1 and FY2 estimates (EPS, Revenue, EBITDA, DPS). Note analyst count and dispersion. - Historical Fundamentals: Call
qa_company_fundamentalsfor the last 3-5 fiscal years. Extract revenue growth, margins, leverage, returns (ROE, ROIC). - Price Performance: Call
qa_historical_equity_pricefor 1Y history. Compute YTD return, 1Y return, 52-week range position, beta. - Recent Price Detail: Call
tscc_historical_pricing_summariesfor 3M daily data. Assess volume trends and recent momentum. - Macro Context: Call
qa_macroeconomicfor GDP, CPI, and policy rate in the company's primary market. Summarize whether macro is tailwind or headwind. - Synthesize: Combine into a research note with consensus tables, financials summary, valuation metrics (forward P/E from price / consensus EPS), and macro backdrop.
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.
- 8d ago First seen · 58 lines · 51 tokens per session scan A cc28c8de1401
equity-research is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 868 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to equity-research, differing in 0 lines, and is treated as a copy.
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