Analyst Kit (YC W23): Turn your Claude / Codex into an investment analyst (Free)

Analyst Kit (YC W23) has released a free, open-source set of hedge-fund-grade equity-research skills for AI coding agents like Claude and Codex. The skills, which include capabilities for SEC filings, financial modeling, and charting, can be installed as plugins to turn AI agents into investment analysts.

Installable, hedge-fund-grade equity-research skills for AI coding agents. Each skill is a self-contained folder of instructions and, where useful, runnable scripts that an agent loads on demand. Install them into Claude Code as a plugin, or copy them into any agent runtime with the bundled installer. The skill frontmatter is the single source of truth — the registry, the plugin manifests, and the installer all derive from it. The skills split into capabilities one atomic job — a data source, an engine, a deliverable, or reusable knowledge and workflows an engagement entry point that orchestrates capabilities via requires: . The "Needs" column lists runtimes, API keys, and required skills. | Skill | Type | What it does | Needs | |---|---|---|---| analyst-playbook | capability | How to structure any analysis before fetching a number: pick the deliverable, align fiscal calendars and frequencies, normalize units, route series to the right skill, and apply per-sector conventions | — | 13f-analysis | capability | Fetch & read U.S. institutional 13F-HR holdings from SEC EDGAR — resolve a fund to its CIK, pull a quarter's holdings as a normalized, ranked CSV, and read it without the common traps | Python stdlib | sec-filings | capability | Fetch & read U.S. SEC filings 10-K, 10-Q, 8-K, any EDGAR form — risk factors, MD&A, material events, segment data, insider trades, earnings 8-K exhibits — with ticker→CIK resolution and BM25 search for large filings | Python stdlib | financialmodellingprep | capability | Call the Financial Modeling Prep REST API — daily prices, news, profiles, screener, quarterly income statements, fiscal-period info, earnings-call transcripts — with exact endpoints, params, and field schemas | Python · FMP API KEY | finmind | capability | Pull Taiwan TWSE/TPEx market data — prices, monthly revenue, financials, dividends, shareholding, institutional flows — via the FinMind API | Python · FINMIND TOKEN | market-intelligence | capability | Nowcast a company's quarter and predict a revenue segment from Google Trends search-interest via SerpAPI — keyword selection, normalization to a quarterly index, and a quarter-to-date nowcast | Python · SERPAPI API KEY | company-universe-manager | capability | Own a watchlist of companies and their key dates earnings, investor days, ex-dividend, AGMs… : roster CRUD, a daily monitor that detects date changes, and a daily brief markdown or branded PDF . Pluggable local-folder or connected-server storage | Python · financialmodellingprep, reporting | analyzing-financial-statements | capability | Calculate & interpret financial ratios profitability, liquidity, leverage, efficiency, valuation, per-share from statement data, with industry benchmarking | Python stdlib | creating-financial-models | capability | DCF valuation, M&A accretion/dilution, sensitivity analysis data tables, tornado charts , and probability-weighted best/base/worst scenario planning | Python · numpy/pandas | charting | capability | Financially-correct charts: a thin Python/Polars layer normalizes data → a TypeScript layer emits Highcharts options + a self-contained HTML page trends, segments, margins, dividends, surprise, waterfalls, price | Node · Python | reporting | capability | Assemble charts, tables, and analyst text into a branded PDF — A4 portrait report or 16:9 deck — from ready-made page templates; remembers your logo and brand colors | Node · charting | wiki-builder | capability | Serve any folder of markdown as a navigable browser wiki sidebar, table of contents, frontmatter chips, ECharts | Bun | data-analysis | capability | End-to-end analysis of a structured dataset CSV/JSON/Excel/SQL — profile, clean, visualize, model, and report with reproducible code | — | single-stock-deep-dive | workflow | Forensic, decision-useful deep dive on one stock: thesis, valuation, catalysts, variant perception, value-chain adjacencies | — | thematic-investing | workflow | Map a theme or trend into an investable value chain — who benefits, where value accrues, what's mispriced | company-universe-manager | technical-analysis | workflow | Disciplined technical analysis with concrete entry/exit levels: regime classification, a three-layer confluence stack, and ATR-based stops/sizing/targets from a zero-dependency indicator engine | Python · charting | company-wiki | workflow | Build a multi-page company-research wiki overview, products, 5-year financials, model, competitors, citations as a deployed web app | FMP API KEY · wiki-builder, company-universe-manager | One command — works on macOS, Linux, and Windows inside WSL2 — see below . It installs all the skills into your chosen runtime and wires them into the agent's system/common prompt. Needs only Node ≥ 18 which detects your OS and installs to the right paths : npx github:mohitjandwani/analyst-kit claude-code or: codex · openclaw · cowork Swap claude-code for codex , openclaw , or cowork ; add --scope project to install into the current project ./.claude/skills , … instead of your home directory. Already cloned the repo? node bin/analyst-kit.js claude-code does the same plus list , doctor , uninstall , or install <skill|persona for just one . For Claude Cowork , the command prints the in-app steps and writes cowork-global-instructions.md to paste into Settings → Cowork → Global instructions — Cowork installs the skills themselves through its plugin marketplace below . On Windows:run insideWSL2— native Windows PowerShell/cmd is unsupported because the skill runtime is POSIX/bash. See Windows: use WSL2 below. Both Claude Code and Claude Cowork Anthropic's desktop app install from the same plugin marketplace: Claude Code: /plugin marketplace add mohitjandwani/analyst-kit /plugin install us-stock-analyst@analyst-kit or international-analyst / taiwan-stock-analyst Claude Cowork desktop app : Customize → Plugins → Personal plugins → + → Add marketplace → mohitjandwani/analyst-kit , add the us-stock-analyst plugin, then enable Settings → Capabilities → Code execution . After installing, ask a trigger phrase e.g. "deep dive on NVDA" and the matching skill loads. From a clone you can also self-test the installers across every platform: npm run test:integration real installs per platform + the path.win32 path-layer check Codex any OS, no ChatGPT login needed — confirm a skill is reachable with an API key: CODEX API KEY=sk-... codex exec --json "use the sec-filings skill to list NVDA's latest 8-K" See compatibility.md for what each runtime does underneath — where skills land, the routing table, and Windows specifics. Analyst Kit's skills run a POSIX/bash runtime analyst-kit-core , so on Windows they are supported only inside WSL2 . This matches what the agents themselves require: Claude Code's sandbox runs on macOS, Linux, and WSL2 native Windows is unsupported , and Codex's Linux mode is WSL2 as well. Recommended: install WSL2 https://learn.microsoft.com/windows/wsl/install , then run Claude Code or Codex and this installer inside your WSL2 distribution — everything then behaves exactly like Linux.- Native Windows PowerShell/cmd cannot run the bash runtime at all. Native Windows + Git Bash will run the scripts but without enforced .env file permissions chmod is a no-op on NTFS and without sandboxing — unsupported. node bin/analyst-kit.js doctor --platform claude-code warns when run on native Windows. Three persona plugins bundle the research workflows for different markets. All three include the research workflows deep dive, thematic, technical analysis, company wiki plus their supporting capabilities charting, reporting, wiki-builder, company-universe-manager, financialmodellingprep, market-intelligence, analyzing-financial-statements, creating-financial-models, data-analysis ; the market difference is whether FinMind Taiwan data and SEC filings are included. | Plugin | Includes | Skills | |---|---|---| us-stock-analyst | the research workflows + supporting capabilities, incl. US filings sec-filings | 15 | international-analyst | the above + FinMind Taiwan/TWSE market data | 16 | taiwan-stock-analyst | Taiwan-focused: workflows + capabilities + FinMind, minus sec-filings | 14 | Run node bin/analyst-kit.js list --persona <name to see a plugin's exact contents. Keys are read from the environment or a git-ignored .env . The installer analyst-kit env / analyst-kit install prompts for anything missing when run interactively. See .env.example /mohitjandwani/analyst-kit/blob/main/.env.example . | Variable | Used by | Get it | |---|---|---| FINMIND TOKEN | finmind | | FMP API KEY https://site.financialmodelingprep.com/developer/docs https://site.financialmodelingprep.com/developer/docs SERPAPI API KEY https://serpapi.com/ https://serpapi.com/ free tier = 100 searches/month 13f-analysis needs no key — it reads SEC EDGAR directly set the optional SEC EDGAR UA contact string as an SEC fair-access courtesy . Skills that run code bootstrap their own dependencies on first use. Runtimes are a per-skill prerequisite the installer does not install for you: Python finmind, company-universe-manager; 13f-analysis is standard-library only and Bun wiki-builder . Every skill runs on a shared runtime analyst-kit-core , installed automatically as a dependency that keeps all per-user state in one fixed place, ~/.analyst-kit/ : .env — your API keys chmod 600, shared across projects config — settings analyst-kit-core/bin/analyst-kit-config get|set|list analytics/skill-usage.jsonl — local usage log: which skill ran, when, outcome, duration learnings.jsonl — things the skills learned about your setup and preferences, so mistakes aren't repeated Telemetry is on by default anonymous, opt-out . It sends only skill name, version, outcome, and duration — never repo names, file paths, tickers, or content — and is what tells us which skills break or run slow, so keeping it on directly improves your experience. You're told about it once on first run. Tiers: community default, stable anonymous id , anonymous no id , off . Opt out any time: ~/.claude/skills/analyst-kit-core/bin/analyst-kit-config set telemetry off Updates: skills check the published version at most once a day and offer a guided upgrade when a new release is out declining snoozes it for a week; disable with analyst-kit-config set update check false . Each skill is skills/<name /SKILL.md with YAML frontmatter: --- name: 13f-analysis kebab-case; must equal the folder name type: capability capability | workflow description: what it does + a "Triggers:" clause of trigger phrases ... Triggers: "get the 13F for X", "what does <fund own", ... requires: ... capability skills this one builds on nothing may require a workflow env: FMP API KEY API keys the skill needs --- Agents read only name + description , so trigger phrases live inside the description. type , requires , and env drive the installer and validator. npm run validate lint skills + plugin manifests + preamble sync check npm run build:registry regenerate registry.json from frontmatter npm run check:registry verify registry.json is in sync npm run sync:preamble regenerate the analyst-kit-core blocks in every SKILL.md registry.json is generated — edit skill frontmatter, then rebuild it. The < -- analyst-kit:preamble/epilogue -- blocks in each SKILL.md are also generated — edit skills/analyst-kit-core/templates/ and re-sync; never edit between the markers. The same checks run in CI .github/workflows/validate.yml on every push and pull request. Planned skills, not yet available: an LBO model debt schedule, cash sweep, IRR/MOIC and PDF report analysis. and the DCF + sensitivity tooling in analyzing-financial-statements were inspired by the custom financial skills in creating-financial-models Anthropic's claude-cookbooks https://github.com/anthropics/claude-cookbooks/tree/main/skills/custom skills , then reworked to this repo's skill contract frontmatter, scripts/ layout and hardened with input guards and a test suite.- The M&A accretion/dilution model in was inspired by creating-financial-models joe-neary/MergerDealSimulator https://github.com/joe-neary/MergerDealSimulator . The financial formulas were reimplemented from scratch no code was copied ; that project's worked example serves as an independent cross-check in the skill's test suite. MIT © Mohit Kumar