---
name: Investment Intelligence
slug: investment-intelligence
category: AI Engineering
description: Investment Intelligence provides institutional-style equity research workflows for AI agents using SEC filings, XBRL financials, company profiles, and earnings data. Use it for analyst-style research, models, and pitch materials with citation-backed retrieval and office output generation.
github: "https://github.com/agentii-ai/agentii-investment-intelligence"
language: Python
stars: 203
forks: 16
install: "npx degit https://github.com/agentii-ai/agentii-investment-intelligence ~/.claude/skills/agentii-investment-intelligence"
installs_to: ~/.claude/skills/agentii-investment-intelligence
source_path: SKILL.md
collection_size: 21
category_size: 3101
collection_url: "https://dirskills.com/collections/agentii-ai/agentii-investment-intelligence"
added: 2026-09-05T05:29:44.091Z
last_synced: 2026-09-05T05:29:44.091Z
canonical_url: "https://dirskills.com/skills/investment-intelligence"
---
# Investment Intelligence
Investment Intelligence provides institutional-style equity research workflows for AI agents using SEC filings, XBRL financials, company profiles, and earnings data. Use it for analyst-style research, models, and pitch materials with citation-backed retrieval and office output generation.
**Install:**
```bash
npx degit https://github.com/agentii-ai/agentii-investment-intelligence ~/.claude/skills/agentii-investment-intelligence
```
## README
# agentii-investment-intelligence
The financial data layer for AI agents.
Open-source alternative to FactSet / Daloopa / S&P Global for AI agents.
1,146+ US equities with full SEC filing history. 48 Claude-type skills. 20+ MCP tools.
One API key. Zero infrastructure. Single entrance: /agentii:skill-name.
---
## Why agentii
Wall Street pays $30K+/seat/year for FactSet, Bloomberg, and S&P Global. Those platforms were built for humans clicking through terminals. AI agents need **agent-use-ready data** — structured, citation-backed, page-addressable, API-delivered.
agentii.ai ingests every SEC filing (10-K, 10-Q, 8-K, 20-F, 6-K) and 15K+ earnings call transcripts (2022+) into a Neon PostgreSQL data plane with 15.99M XBRL facts, 51K+ source documents, and 1.34M+ parsed pages. A single `agentii` MCP server at `mcp.agentii.ai` exposes 30+ tools (incl. institutional ownership + insider activity) that Claude Code, OpenCode, Goose, Codex, OpenClaw, and Claude Cowork consume natively.
This repository mirrors [`anthropics/financial-services`](https://github.com/anthropics/financial-services) — marketplace plugin system, vertical skill decomposition, agent-plugin bundling. The difference: all skills point at a **single `agentii` MCP server** backed by agentii.ai's own data plane. There is no second MCP — office output uses the same **code-mode** approach (Python + LibreOffice, invoked via `Bash`) that Anthropic's package uses.
> [!IMPORTANT]
> Nothing in this repository constitutes investment, legal, tax, or accounting advice. These skills produce analyst work product for review by a qualified professional. Every output is staged for human sign-off.
---
## Quick Install
### 1. Get an API Key
[agentii.ai/api-keys](https://agentii.ai/api-keys) — 7-day free trial, 2,000 credits, no credit card.
### 2. Global MCP Setup
```bash
export AGENTII_API_KEY=sk_live_YOUR_KEY_HERE
claude mcp add-json --scope user agentii \
'{"type":"http","url":"https://mcp.agentii.ai/mcp","headers":{"Authorization":"Bearer "}}'
```
Writes to `~/.claude.json`. Restart Claude Code — 30+ tools auto-discover on every session from any directory.
### 3. Install Skills (Primary: Local Copy)
```bash
bash scripts/copy-skills-local.sh # Copies all 48 skills + commands to ~/.claude/
```
Restart Claude Code — all 48 skills register under a **single unified namespace, `/agentii:skill-name`** (skills land in `~/.claude/skills/agentii/`, commands in `~/.claude/commands/agentii/`). This is the **recommended install method** — one namespace, no per-vertical prefixes, and it works reliably on all Claude Code versions.
> **Single namespace by design**: the local-copy path exposes *only* `/agentii:*`. There is no `/equity-research-core:*` or `/models-and-pitches:*` surface — every skill is reached the same way regardless of which vertical authored it.
Advanced (not recommended): per-vertical plugin installs
Installing the vertical plugins individually creates **additional** `/vertical:skill` namespaces (e.g. `/equity-research-core:risk`) alongside `/agentii:*`. Prefer the local-copy path above for a clean single namespace.
```bash
claude plugin marketplace add agentii-ai/agentii-investment-intelligence
claude plugin install models-and-pitches # adds /models-and-pitches:* namespace
# ... etc for other verticals
```
> **Future path**: `claude plugin install agentii@agentii-investment-intelligence` for the unified meta-plugin (single `/agentii:*` namespace). Currently blocked by [Claude Code issue #15178](https://github.com/anthropics/claude-code/issues/15178); use `bash scripts/copy-skills-local.sh` until the plugin bug is fixed.
### 4. Verify
```
/agentii:recent-quarter LLY
```
Expected: structured, citation-backed report with real SEC filing data and clickable citations like `[📄 LLY 10-K p.42](https://agentii.ai/v/LLY/sec129/42)` — every material fact is immediately followed by its source link. The closing TUI reply includes a **Key Citations** block of clickable URLs so you can cmd+click straight to the exact SEC page.
---
## What's Inside
| Component | Description |
|-----------|-------------|
| **Skills** | 48 Claude-type skills across 12 verticals — trigger-phrase auto-activation + `/agentii:skill-name` single entrance |
| **Meta-Plugin** | `plugins/agentii-plugin/` — unified install bundles all 48 skills under `/agentii:*` namespace (symlinked from verticals) |
| **Agent Plugin** | `agentii-equity-agent` — managed agent with the `system_v2_7`-ported system prompt, three-layer retrieval protocol, and citation discipline |
| **MCP Tools** | 30+ tools at `mcp.agentii.ai/mcp` — SEC filings, XBRL financials, entity search, earnings calendar, two-tier page outline, real-time quotes |
| **Office Output** | Code-mode: `openpyxl` (Excel .xlsx), `python-pptx` (PowerPoint .pptx), `python-docx` (Word .docx) + LibreOffice headless recalc — no office MCP server |
| **Citations** | Every fact links to `agentii.ai/v/{ticker}/{citation_id}/{page}` — clickable, verifiable, inline-after-fact + TUI Key Citations block |
| **Workspace Memory** | `agentii.md` index, per-ticker outputs with YAML frontmatter, `snapshots/` thesis synthesis with `[FACT]`/`[DEDUCTED]`/`[VIEW]` taxonomy, `sessions/` archive |
| **Contracts** | 21 shared contracts in `contracts/` — single source of truth for retrieval protocol, citations, office tooling, preflight, memory, and tracing |
| **Instant Data** (spec 039) | `data-tools/` — zero-key-first macro/market/earnings tools behind `~~category` placeholders, AGENT_CONTRACT envelope, file cache + failover; opt-in `setup_credentials.py` wizard for free API keys |
| **Enrichment & Quality** (spec 039) | `skill-registry.yaml` + `scripts/enhance-skill.py` (YAML workflow presets) + `scripts/quality-scan.py` (5-dimension 0–10 score, CI gate) |
| **Packaging** (spec 039) | `packaging/export.py` emits codex/cowork/generic-cli variants from canonical SKILL.md (diff-clean, placeholders preserved) |
---
## Skills
Skills auto-activate when trigger phrases match. Each is a `skills/agentii//SKILL.md` file with YAML frontmatter and markdown methodology — the single canonical artifact across all 6 CLI hosts (Claude Code, OpenCode, Codex, OpenClaw, Goose, Claude Cowork). Thin `commands/*.md` wrappers are also shipped per vertical for explicit `/agentii:skill-name` slash-command invocation.
Deep methodology and output structure live in per-skill `references/` directories (progressive disclosure) — the SKILL.md body stays lean (~700–900 words), and depth loads on demand. [Full methodology →](./contracts/skill-methodology-template.md)
### equity-research-core (9 skills)
| Command | Description |
|---------|-------------|
| `/agentii:recent-quarter` | Quarterly P&L progression, margin drivers, EPS vs consensus, sequential momentum |
| `/agentii:business-model` | Revenue model classification, product-line decomposition, distribution channels, customer segments |
| `/agentii:competitive` | Peer positioning, market-share dynamics, moat assessment, pricing power |
| `/agentii:growth-strategy` | Organic/inorganic growth decomposition, pipeline analysis, execution tracking |
| `/agentii:secular-trends` | Technology adoption cycles, disruption risk, strategic positioning |
| `/agentii:turnaround` | Performance inflection detection, operational metrics, leadership impact |
| `/agentii:risk` | Regulatory, competitive, macro, and technology risk assessment |
| `/agentii:earnings-sentiment` | Analyst estimates vs. guidance, sentiment trends, surprise history |
| `/agentii:valuation-methods` | Multiples, DCF inputs, PEG integration, valuation assumption extraction |
### models-and-pitches (9 skills)
| Command | Description |
|---------|-------------|
| `/agentii:dcf` | DCF valuation with live formulas, WACC decomposition, sensitivity tables → `.xlsx` |
| `/agentii:comps` | Trading comps with statistical benchmarking (mean/median/high/low) → `.xlsx` |
| `/agentii:3-statement` | Integrated IS/BS/CF with cross-statement balancing via XBRL calculation arcs → `.xlsx` |
| `/agentii:lbo` | LBO with sources & uses, debt schedule, returns waterfall → `.xlsx` |
| `/agentii:sotp-valuation` | Sum-of-the-parts valuation with segment-level multiples |
| `/agentii:audit-xls` | Workbook auditor: formula errors, hardcoded cells, calculation arc cross-validation |
| `/agentii:xlsx-financials` | XBRL-to-Excel with number formatting, frozen headers, named ranges, Checks tab → `.xlsx` |
| `/agentii:pitch-deck` | 12–16 slide investment thesis presentation → `.pptx` (`.md` fallback) |
| `/agentii:earnings-preview` | 4–6 slide earnings preview with consensus estimates, surprises, catalysts → `.pptx` (`.md` fallback) |
### quantitative-analysis (5 skills)
| Command | Description |
|---------|-------------|
| `/agentii:ratio-analysis` | 24 financial ratios across 6 categories with cross-company comparison |
| `/agentii:peg-valuation` | PEG = P/E ÷ Growth Rate with Peter Lynch thresholds and sector benchmarks |
| `/agentii:reverse-dcf` | Solve for implied growth rate/margins from current price |
| `/agentii:ddm-valuation` | Multi-stage Dividend Discount Model for mature dividend payers and financials |
| `/agentii:residual-income` | Book Value + PV of future economic profit — for banks, insurers, capital-intensive firms |
### business-intelligence (4 skills)
| Command | Description |
|---------|-------------|
| `/agentii:revenue-decomp` | Segment breakdown, geographic split, product-line waterfall |
| `/agentii:unit-economics` | CAC/LTV estimation, churn inference, gross margin per unit |
| `/agentii:what-if` | Scenario tree (bear/base/bull), probability-weighted EV, sensitivity matrix |
| `/agentii:operational-kpi` | Headcount trends, utilization rates, backlog/book-to-bill |
### industry-analysis (4 skills)
| Command | Description |
|---------|-------------|
| `/agentii:peer-bench` | Multi-ticker financial comparison, growth/value matrix, z-score ranking |
| `/agentii:sector-overview` | TAM estimation, competitive concentration (HHI), regulatory landscape |
| `/agentii:competitive-positioning` | Porter's Five Forces, strategic group mapping, differentiation analysis |
| `/agentii:supply-chain` | Supplier/customer dependency, geographic concentration, bottleneck identification |
All valuation skills support `--mode=scenario` for Bear/Base/Bull probability-weighted analysis.
---
## Office Output (Code-Mode + LibreOffice)
v2.3.1 adopts Anthropic's proven code-mode architecture — no office MCP server. The agent writes self-contained Python scripts and executes them via `Bash`. [Full contract →](./contracts/office-tooling.md)
| Format | Library | Primary | Degraded Fallback |
|--------|---------|---------|-------------------|
| **Excel** | `openpyxl` + LibreOffice recalc | `.xlsx` with live formulas, named ranges, Checks tab | `.md` with full data tables |
| **PowerPoint** | `python-pptx` + LibreOffice validation | `.pptx` with one idea/slide, sourced footers | `.md` slide specification |
| **Word** | `python-docx` (available, deferred) | `.docx` for memo/IC-note deliverables | `.md` (default until memo skill ships) |
**Conventions** (mirroring Anthropic `xlsx-author`): blue font = hardcoded input, black = formula, green = cross-sheet link. A `Checks` tab carries TRUE/FALSE validation ties. LibreOffice headless (`soffice --headless`) handles recalculation and PDF export. The formulas-over-hardcodes invariant (`hardcoded_count == 0` for projection/discount/PV cells) is mandatory per FR-020.
**Layered preflight (FR-043)**: skills probe for a live Office session (Cowork `mcp__office__*` tools) first, then fall back to Python + LibreOffice, then degrade to `.md` with the exact `pip install` remediation command. Never a silent failure.
---
## Workspace Memory
v2.3.1 introduces a file-first hybrid memory architecture that persists context across sessions. After running skills, your workspace looks like this:
```
workspace/
├── agentii.md # Project memory index (YAML frontmatter + markdown table)
├── style.md # Optional workspace overrides (currency, peers, verbosity)
├── NVDA/
│ ├── 2026-06-15_0930_recent-quarter_summary.md # Tier 1: per-skill outputs with YAML frontmatter
│ └── 2026-06-15_1045_dcf_base.xlsx # Office artifacts
├── snapshots/
│ └── NVDA/
│ └── 2026-06-15_thesis.md # Tier 2: cross-skill synthesis (auto-triggered at ≥2 skills)
├── sessions/
│ ├── INDEX.md # Session index (auto-loaded)
│ └── 2026-06-15/ # Full transcripts (on-demand only)
├── _cross/ # Multi-ticker analyses (peer-bench, comps, competitive-positioning)
│ └── semis_2026-06-15_1400_peer-bench_nvda-amd-avgo.md
└── _sector/ # Pure sector/thematic analyses
└── tech.semiconductors/
└── 2026-06-15_1500_sector-overview_summary.md
```
**Key conventions:**
- **`agentii.md`**: YAML frontmatter header (machine-parseable via `head -30`) + Markdown summary table (human-readable). Appended after every skill run.
- **`{ticker}/`**: Tier-1 per-skill outputs with structured YAML frontmatter (`key_metrics`, `conclusions`, `facts_count`, `deducted_count`, `views_count`, `citation_count`).
- **`snapshots/{ticker}/`**: Tier-2 thesis synthesis auto-triggered when ≥2 skills run on the same ticker in a session. Distills cross-skill conclusions with `[FACT]`/`[DEDUCTED]`/`[VIEW]` classification.
- **`_cross/`**: Multi-ticker outputs for peer comparisons and cross-company analyses.
- **`_sector/`**: Pure industry/thematic analyses with no primary ticker.
- **`sessions/`**: Full transcripts archived by date (not auto-loaded); `INDEX.md` lists all sessions.
---
## Citations: Page-Accurate Provenance
Every material fact, table row, and metric in a deliverable is immediately followed by its clickable source link — not deferred to a bottom appendix. **Inline-first placement** is the package's core UVP.
```
Revenue grew 22% YoY to $215.9B [📄 NVDA 10-K p.42](https://agentii.ai/v/NVDA/sec173/42)
```
The bottom `## Citations` section provides a non-duplicative roll-up index. The closing TUI reply includes a **Key Citations** block (0–10 clickable URLs) so you can cmd+click straight to the exact SEC page without opening the deliverable file.
**Citation format**: `https://agentii.ai/v/{ticker}/{citation_id}/{N}` — path-based, ~7 tokens, browser-redirects to the exact filing page. Non-SEC sources use `ref` (PDF) / `fda` (FDA) prefixes.
---
## Data-Source Priority
Every skill follows a mandatory data-source ordering (FR-075):
1. **XBRL facts FIRST** (grounding truth) — `search_xbrl_facts` with `view=detailed` for segment/product/channel breakdowns
2. **SEC filings SECOND** — 10-K (annual), 10-Q (quarterly), 20-F/6-K (foreign) via the three-layer retrieval protocol
3. **Web search LAST RESORT** — only when both XBRL and SEC filings are insufficient; flagged `web_search_used: true` in frontmatter; non-authoritative
---
## Three-Layer Retrieval Protocol
Skills that search unstructured documents at scale follow a mandatory protocol codified in `contracts/retrieval.md`:
| Layer | Tool | What It Returns |
|-------|------|-----------------|
| **1 — Document Discovery** | `search_documents`, `search_sec_filings` | Candidate filings by ticker, form_type, date, labels |
| **2 — Page Map** | `read_source_outline` (lightweight) → `read_source_deep_outline` (escalation) | Page descriptions + keywords; NULL = skip (cover/TOC/legal) |
| **3 — Deep Read** | `read_source_pages` | Full `page_content` with `[[Table{idx}]]` markers for the 3–5 selected pages only |
`search_cross_period` is the primary multi-period path for skills analyzing 4+ fiscal quarters.
---
## Coverage
**1,146+ US public companies** across med + tech + industrial + fin + consumer sectors, with full SEC filing history from 2022 onward (10-K, 10-Q, 8-K, 20-F, 6-K) and earnings call transcripts (2022+). Every data point carries a clickable citation watermark linking to the original filing page.
| Sector | Example Tickers |
|--------|-----------------|
| Technology / Semiconductors | NVDA, AMD, AVGO, MSFT, AAPL, CRM, ORCL, INTC |
| Healthcare / Biotech / Pharma | LLY, ABBV, JNJ, PFE, MRK, BMY, UNH |
| Financials | JPM, BAC, GS, MS, V, MA |
| Consumer / Retail | AMZN, WMT, COST, HD, NKE, TSLA |
| Industrials / Energy / Materials | GE, CAT, XOM, BA, RTX, LMT |
| Communication / Media | META, GOOG, NFLX, DIS, T, VZ |
**Data volume**: 15.99M XBRL facts, 51K+ source documents (SEC filings + earnings call transcripts), 1.34M+ parsed silver pages. XBRL facts updated daily via Dagster pipeline. SEC filings indexed within hours of EDGAR publication. [Full coverage →](https://agentii.ai/coverage) | [Request a ticker →](https://agentii.ai/request-data)
Skills surface a `data_freshness` warning for tickers with < 100% coverage and refuse to fabricate data outside the launch cohort.
---
## Architecture
```
┌─────────────────┐ ┌──────────────────┐ ┌────────────────────┐
│ AI Agent │ │ MCP Server │ │ REST API │
│ (Claude Code, │ ──► │ mcp.agentii.ai │ ──► │ api.agentii.ai │
│ OpenCode, etc) │ │ 30+ tools │ │ Hono + Vercel │
└─────────────────┘ └──────────────────┘ └────────┬───────────┘
│
┌───────────────────────────────┤
│ │
┌─────▼──────┐ ┌─────▼──────┐
│ Neon │ │ Redis │
│ PostgreSQL│ │ (Upstash) │
│ 4.17M │ │ tracing │
│ XBRL facts│ │ hot tier │
└────────────┘ └────────────┘
```
**Data plane**: Neon PostgreSQL (product data — XBRL facts, companies, filings, entity aliases). **Tracing plane**: Redis Upstash (hot, 7d TTL) + Supabase PostgreSQL (cold, audit). **Office**: code-mode Python + LibreOffice — no office MCP server.
One MCP server. One API key. Zero infrastructure.
---
## Pricing
| Plan | Monthly | Credits/mo | Overage |
|------|---------|------------|---------|
| **Starter** | $19.90/mo | 2,000 | $5/1,000 credits |
| **Pro** | $39.90/mo | 10,000 | $5/1,000 credits |
| **Enterprise** | Custom | 500,000+ | Custom |
7-day free trial, 2,000 credits, no credit card required. Early adopter pricing — your rate stays as coverage grows. [Full pricing →](https://agentii.ai/pricing)
---
## For Other CLI Agents
### OpenCode / Codex / Goose / OpenClaw
All 48 skills use the open Agent Skills standard (`skills/agentii//SKILL.md`) — works identically across all 6 CLI hosts. The `agentii` MCP entry is replicated in each vertical's `.mcp.json` — host CLIs deduplicate by server name.
```bash
# Recommended: insta