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 — 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 — 7-day free trial, 2,000 credits, no credit card.

2. Global MCP Setup

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 <YOUR_KEY>"}}'

Writes to ~/.claude.json. Restart Claude Code — 30+ tools auto-discover on every session from any directory.

3. Install Skills (Primary: Local Copy)

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.

claude plugin marketplace add agentii-ai/agentii-investment-intelligence
claude plugin install models-and-pitches     # adds /models-and-pitches:* namespace
# ... etc for other verticals

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

Skills

Skills auto-activate when trigger phrases match. Each is a skills/agentii/<name>/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 →

equity-research-core (9 skills)

models-and-pitches (9 skills)

quantitative-analysis (5 skills)

business-intelligence (4 skills)

industry-analysis (4 skills)

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 →

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<N> (PDF) / fda<N> (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:

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.

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 → | Request a ticker →

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

7-day free trial, 2,000 credits, no credit card required. Early adopter pricing — your rate stays as coverage grows. Full pricing →

For Other CLI Agents

OpenCode / Codex / Goose / OpenClaw

All 48 skills use the open Agent Skills standard (skills/agentii/<name>/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.

# Recommended: install the full agentii namespace
cp -r plugins/agentii-plugin/skills/agentii ~/.claude/skills/agentii/    # Claude Code
cp -r plugins/agentii-plugin/skills/agentii ~/.config/opencode/skills/   # OpenCode
cp -r plugins/agentii-plugin/skills/agentii ~/.codex/skills/             # Codex
cp -r plugins/agentii-plugin/skills/agentii ~/.config/goose/skills/      # Goose
openclaw add ./plugins/agentii-plugin                                      # OpenClaw

# Or: single vertical for lightweight installs
cp -r plugins/vertical-plugins/equity-research-core/skills/agentii ~/.config/opencode/skills/

See adapters/ for per-CLI configuration files. All agents benefit from ai-agents.txt at the repo root.

Making It Yours

• Bring your templates — mount firm-branded .pptx templates at ./templates/ for pitch-deck and earnings-preview
• Adjust methodology — edit ## Defaults tables and references/institutional-defaults.md
• Override via style.md — per-workspace style.md overrides defaults for lookback quarters, reporting currency, peer universe, and output verbosity
• Chain skills — dcf → pitch-deck for end-to-end model-to-deck workflows; xlsx-financials → audit-xls for quality assurance
• Edit skills in plugins/vertical-plugins/<vertical>/skills/agentii/<name>/SKILL.md — the single canonical source
• Sync changes: python3 scripts/sync-agent-skills.py then bash scripts/assemble-agentii-namespace.sh
• Run CI checks: python3 scripts/check.py before pushing — validates manifests, frontmatter, CI gates, and cross-file consistency

Troubleshooting

Repository Structure

agentii-investment-intelligence/
├── plugins/
│   ├── agentii-plugin/                  # Meta-plugin: /agentii:* surface (48 symlinked skills)
│   ├── vertical-plugins/
│   │   ├── equity-research-core/        # 9 skills
│   │   ├── business-intelligence/       # 4 skills
│   │   ├── industry-analysis/           # 4 skills
│   │   ├── models-and-pitches/          # 9 skills
│   │   └── quantitative-analysis/       # 5 skills
│   └── agent-plugins/
│       └── agentii-equity-agent/        # Managed agent bundle
├── contracts/                           # 21 shared contracts (single source of truth)
├── scripts/                             # CI gates, sync, validation, assembly
├── docs/install/                        # Per-CLI install guides
├── style.md                             # Package-shipped formatt