# Full Equity Research

> The Auto 档 scenario — a soft-plan orchestrator for a complete fundamental equity research thesis: understand → financials → valuation → synthesize. The body is a soft plan (stages), not a rigid DAG; correctness is enforced by each referenced skill's own requires: preconditions, never by this plan's rigidity (Q2).

- Skill: `agentii-ai/full-equity-research` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add agentii-ai/full-equity-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/agentii-ai/full-equity-research/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: agentii-ai (https://skillmd.com/u/agentii-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/agentii-ai/full-equity-research

---


# full-equity-research

```yaml
scenario: full-equity-research
stages:
  - skill: business-model
  - skill: competitive
  - skill: recent-quarter
  - skill: valuation-methods
  - skill: dcf
notes: >-
  Foreign issuers use 20-F/6-K instead of 10-K/8-K; small caps may skip dcf.
  Dispatch each stage as its own subagent (context isolation, Q2); handoff
  passes artifact PATHS, never content (Q13 rule 3).
```

Validated by `validate_scenes.py`: registry-key references + acyclicity.