equity-research

equity-research is a skill for Claude Code, Codex from Day1Labs/OpenAva. It costs 50 tokens per session (1,589 once invoked), scanned A, original, MIT.

A structured research workflow for a publicly traded company or stock ticker. It combines market data, company financial results, sentiment signals, and a weighted comparison framework.

In plain words
What is it for?
Use it to investigate a listed company, review its earnings and fundamentals, assess sentiment, and produce a reasoned report without treating uncertain conclusions as facts.
Why use it?
It brings different kinds of investment information into one traceable report and makes missing evidence and risks explicit.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to investigate a listed company, review its earnings and fundamentals, assess sentiment, and produce a reasoned report without treating uncertain conclusions as facts.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/day1labs/openava/equity-research
Install

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.

Any agent
npx skills add Day1Labs/OpenAva --skill equity-research
Clone the repo
git clone --depth 1 https://github.com/Day1Labs/OpenAva

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for equity-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/day1labs/openava/equity-research/github.svg)](https://agentmods.dev/skills/day1labs/openava/equity-research)
Your own site
<a href="https://agentmods.dev/skills/day1labs/openava/equity-research"><img src="https://agentmods.dev/badge/skills/day1labs/openava/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.

agentmods 80×15 button for equity-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/day1labs/openava/equity-research"><img src="https://agentmods.dev/badge/skills/day1labs/openava/equity-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,589 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00050 $0.01589
Opus 5.5 $0.00020 $0.00636
Sonnet 5.5 $0.00010 $0.00318
Haiku 4.5 $0.00005 $0.00159

Measured 2mo ago against content hash 0394a5e480ce, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

OpenAva/Runtime/Agent/Skills/equity-research/SKILL.md · 175 lines

How it starts

The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Equity Research Analyst

Inputs

  • target: Company name or stock ticker (required)
  • market: Trading venue (optional, e.g. US / HK / CN)
  • report_language: Report language (optional, defaults to the user's language)

Skill Goals

  • Deliver a traceable, explainable, and structured deep research report.
  • Keep data, facts, and inference aligned to avoid unsupported conclusions.
  • Make uncertainty and risk boundaries explicit; avoid absolute claims.

Core Execution Principles

  • Stay goal-oriented: dynamically choose equivalent runtime capabilities instead of hard-binding to one fixed method.
  • Facts first, judgment second; every key conclusion must be evidence-backed.
  • Source priority: official IR/earnings disclosures > top-tier financial media > community discussions.
  • If critical data is missing, explicitly state the gap and its impact on conclusions.
  • Separate structural drivers from event-driven shocks, and label their time horizon impact (short/mid term).
  • For policy or geopolitical topics, use multiple sources and avoid single-source narratives.

Goal 1: Build a Reliable Data Baseline (Required)

You must collect and verify the following data (Yahoo Finance basis):

  • Real-time/near real-time market snapshot: current price, daily % change, market cap.
  • Core financial indicators: P/E ratio, EPS.
  • Historical daily series:
    • 5-year price and volume;
    • 12-month price and volume.

Requirements:

  • You must explicitly state: market data may be delayed and is for reference only.
  • If anomalies appear (missing points, trading halt, extreme spikes), explain how they are handled.

Goal 2: Deliver Fundamental Analysis from the Latest Earnings (Required)

Extract key information centered on the latest quarterly earnings, including:

  • Revenue and YoY/QoQ trend;
  • Net income and earnings quality;
  • Performance by key business segments;
  • Management guidance.

Output must include two sections:

  • Highlights
  • Risks

Source priority:

Read the full file on GitHub · 175 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 2mo ago First seen · 175 lines · 50 tokens per session scan A 0394a5e480ce

Subscribe to this mod's changes

equity-research is a skill published in the GitHub repository Day1Labs/OpenAva (11 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 1,589 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

equity-research compared with skills from other repositories

equity-research next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
equity-researchDay1Labs/OpenAvaSkill113mo agoMITA50Claude Code, Codex
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ashare-pre-st-filterHKUDS/Vibe-TradingSkill38kyesterdayMITA89Claude Code, Codex
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etf-analysisHKUDS/Vibe-TradingSkill38kyesterdayMITA39Claude Code, Codex