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Long Short Equity

Community
agentii-ai
long-short-equity

long short equity strategy, pair trade construction, market neutral portfolio, net exposure management, factor neutrality, equity long short, hedge fund strategy, alpha generation, stock picking portfolio construction, long short position management

Overview

Publisheragentii-ai
Repositoryagentii-investment-intelligence
Skill namelong-short-equity
Stars
207
Forks
16
Bundled files
3
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by agentii-ai on GitHub. Read the source before you install it.

Installation

Install the Long Short Equity AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/portfolio-strategy/skills/agentii/long-short-equity
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git /tmp/agentii-investment-intelligence
mkdir -p .claude/skills
cp -r /tmp/agentii-investment-intelligence/plugins/vertical-plugins/portfolio-strategy/skills/agentii/long-short-equity .claude/skills/long-short-equity
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Long Short Equity in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Long Short Equity on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Long Short Equity is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Methodology fused from institutional long/short and buy-side research frameworks; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
parameter_freetrueThis skill has no tunable parameters; analysis scope set by temporal_scope frontmatter

Preflight

Run canonical pre-flight per contracts/preflight.md. Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md — carry the _run_id from your first tool result and name yourself (and your parent, if you were spawned).

Data Source Priority

  1. Strategy methodology — references/strategy-methodology.md (bundled long/short framework)
  2. Strategy frameworks — search_investment_strategies(domain=fundamental)
  3. Historical precedent — search_investment_cases + search_by_analogue(company_situation=...)
  4. Financials — search_xbrl_facts for unit economics and decline-rate evidence

Methodology

Retrieval Scope

structured_only

Retrieval Strategy

Ownership & insider signals: search_institutional_holdings (top-10 holders + whale portfolios, direction=accumulating|reducing|new|exited) and search_insider_trades (Form-4 transactions with SEC URLs) are available as signal inputs.

Branch (a) Structured Data Query from contracts/retrieval.md. Retrieve frameworks via search_investment_strategies, precedent via search_investment_cases and search_by_analogue. Detailed methodology in references/strategy-methodology.md.

Temporal Scope

See frontmatter temporal_scope block.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

Every long/short position rests on an earnings disconnect — a gap between what the market has priced and what the business will deliver. The unifying question across all strategy types is: what does consensus believe, and what do I believe differently? Strategy spectrum, short archetypes, and process detail are in references/strategy-methodology.md.

Consensus is not the published number. Sell-side consensus is a simple average of analyst estimates; buy-side consensus typically moves ahead of it. Trading against the published figure while the real positioning has already shifted is a common and expensive error. Triangulate the effective buy-side expectation before sizing any view.

Steps
  1. Strategy Classification: Place the idea on the spectrum from spread-focused to fundamentals-focused — merger arbitrage (0-18mo), general event-driven (0-24mo), activist (0-24mo), value/deep value (0-5yr+), growth or growth-at-reasonable-price (0-5yr+). The classification sets the expected holding period and the evidence bar. Categories overlap; record the dominant driver rather than forcing a single label.

  2. Consensus Reconstruction: Establish the published sell-side figure, then estimate the effective buy-side expectation. Document the gap and its direction — the disconnect is the position, not the absolute valuation level.

  3. Idea Provenance: Record how the idea was sourced — industry research and trade events, tangential analysis of adjacent markets, practitioner conversations, curated research communities, or quantitative screens. Provenance predicts which failure modes to test for: screen-sourced ideas need qualitative validation, conversation-sourced ideas need independent quantitative confirmation.

  4. Long-Side Thesis: Apply value or growth frameworks per classification. Value requires a discount to net present value on a price basis; growth requires a discount on an earnings basis. State which, and why the market has mispriced it.

  5. Short-Side Archetype (when shorting): Classify into one of three patterns — competition short (complacent incumbent facing a credible new entrant; test the entrant's unit economics and assess whether incumbent management can and will react), consumer euphoria (a product with outsized enthusiasm that will not sustain; test via market sizing and product work), or disappearing business (an incumbent in a structurally terminal market; establish the rate of decline, the cash-flow profile during decline, and whether cash can be redeployed). Each archetype has a distinct evidence requirement — do not substitute a valuation opinion for the archetype test.

  6. Precedent Retrieval: Query search_by_analogue for the matching situation and search_investment_cases for outcome history. Weight precedent by archetype match, not sector match.

  7. Risk and Invalidation: Name what would falsify the thesis and by when. For shorts, additionally record borrow availability, short interest, and days-to-cover — a correct short thesis in a crowded, hard-to-borrow name is not an executable position.

  8. Output: Deliver the classification, the quantified disconnect, the archetype evidence, precedent with /v/ citations, and explicit invalidation conditions.

Output File

{ticker}/{YYYY-MM-DD_HHMM}_long-short-equity_{affix}.md

Output Structure

  1. Executive Summary — strategy classification, the disconnect, and direction in 2-3 sentences
  2. Strategy Classification — position on the spread-to-fundamentals spectrum with expected holding period
  3. Consensus Disconnect — published sell-side figure vs estimated buy-side expectation, with the gap quantified
  4. Idea Provenance — sourcing route and the failure modes it implies
  5. Thesis Detail — long-side value/growth basis, or short-side archetype with its specific evidence test
  6. Historical Analogues — archetype-matched precedent with /v/ citations
  7. Executability — for shorts: borrow, short interest, days-to-cover
  8. Risk and Invalidation — falsification conditions and deadline
  9. Coverage Gaps — data limitations and degraded flags

Error Handling

ErrorFallback
No strategy frameworks availableProceed with references/strategy-methodology.md; flag knowledge_coverage: degraded
Buy-side consensus cannot be triangulatedReport the sell-side figure only and mark the disconnect as unquantified
Borrow / short-interest data unavailableMark the short as not executability-cleared; do not present it as actionable
search_by_analogue returns emptyContinue without precedent; annotate coverage_gap

Final Summary (TUI)

Include ### Key Citations block (0–10 /v/ URLs).

Memory Load

Load prior context before retrieval. See contracts/memory-load.md.

Snapshot

Post-session synthesis. See contracts/snapshot-synthesis.md.

Output Frontmatter

Structured output per contracts/output-frontmatter-schema.md.

References

  • references/strategy-methodology.md
  • references/knowledge-frameworks.md
  • contracts/citation-and-memory.md
  • contracts/retrieval.md
  • contracts/preflight.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Long Short Equity AI skill do?

long short equity strategy, pair trade construction, market neutral portfolio, net exposure management, factor neutrality, equity long short, hedge fund strategy, alpha generation, stock picking portfolio construction, long short position management

Why use Long Short Equity on TypingMind?

Because you install it once and use it with any model. Long Short Equity is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Long Short Equity in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/portfolio-strategy/skills/agentii/long-short-equity. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Long Short Equity?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Long Short Equity?

As many as you like. As long as a model supports skills, you can use Long Short Equity with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Long Short Equity AI skill free?

Yes. It is published on GitHub by agentii-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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