Earnings Preview Med logo

Earnings Preview Med

Community
agentii-ai
earnings-preview-med

Med-adapted earnings preview: consensus estimates, historical surprises, guidance sensitivities, and the FDA-catalyst overlay (PDUFA/AdCom/trial readouts near the print) for biotech/pharma names.

Overview

Publisheragentii-ai
Repositoryagentii-investment-intelligence
Skill nameearnings-preview-med
Stars
207
Forks
16
Bundled files
2
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.

  • 2 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 Earnings Preview Med 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/marketplace-beachhead/plugins/bio-pharm/skills/agentii/earnings-preview-med
  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/marketplace-beachhead/plugins/bio-pharm/skills/agentii/earnings-preview-med .claude/skills/earnings-preview-med
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Earnings Preview Med 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 Earnings Preview Med 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 Earnings Preview Med 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 inspired by publicly taught earnings-preview frameworks; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
surprise_window8 quartersStandard surprise history window
include_catalyststrueMed prints move on catalysts as much as EPS
guidance_sensitivitytrueGuidance is the swing factor for pharma

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).

Triggers

  • "Preview [biotech ticker]'s upcoming earnings."
  • "What should I expect at [ticker]'s next print?"
  • "How has [ticker] surprised historically?"
  • "Which catalysts land near [ticker]'s earnings date?"
  • "Build an earnings preview with consensus estimates."
  • "What's the guidance risk for [ticker] this quarter?"
  • "Summarize the last few quarters for [ticker]."
  • "Earnings + FDA calendar overlap for [ticker]."
  • "What are the swing factors for [ticker]'s print?"
  • "Historical reaction to [ticker]'s earnings surprises."
  • "What changed in my estimates, thesis, and positioning ahead of [ticker]'s print?"

Production Grounding

  • Med prints have TWO drivers: financials (revenue/EPS/guidance) and catalysts (PDUFA/AdCom/readouts). The catalyst overlay is mandatory — a clean quarter can be undone by a CRL days earlier.
  • Every preview closes with model-vs-consensus deltas (our modeled numbers vs consensus from search_earnings_calendar, signed with the driver named) and the three what's-changed vectors (estimates / thesis / positioning) — estimates move first, thesis and positioning follow only when the facts justify them.
  • For pre-revenue biotechs, the print is mostly about cash runway + pipeline updates; consensus EPS is secondary.
  • Grounding frameworks: references/knowledge-frameworks.md (道/法 review knowledge).

Data Source Priority

  1. search_earnings_calendar — estimates, actuals, surprise history, next date.
  2. search_xbrl_facts — revenue/EPS/margin trends.
  3. search_documents/read_source_* — prior-quarter commentary and guidance.
  4. Knowledge layer: search_investment_cases for historical print reactions.

Methodology

Retrieval Scope

unstructured_document_search

Retrieval Strategy

  1. Resolve the earnings event: search_earnings_calendar for dates/estimates/surprises (consensus values).
  2. Pull fundamentals trend: search_xbrl_facts key line items (our modeled numbers).
  3. Compute model-vs-consensus deltas on each key line item; state assumptions; annotate coverage_gap where no consensus value exists.
  4. Slot-refresh the three what's-changed vectors: estimates / thesis / positioning.
  5. Catalyst overlay: nearest PDUFA/AdCom/readout vs print date.
  6. Ground with historical cases (print reactions) via knowledge tools.

Temporal Scope

See frontmatter temporal_scope block.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

  1. Event & estimates
  2. Fundamental trend
  3. Model-vs-consensus deltas
  4. What's-changed vectors (estimates / thesis / positioning)
  5. Catalyst overlay
  6. Swing-factor synthesis

Modes

  • Standard (default): estimates + surprises + guidance.
  • Pre-revenue: runway + pipeline + readout framing.
  • Catalyst-overlap: print framed around nearby FDA events.

Tool Fallbacks

FailureFallback
search_earnings_calendar emptyUse filings (search_documents) for dates; annotate coverage_gap
No catalyst dataFlag "catalyst overlay unavailable"
Knowledge tools emptyProceed with structured data only

Output File

{ticker}/{YYYY-MM-DD_HHMM}_earnings-preview-med_{affix}.md

Output Structure

  1. Executive Summary — setup for the print in 2-3 sentences
  2. Consensus & Surprise History — estimates table + surprise record
  3. Model vs Consensus — our modeled numbers vs consensus (search_earnings_calendar) with signed deltas and named drivers
  4. What's Changed — the three vectors: estimates / thesis / positioning (estimates move first, ratings last)
  5. Guidance & Swing Factors — guidance risk analysis
  6. Catalyst Overlay — FDA events near the print
  7. Historical Context — cases with /v/ citations
  8. Coverage Gaps — degraded flags

Error Handling

ErrorFallback
Estimates missingPresent fundamentals trend only; flag
Date uncertainUse calendar's best estimate + annotate

Memory Load

See contracts/memory-load.md.

Snapshot

See contracts/snapshot-synthesis.md.

Final Summary (TUI)

Include ### Key Citations block with 0-10 clickable /v/ URLs.

References

  • contracts/citation-and-memory.md
  • contracts/output-frontmatter-schema.md
  • contracts/memory-load.md
  • contracts/snapshot-synthesis.md
  • contracts/preflight.md
  • references/knowledge-frameworks.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 Earnings Preview Med AI skill do?

Med-adapted earnings preview: consensus estimates, historical surprises, guidance sensitivities, and the FDA-catalyst overlay (PDUFA/AdCom/trial readouts near the print) for biotech/pharma names.

Why use Earnings Preview Med on TypingMind?

Because you install it once and use it with any model. Earnings Preview Med 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 Earnings Preview Med in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/marketplace-beachhead/plugins/bio-pharm/skills/agentii/earnings-preview-med. 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 Earnings Preview Med?

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 Earnings Preview Med?

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

Is the Earnings Preview Med 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇