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Med Event Takeaways

Community
agentii-ai
med-event-takeaways

Post-event analyst note (conferences, KOL events, site visits) with the professional note anatomy — headline verdict, per-stock takeaways with model numbers, KOL distillation, cross-trial comparison lattices, modeled deltas vs consensus (peak × POS), three what's-changed vectors, risk bullets, and materiality-rated conflicting views. Use after any med event to convert the event into actionable takeaways.

Overview

Publisheragentii-ai
Repositoryagentii-investment-intelligence
Skill namemed-event-takeaways
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 Med Event Takeaways 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/med-event-takeaways
  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/med-event-takeaways .claude/skills/med-event-takeaways
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Med Event Takeaways 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 Med Event Takeaways 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 Med Event Takeaways 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 sell-side note-writing practice; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
note_anatomy9 sectionsheadline → per-stock → KOL → lattices → deltas → vectors → risks → trackers
delta_framingpeak × POSModeled deltas vs consensus are unadjusted peak × POS
conflict_policymateriality-ratedSurface verbatim, rate materiality, or defer to an outcomes trial
vector_setestimates/thesis/positioningThe three what's-changed vectors per covered name

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). Confirm ticker resolution via search_companies before event queries.

Triggers

  • "Write the note on [conference / KOL event]."
  • "What are the takeaways from [event] for my coverage?"
  • "Distill the [conference] KOL panel on [drug/class]."
  • "What changed for [ticker] after [event]?"
  • "Model the delta to consensus from [event]'s data."
  • "Which stocks move on [event] and why?"
  • "Conflict check: what did KOLs disagree on at [event]?"
  • "What do I update first — estimates, thesis, or positioning?"
  • "Summarize the cross-trial comparisons from [event]."
  • "Upside and downside risks after [event]."
  • "Link the living trackers that [event] should update."

Production Grounding

  • Note anatomy is fixed: headline verdict (paragraph one) → per-stock takeaways with model numbers → KOL distillation → raw data + cross-trial lattices → modeled deltas vs consensus (unadjusted peak × POS) → three what's-changed vectors (estimates / thesis / positioning) → risk bullets → living-tracker links.
  • Conflict rule: conflicting views surfaced verbatim, rated by materiality ("incremental positive, not narrative-changing") or deferred to a larger outcomes trial — never silently averaged.
  • Consensus delta: model-vs-consensus always explicit; POS changes logged with reasons; abstain where unquantifiable.
  • Buy-side lens: client-question section, where investor attention sits, per-name bull/bear pivot conditions.
  • Badges: [FACT]/[DEDUCTED]/[VIEW] with the summary table.

Data Source Priority

  1. Event content: transcripts/slides via search_documents + list_sources + read_source_outline / read_source_pages.
  2. Trial context: search_clinical_trials / get_clinical_trial for the studies discussed.
  3. Consensus: search_earnings_calendar (company-level); product consensus derived and labeled [DEDUCTED].
  4. Knowledge: search_investment_cases / search_investment_strategies / search_by_analogue (sectors=med) for pattern grounding and /v/ citations.

Methodology

Retrieval Scope

unstructured_document_search

Retrieval Strategy

  1. Enumerate the event's covered names, sessions, and data presentations.
  2. Retrieve transcripts/abstracts; extract per-presentation data with sources.
  3. Pull each discussed trial's record for lattice construction (vs SoC and class peers).
  4. Model deltas: unadjusted peak × POS per asset vs consensus, assumptions stated.
  5. Ground patterns via search_investment_cases / search_by_analogue (sectors=med); cite /v/.

Temporal Scope

See frontmatter temporal_scope block. Notes cover the meeting window plus the forward quarters its data affects.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

  1. Event inventory
  2. Data extraction (raw numbers + sources)
  3. KOL distillation + conflict capture (verbatim)
  4. Cross-trial lattices
  5. Model deltas vs consensus
  6. What's-changed vectors + risk bullets + tracker links

Modes

  • Conference note (default): multi-day meeting, per-session distillation.
  • KOL / site visit: single-source distillation with conflict emphasis.
  • Single-name note: one covered name with deeper model treatment.
  • Basket: multi-ticker note with per-name sections.

Tool Fallbacks

FailureFallback
Event transcript unavailableDegrade to abstracts/slides; annotate coverage_gap
No consensus figureState required inputs; label the delta [VIEW] — never fabricate
Trial record missingLattice cell marked unavailable; do not guess
Knowledge tools emptyEvent data only; annotate knowledge coverage_gap

Output File

{ticker}/{YYYY-MM-DD_HHMM}_med-event-takeaways_{affix}.md (multi-name: watchlist/{YYYY-MM-DD_HHMM}_med-event-note_{affix}.md)

Output Structure

  1. Headline Verdict — the event in 2-3 sentences
  2. Per-Stock Takeaways — 2-4 lines each, ≥1 model number
  3. KOL Distillation — themes + conflicting views verbatim with materiality ratings
  4. Cross-Trial Lattices — vs standard of care and class peers
  5. Model Deltas vs Consensus — peak × POS arithmetic, [DEDUCTED] labels
  6. What's Changed — estimates / thesis / positioning per name
  7. Risk Bullets — upside and downside
  8. Living-Tracker Links — trackers to append, never rewrite

Error Handling

ErrorFallback
Conflicting KOL viewsSurface both verbatim; rate materiality or defer to an outcomes trial — never average
Consensus absentcoverage_gap with required inputs; keep the internal model consistent
Event scope unclearNarrow to named sessions/presentations; flag what is out of scope

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 Med Event Takeaways AI skill do?

Post-event analyst note (conferences, KOL events, site visits) with the professional note anatomy — headline verdict, per-stock takeaways with model numbers, KOL distillation, cross-trial comparison lattices, modeled deltas vs consensus (peak × POS), three what's-changed vectors, risk bullets, and materiality-rated conflicting views. Use after any med event to convert the event into actionable takeaways.

Why use Med Event Takeaways on TypingMind?

Because you install it once and use it with any model. Med Event Takeaways 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 Med Event Takeaways 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/med-event-takeaways. 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 Med Event Takeaways?

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 Med Event Takeaways?

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

Is the Med Event Takeaways 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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