Fda Catalyst Vaccines logo

Fda Catalyst Vaccines

Community
agentii-ai
fda-catalyst-vaccines

Vaccine catalyst analysis across the two-gate path: CBER BLA review and the ACIP recommendation gate that turns FDA approval into commercial availability. Lot release, procurement milestones, pediatric bridging, and healthy-population efficacy context — dated, verified, and sized per event.

Overview

Publisheragentii-ai
Repositoryagentii-investment-intelligence
Skill namefda-catalyst-vaccines
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 Fda Catalyst Vaccines 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/fda-catalyst-vaccines
  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/fda-catalyst-vaccines .claude/skills/fda-catalyst-vaccines
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fda Catalyst Vaccines 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 Fda Catalyst Vaccines 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 Fda Catalyst Vaccines 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 vaccine commercial frameworks; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
forward_window_quarters4Standard catalyst window; extend to 8 on request
include_supersededfalseActive-only events by default; supersede chains on request
acip_gatetrueACIP status always shown next to FDA decisions — approval is not availability
procurement_lenstrueGovernment purchasing and campaign milestones tracked
holiday_labelingtrueHoliday-shifted weeks flagged on every calendar row

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 catalyst queries.

Triggers

  • "When is [vaccine]'s ACIP meeting, and what is the vote outlook?"
  • "Is [vaccine] FDA-approved but still awaiting an ACIP recommendation?"
  • "What does the ACIP vote mean for [ticker]'s commercial launch?"
  • "What procurement milestones are pending for [vaccine/ticker]?"
  • "When is lot release expected for [vaccine]?"
  • "What pediatric or age-cohort bridging studies are pending?"
  • "Build a vaccine catalyst calendar for my watchlist."
  • "Which vaccine decisions land this quarter?"
  • "How do ACIP recommendation categories change uptake?"
  • "What happens between FDA approval and commercial availability?"
  • "Which vaccine catalysts overlap with [ticker]'s earnings date?"
  • "How did similar vaccines trade around their ACIP votes?"

Production Grounding

  • Two-gate path: FDA approval (CBER BLA) is necessary but not sufficient — the ACIP row (scheduled → voted → adopted) is the gate data, with CDC Director sign-off and MMWR publication distinct steps from the vote.
  • ACIP recommendation categories (routine / catch-up / risk-based / shared-clinical-decision-making) set the uptake ceiling; votes move stocks like PDUFA dates move drug names.
  • CBER context: healthy-population efficacy bar (large N, safety-first), lot-release testing, and manufacturing capacity as a catalyst in its own right.
  • Procurement milestones: government stockpiles and purchase contracts (doses/serials, procurement value), seasonal campaign timing.
  • Pediatric bridging, booster cycles, and age-cohort expansions re-open the catalyst window after launch.
  • Holiday-week labeling discipline: campaign-week comps are noisy; label holiday weeks explicitly. Never fabricate votes, dates, or procurement values (per references/knowledge-frameworks.md).

Data Source Priority

  1. ACIP gate data: search_acip_events / get_acip_event (vote counts, status ladder).
  2. FDA/CBER track: get_upcoming_pdufa / search_fda_approvals (BLA events).
  3. Panel and filing context: search_adcom_meetings (VRBPAC) + search_documents / search_sec_filings for procurement and lot-release disclosures.
  4. Historical grounding: search_investment_cases (acip_vote/approval events), search_by_analogue.

Methodology

Retrieval Scope

unstructured_document_search

Retrieval Strategy

  1. Resolve ticker(s) via search_companies; note vaccine product names.
  2. Pull ACIP events (search_acip_events) and FDA-track dates (get_upcoming_pdufa); pair them per product.
  3. For each ACIP event, retrieve vote detail (get_acip_event by vaccine + meeting date) and panel context.
  4. Layer procurement and lot-release milestones from filings (search_sec_filings 8-Ks, search_documents).
  5. Ground the play: search_investment_cases / search_by_analogue with vaccine filters; cite /v/ records.

Temporal Scope

See frontmatter temporal_scope block. Seasonal campaigns may require looking back up to 8 quarters.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

  1. Ticker resolution
  2. Two-gate enumeration (FDA date + ACIP row per product)
  3. Per-event vote framing (recommendation category + history)
  4. Procurement and lot-release overlay
  5. Calendar synthesis + commercial-availability flags

Modes

  • Watchlist (default, multi-ticker): one dated calendar with per-ticker sections, sorted by date.
  • Single ticker deep-dive: full two-gate timeline per vaccine + procurement state.
  • ACIP gate: approval-to-recommendation transition tracking across names.

Tool Fallbacks

FailureFallback
search_acip_events emptysearch_documents keyword ACIP + search_sec_filings; annotate coverage_gap
No ACIP row for a productFlag the unmodeled commercial step explicitly; never assume approval = launch
Procurement data missingState purchase-contract data unavailable; do not estimate values
Knowledge tools emptyProceed with structured data only; annotate knowledge coverage_gap

Output File

{ticker}/{YYYY-MM-DD_HHMM}_fda-catalyst-vaccines_{affix}.md (watchlist: watchlist/{YYYY-MM-DD_HHMM}_fda-catalyst-vaccines-calendar_{affix}.md)

Output Structure

  1. Executive Summary — highest-impact vaccine catalysts in the window, 2-3 sentences
  2. Two-Gate Calendar — dated table: date, ticker, product, FDA event, ACIP row, holiday-week flag, availability framing
  3. Per-Event Analysis — vote context (recommendation category, committee read) + decision history
  4. Procurement & Lot Release — contracts, stockpile status, campaign timing
  5. Historical Analogues — matched vaccine cases with /v/ citations and quantified outcomes
  6. Risk Assessment & Coverage Gaps — binary-risk sizing, two-gate failure modes, missing data

Error Handling

ErrorFallback
No ACIP or PDUFA rowsFall back to earnings + pipeline events; annotate coverage_gap — never fabricate dates
Approval without ACIP rowPresent the gap as the central risk; do not imply commercial availability
Ticker not in med universeResolve via CIK/vaccine-universe match; surface the classification gap

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 Fda Catalyst Vaccines AI skill do?

Vaccine catalyst analysis across the two-gate path: CBER BLA review and the ACIP recommendation gate that turns FDA approval into commercial availability. Lot release, procurement milestones, pediatric bridging, and healthy-population efficacy context — dated, verified, and sized per event.

Why use Fda Catalyst Vaccines on TypingMind?

Because you install it once and use it with any model. Fda Catalyst Vaccines 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 Fda Catalyst Vaccines 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/fda-catalyst-vaccines. 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 Fda Catalyst Vaccines?

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 Fda Catalyst Vaccines?

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

Is the Fda Catalyst Vaccines 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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