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Pipeline Vaccines

Community
agentii-ai
pipeline-vaccines

Vaccine pipeline analysis: immunogenicity and seroconversion studies, lot-consistency trials, healthy-population efficacy designs, and age-cohort bridging, with the ACIP gate built into the value path across the med universe.

Overview

Publisheragentii-ai
Repositoryagentii-investment-intelligence
Skill namepipeline-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 Pipeline 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/pipeline-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/pipeline-vaccines .claude/skills/pipeline-vaccines
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pipeline 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 Pipeline 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 Pipeline 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 development frameworks; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
asset_scopeall disclosed vaccine assetsFull-pipeline enumeration first
evidence_lensimmunogenicity + efficacyCorrelate studies distinguished from efficacy trials
acip_gatetrueACIP path modeled as part of pipeline value
cohort_lenstrueAge-cohort bridging tracked per asset
value_framerisk-adjustedModeled value = unadjusted peak x POS, both shown

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 asset queries.

Triggers

  • "Analyze [ticker]'s vaccine pipeline."
  • "What seroconversion or immunogenicity studies support [vaccine]?"
  • "Where are [ticker]'s vaccines in the efficacy ladder?"
  • "Which lot-consistency trials are pending for [ticker]?"
  • "What age-cohort bridging studies does [ticker] have planned?"
  • "Does [ticker]'s vaccine have an ACIP path?"
  • "Risk-adjust [ticker]'s vaccine pipeline."
  • "What is the next catalyst for each of [ticker]'s vaccine assets?"
  • "How do healthy-population efficacy designs change the evidence bar?"
  • "Map [ticker]'s vaccines by antigen, cohort, and phase."
  • "Which vaccine assets could reach a BLA in the next two years?"
  • "What does [ticker]'s pipeline say about its seasonal strategy?"

Production Grounding

  • Vaccine evidence ladder: immunogenicity/seroconversion studies (correlates, not efficacy) → efficacy trials in healthy populations (large N, safety-first) → lot-consistency trials (immunogenicity equivalence across three manufacturing lots — a licensure gate) → age-cohort bridging for pediatric and elderly label expansion.
  • Healthy-population design: the efficacy bar is safety-first; endpoints prevent infection or disease; power comes from large N, and placebo arms shrink post-licensure (non-inferiority and bridging designs follow).
  • Lot consistency is a regulatory milestone, not a formality: without it, licensure stalls regardless of efficacy data.
  • The ACIP gate is part of pipeline value: a Phase III vaccine without a plausible ACIP path carries an unmodeled commercial step.
  • Booster cycles and variant updates re-open the market; cohort bridging extends the label beyond the pivotal population.
  • Grounding detail lives in references/knowledge-frameworks.md.

Data Source Priority

  1. get_company_drugs / search_universe_drugs — vaccine asset inventory.
  2. search_clinical_trials / get_clinical_trial — study design type, cohorts, status-diff, enrollment.
  3. search_acip_events / get_acip_event — the gate path per vaccine; search_fda_approvals for BLA history.
  4. Knowledge layer: search_knowledge_entries for framework grounding.

Methodology

Retrieval Scope

structured_only

Retrieval Strategy

  1. Pull vaccine assets (get_company_drugs), cross-check the universe (search_universe_drugs).
  2. Enrich per asset with trial records (search_clinical_trials) — design type, cohorts, status.
  3. Map the ACIP path per asset (search_acip_events / get_acip_event).
  4. Layer BLA and approval history (search_fda_approvals).
  5. Size each asset (peak x POS); ground in knowledge entries.

Temporal Scope

See frontmatter temporal_scope block. Seasonal and bridging cycles span years; history may reach back 8-12 quarters.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

  1. Asset enumeration
  2. Evidence-ladder mapping (immunogenicity → efficacy → lot consistency → bridging)
  3. ACIP-path assessment
  4. Cohort and booster overlay
  5. Risk-adjusted synthesis

Modes

  • Phase scan (default): all vaccine assets by stage with next catalyst and ACIP path.
  • Cohort bridge: age-cohort expansions, booster cycles, and label breadth.
  • Competitor map: antigen and indication overlap with class-read.

Tool Fallbacks

FailureFallback
get_company_drugs emptysearch_universe_drugs by company or indication; annotate coverage_gap
No trial rowsMark design type undisclosed; do not guess efficacy vs immunogenicity
No ACIP rowsFlag the gate path unmodeled; never assume approval = availability
Knowledge tools emptyProceed with structured data only

Output File

{ticker}/{YYYY-MM-DD_HHMM}_pipeline-vaccines_{affix}.md

Output Structure

  1. Executive Summary — pipeline stance in 2-3 sentences
  2. Asset Table — stage, design type, cohorts, next catalyst, ACIP path
  3. Evidence Ladder — immunogenicity vs efficacy vs lot-consistency status per asset
  4. Cohort & Booster Map — bridging studies, label breadth, seasonal strategy
  5. Risk-Adjusted Sizing — peak x POS with the two-gate path made explicit
  6. Coverage Gaps — missing records, undisclosed design, degraded modes

Error Handling

ErrorFallback
Study design unknownMark "undisclosed"; do not guess
Efficacy vs correlate ambiguityState both readings; never call seroconversion efficacy
No catalysts foundSay so explicitly; note the pipeline may be early-stage

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 Pipeline Vaccines AI skill do?

Vaccine pipeline analysis: immunogenicity and seroconversion studies, lot-consistency trials, healthy-population efficacy designs, and age-cohort bridging, with the ACIP gate built into the value path across the med universe.

Why use Pipeline Vaccines on TypingMind?

Because you install it once and use it with any model. Pipeline 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 Pipeline 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/pipeline-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 Pipeline 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 Pipeline Vaccines?

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

Is the Pipeline 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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