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Pipeline Medicines

Community
agentii-ai
pipeline-medicines

Medicines pipeline analysis over structured universes: Phase I-III assets with probability-of-success discipline (unadjusted peak x POS = modeled value; POS changes logged with reasons), trial status-diff tracking, and mechanism/competitor mapping via drug-knowledge lookups.

Overview

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

Use it in TypingMind

Enable Pipeline Medicines 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 Medicines 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 Medicines 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 pipeline-valuation frameworks; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
asset_scopeall disclosed assetsFull-pipeline enumeration first
pos_sourcepublished phase transition rangesStarting point, then adjusted with logged reasons
status_difftruePrevious vs current trial status shown per asset
competitor_maptrueSame-target and same-indication crowding surfaced
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 drug pipeline by phase."
  • "What are the Phase II and III assets for [ticker]?"
  • "Risk-adjust [ticker]'s pipeline with explicit POS assumptions."
  • "Which of [ticker]'s programs is most valuable?"
  • "What trials recently changed status for [ticker]?"
  • "Who else is targeting [mechanism/target]?"
  • "How crowded is [indication], and what does that mean for [ticker]?"
  • "Log every POS change with reasons for [ticker]'s pipeline."
  • "What is the next catalyst for each of [ticker]'s assets?"
  • "Compare [ticker]'s pipeline against same-indication competitors."
  • "Map [ticker]'s assets by mechanism of action."
  • "What does [ticker]'s pipeline say about its runway?"

Production Grounding

  • POS discipline: every asset carries an unadjusted peak sales estimate and a phase-appropriate POS; the modeled number is peak x POS, never a bare risk-adjusted figure. Every POS change is logged with its reason (trial data, competitor data, discontinuation) — silent POS drift is a defect.
  • Phase ladder: Phase I safety/dosing, Phase II efficacy signal, Phase III registrational; option value rises with phase. Apply scrutiny axes when sizing pivotal assets.
  • Status-diff discipline: show previous vs current status per trial (New/Suspended/Terminated/Ahead/Delayed/...); Terminated plus enrollment collapse is a red flag; recruitment-complete starts the readout clock.
  • Competitor mapping: same-target and same-indication crowding discounts peak sales, not just POS; mechanism overlap explains class risk.
  • Grounding detail lives in references/knowledge-frameworks.md.

Data Source Priority

  1. get_company_drugs / search_universe_drugs — asset inventory from the med universe.
  2. search_drug_knowledge / get_drug_knowledge — mechanism, targets, indications, phase per drug.
  3. search_drugs_by_target / search_drugs_by_indication — reverse lookups for the competitor map.
  4. search_clinical_trials / get_clinical_trial — status, enrollment, primary-completion dates.
  5. search_fda_approvals — registration history; search_earnings_calendar for timing; knowledge layer via search_investment_cases / search_knowledge_entries.

Methodology

Retrieval Scope

structured_only

Retrieval Strategy

  1. Pull asset inventory (get_company_drugs), then universe cross-checks (search_universe_drugs).
  2. Enrich per asset from search_drug_knowledge / get_drug_knowledge (mechanism, targets, phase).
  3. Pull trial records (search_clinical_trials) for status-diff and next-catalyst dates.
  4. Build the competitor map via search_drugs_by_target / search_drugs_by_indication.
  5. Size each asset (peak x POS with change log); ground in cases and knowledge entries.

Temporal Scope

See frontmatter temporal_scope block. Development programs span years; status history may reach back 8-12 quarters.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

  1. Asset enumeration
  2. Phase and mechanism enrichment
  3. Status-diff and catalyst dating
  4. Competitor map assembly
  5. POS model with change log

Modes

  • Phase scan (default): all assets by phase with status-diff and next catalyst.
  • POS model: peak x POS modeling with a full change log and scenario brackets.
  • Competitor map: target/indication crowding with class-risk read.

Tool Fallbacks

FailureFallback
get_company_drugs emptysearch_universe_drugs by company or indication; annotate coverage_gap
Drug-knowledge record missingFlag mechanism/competitor data unavailable; never infer targets
No trial rowsMark status undisclosed; note the readout clock is unverified
Knowledge tools emptyProceed with structured data only

Output File

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

Output Structure

  1. Executive Summary — pipeline stance in 2-3 sentences
  2. Asset Table — phase, indication, mechanism, status-diff, next catalyst
  3. POS Model — unadjusted peak, POS, modeled value per asset, change log with reasons
  4. Competitor Map — same-target and same-indication crowding, class risk
  5. Historical Context — cases and knowledge entries with /v/ citations
  6. Coverage Gaps — missing records, undisclosed status, degraded modes

Error Handling

ErrorFallback
Phase unknownMark "undisclosed"; do not guess
POS source missingState the assumption range and label it a judgment
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 Medicines AI skill do?

Medicines pipeline analysis over structured universes: Phase I-III assets with probability-of-success discipline (unadjusted peak x POS = modeled value; POS changes logged with reasons), trial status-diff tracking, and mechanism/competitor mapping via drug-knowledge lookups.

Why use Pipeline Medicines on TypingMind?

Because you install it once and use it with any model. Pipeline Medicines 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 Medicines 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-medicines. 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 Medicines?

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 Medicines?

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

Is the Pipeline Medicines 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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