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Peer Bench Med

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agentii-ai
peer-bench-med

Med peer benchmarking: select actual biotech/pharma peers via the med universe (drug/indication overlap where possible) and compare med-relevant metrics — pipeline depth, catalyst density, cash position, margins, valuation multiples.

Overview

Publisheragentii-ai
Repositoryagentii-investment-intelligence
Skill namepeer-bench-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 Peer Bench 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/peer-bench-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/peer-bench-med .claude/skills/peer-bench-med
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Peer Bench 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 Peer Bench 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 Peer Bench 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 peer-comparison frameworks; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
peer_count4-6Comparable-set size standard for comps
peer_logicindication/drug overlap firstMed peers are defined by science, not SIC codes
include_med_metricstruePipeline depth, catalyst density, cash runway

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).

Never fabricate a peer metric when the data surface is absent — annotate coverage_gap instead (spec 055 FR-B02 discipline).

Triggers

  • "Who are the closest peers to [biotech ticker]?"
  • "Benchmark [ticker] against its biotech comp set."
  • "How does [ticker]'s valuation compare to peers?"
  • "Which names compete with [ticker]'s pipeline?"
  • "Build a peer table with pipeline depth and cash."
  • "Is [ticker] expensive relative to its med peers?"
  • "Compare margins across the pharma peer group."
  • "What are the catalyst-dense names in this peer set?"
  • "Peer analysis for [ticker] with indication overlap."
  • "Which peers have the strongest balance sheets?"
  • "Which same-mechanism competitors matter for [ticker]'s pipeline?"

Production Grounding

  • Med peers are science-defined: use get_company_drugs to find indication/therapy overlap before financial comparison.
  • Launch-analog selection: for commercial-stage names, match same-class (mechanism/indication class) + same-channel (specialty/retail/hospital/device-rep/vaccine-procurement) analogs so launch trajectories compare like-for-like.
  • Mechanism-overlap scoring: rank candidate peers by shared targets/indications via the drug-knowledge reverse lookups; where sources conflict, keep both values with provenance.
  • Med-relevant metrics: pipeline assets by phase, catalyst density (PDUFA/AdCom/trial readouts), cash runway (quarters), R&D productivity; generic margins only as secondary.
  • Grounding frameworks: references/knowledge-frameworks.md (道/法 review knowledge + valuation lenses).

Data Source Priority

  1. get_company_drugs / search_companies — peer discovery by drug/indication overlap.
  2. search_drugs_by_target / search_drugs_by_indication — mechanism competitor mapping + overlap scoring (054 silver reverse lookups).
  3. get_financial_ratios / search_xbrl_facts — financial comparison data.
  4. get_peer_comparison — platform pre-computed peer metrics.
  5. Knowledge layer: search_investment_cases/search_by_analogue for historical peer dynamics + launch analogs.

Methodology

Retrieval Scope

structured_only

Retrieval Strategy

  1. Resolve the target via get_company_profile; pull its drugs (get_company_drugs).
  2. Find peers by indication/therapy overlap + med industry membership (search_companies).
  3. Map mechanism competitors: search_drugs_by_indication / search_drugs_by_target per key indication/target; score peers by shared targets/indications (conflicting sources shown with provenance).
  4. Match launch analogs for commercial-stage names: same-class + same-channel; retrieve analog launch history via knowledge tools.
  5. Pull per-peer financials (get_financial_ratios) + valuation context.
  6. Ground with historical cases/analogues via knowledge tools.

Temporal Scope

See frontmatter temporal_scope block.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

  1. Target profile
  2. Science-based peer selection (indication/drug overlap)
  3. Mechanism-overlap scoring (reverse lookups)
  4. Launch-analog matching (same-class, same-channel)
  5. Med-metric comparison
  6. Valuation & risk synthesis

Modes

  • Science-based (default): indication/drug-overlap peers.
  • Launch-analog (commercial-stage): same-class, same-channel analog peers with launch-trajectory context.
  • Financial: margin/valuation peers within the same industry.
  • Catalyst: peers ranked by upcoming FDA events.

Tool Fallbacks

FailureFallback
get_company_drugs emptyFall back to industry peers via search_companies; annotate
Reverse lookups emptyScore overlap from get_company_drugs classes only; annotate mechanism mapping unavailable
get_peer_comparison emptyBuild comparison manually from get_financial_ratios
Knowledge tools emptyProceed with structured data only

Output File

{ticker}/{YYYY-MM-DD_HHMM}_peer-bench-med_{affix}.md

Output Structure

  1. Executive Summary — relative standing in 2-3 sentences
  2. Peer Selection — peers + selection logic (science overlap)
  3. Mechanism Overlap — target/indication overlap matrix from reverse lookups, scored by shared mechanisms; conflicting source values shown with provenance
  4. Launch-Analog Match — same-class, same-channel analogs (commercial-stage names)
  5. Comparison Table — med metrics + financials + valuation
  6. Historical Context — cases/analogues with /v/ citations
  7. Risk Assessment — concentration/catalyst risks
  8. Coverage Gaps — missing data flags

Error Handling

ErrorFallback
No indication overlap foundWiden to same-industry peers; flag science-overlap unavailable
Mechanism data missingScore from indication overlap only; flag reverse-lookup coverage gap
Missing financialsMark N/A in table; do not fabricate

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 Peer Bench Med AI skill do?

Med peer benchmarking: select actual biotech/pharma peers via the med universe (drug/indication overlap where possible) and compare med-relevant metrics — pipeline depth, catalyst density, cash position, margins, valuation multiples.

Why use Peer Bench Med on TypingMind?

Because you install it once and use it with any model. Peer Bench 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 Peer Bench 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/peer-bench-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 Peer Bench 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 Peer Bench Med?

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

Is the Peer Bench 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.

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