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Agent skill

Cross Disciplinary Bridge Finder

by aipoch in aipoch/medical-research-skills

A skill your agent uses when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific domains, or building…

MITAuto-check passedWriting & Content

Install Cross Disciplinary Bridge Finder

skills CLI
$ npx skills add aipoch/medical-research-skills --skill cross-disciplinary-bridge-finder -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills cross-disciplinary-bridge-finder --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Evidence Insight/cross-disciplinary-bridge-finder' .claude/skills/cross-disciplinary-bridge-finder && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cross-disciplinary-bridge-finder
GitHub stars
1.9k
Token cost
~2.6k tokens
SKILL.md length
824 words
Files
7 (incl. scripts, references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific domains, or building…

  • Works in 4 steps: Confirm the user input, output path, and… → Edit the in-file CONFIG block or… → Run python scripts/main.py with the… → …
  • Identifying collaboration opportunities across fields
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 14 more sections
  • Runs Python scripts from its folder; calls python

What it does

Cross Disciplinary Bridge Finder is an agent skill from aipoch/medical-research-skills. Use when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific domains, or building interdisciplinary research teams. Identifies synergies between scientific disciplines, matches researchers with complementary expertise, and facilitates cross-domain collaborations. Supports interdisciplinary grant applications and innovative research team formation.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `cross-disciplinary-bridge-finder_audit_result_v2.json`, `references/audit-reference.md` and `scripts/main.py`).

It sits in Writing & Content, covering Translation. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Identifying collaboration opportunities across fields
  • Finding experts in complementary disciplines
  • Translating methodologies between scientific domains
  • Building interdisciplinary research teams

Example prompts

  • “/cross-disciplinary-bridge-finder”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Confirm the user input, output path, and any required config values.
  2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
  3. Run python scripts/main.py with the validated inputs.
  4. Review the generated output and return the final artifact with any assumptions called out.

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cross Disciplinary Bridge Finder loads about 2.6k tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 119 tokens; SKILL.md has 824 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~119
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 824 words, ~2,551 tokens.

Download SKILL.mdSave it as .claude/skills/cross-disciplinary-bridge-finder/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
cross-disciplinary-bridge-finder
description
Use when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific domains, or building interdisciplinary research teams. Identifies synergies between scientific disciplines, matches researchers with complementary expertise, and facilitates cross-domain collaborations. Supports interdisciplinary grant applications and innovative research team formation.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Cross-Disciplinary Research Collaboration Finder

When to Use

  • Use this skill when the task needs Use when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific domains, or building interdisciplinary research teams. Identifies synergies between scientific disciplines, matches researchers with complementary expertise, and facilitates cross-domain collaborations. Supports interdisciplinary grant applications and innovative research team formation.
  • Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
  • Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

  • Scope-focused workflow aligned to: Use when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific domains, or building interdisciplinary research teams. Identifies synergies between scientific disciplines, matches researchers with complementary expertise, and facilitates cross-domain collaborations. Supports interdisciplinary grant applications and innovative research team formation.
  • Packaged executable path(s): scripts/main.py.
  • Reference material available in references/ for task-specific guidance.
  • Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

  • Python: 3.10+. Repository baseline for current packaged skills.
  • dataclasses: unspecified. Declared in requirements.txt.
  • networkx: unspecified. Declared in requirements.txt.
  • numpy: unspecified. Declared in requirements.txt.
  • sklearn: unspecified. Declared in requirements.txt.
  • networkx: >=2.8. Declared in scripts/requirements.txt.
  • numpy: >=1.21. Declared in scripts/requirements.txt.
  • pandas: >=1.3. Declared in scripts/requirements.txt.
  • scikit-learn: >=1.0. Declared in scripts/requirements.txt.
  • matplotlib: >=3.5. Declared in scripts/requirements.txt.
  • seaborn: >=0.11. Declared in scripts/requirements.txt.
  • openai: >=1.0. Declared in scripts/requirements.txt.

Example Usage

bash
cd "20260318/scientific-skills/Evidence Insight/cross-disciplinary-bridge-finder"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

  1. Confirm the user input, output path, and any required config values.
  2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
  3. Run python scripts/main.py with the validated inputs.
  4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

  • Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
  • Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
  • Primary implementation surface: scripts/main.py.
  • Reference guidance: references/ contains supporting rules, prompts, or checklists.
  • Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
  • Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

bash
python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

bash
python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

  1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
  2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
  3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
  4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
  5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.
Show full SKILL.md (334 more words)Show less

When to Use This Skill

  • identifying collaboration opportunities across fields
  • finding experts in complementary disciplines
  • translating methodologies between scientific domains
  • building interdisciplinary research teams
  • discovering funding for interdisciplinary projects
  • mapping knowledge transfer pathways

Quick Start

python
from scripts.interdisciplinary import CollaborationFinder

finder = CollaborationFinder()

# Find collaborators in different field
collaborators = finder.find_experts(
    my_expertise="machine_learning",
    target_field="immunology",
    collaboration_type="co_authorship",
    min_publications=10,
    h_index_threshold=15
)

if not collaborators:
    print("No collaborators found — try lowering min_publications or h_index_threshold.")
else:
    # Validate quality before proceeding: only consider complementarity_score > 0.7
    qualified = [e for e in collaborators if e.complementarity_score > 0.7]
    print(f"Found {len(collaborators)} candidates; {len(qualified)} meet quality threshold (score > 0.7):")
    for expert in qualified[:5]:
        print(f"  - {expert.name} ({expert.institution})")
        print(f"    Research: {expert.research_focus}")
        print(f"    Complementarity score: {expert.complementarity_score}")

# Identify transferable methods
methods = finder.identify_transferable_methods(
    from_field="physics",
    to_field="biology",
    application_area="systems_modeling"
)

if not methods:
    print("No transferable methods found — consider broadening the application_area.")
else:
    # Validate applicability before proceeding: review transfer_potential
    for method in methods:
        print(f"Method: {method.name}")
        print(f"  Success in source field: {method.success_rate}")
        print(f"  Application potential: {method.transfer_potential}")
        if method.transfer_potential < 0.6:
            print(f"  ⚠ Low transfer potential — consider a different application_area.")

# Find interdisciplinary funding
grants = finder.find_interdisciplinary_funding(
    fields=["AI", "medicine", "ethics"],
    funder_types=["NIH", "NSF", "private_foundation"],
    deadline_within_months=6
)

if not grants:
    print("No grants found — try extending deadline_within_months or broadening funder_types.")

# Generate collaboration proposal outline
proposal_outline = finder.generate_collaboration_proposal(
    partner_expertise="clinical_trial_design",
    my_expertise="data_science",
    research_question="precision_medicine"
)

Command Line Usage

text
python scripts/main.py --my-field machine_learning --target-field immunology --find-collaborators --output matches.json

Handling Poor Results

  • Empty collaborator list: Lower min_publications or h_index_threshold; broaden collaboration_type.
  • No transferable methods: Widen application_area to a higher-level domain (e.g., "modeling" instead of "systems_modeling").
  • No funding results: Extend deadline_within_months or add more entries to funder_types.
  • Weak proposal outline: Ensure research_question is a descriptive string rather than a short keyword.

References

  • references/guide.md - Comprehensive user guide
  • references/examples/ - Working code examples
  • references/api-docs/ - Complete API documentation

Output Requirements

Every final response should make these items explicit when they are relevant:

  • Objective or requested deliverable
  • Inputs used and assumptions introduced
  • Workflow or decision path
  • Core result, recommendation, or artifact
  • Constraints, risks, caveats, or validation needs
  • Unresolved items and next-step checks

Error Handling

  • If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
  • If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
  • If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
  • Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of cross-disciplinary-bridge-finder and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

cross-disciplinary-bridge-finder only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

References

Response Template

Use the following fixed structure for non-trivial requests:

  1. Objective
  2. Inputs Received
  3. Assumptions
  4. Workflow
  5. Deliverable
  6. Risks and Limits
  7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references) in scientific-skills/Evidence Insight/cross-disciplinary-bridge-finder of aipoch/medical-research-skills.

  • SKILL.md
  • cross-disciplinary-bridge-finder_audit_result_v2.json
  • references/audit-reference.md
  • requirements.txt
  • scripts/main.py
  • scripts/requirements.txt
  • tile.json

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Cross Disciplinary Bridge Finder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Cross Disciplinary Bridge Finder compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
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Academic Prose De-AI Editorheise3/academic-deai254—~1.4kAutomated safety check: PassMIT
Academic HumanizerYila-AI/awesome-research-skills133—~1.7kAutomated safety check: PassApache-2.0
Academic Paper PolishHKUSTDial/Supervisor-Skills8.8k—~3.1kAutomated safety check: PassCC-BY-NC-SA-4.0
Smooth Translationopenforecast-org/smooth107—~3.2kAutomated safety check: PassLGPL-2.1

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Categories

Questions about Cross Disciplinary Bridge Finder

What does Cross Disciplinary Bridge Finder do?

A skill your agent uses when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific domains, or building…. Cross Disciplinary Bridge Finder is an agent skill from aipoch/medical-research-skills. Use when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific domains, or building interdisciplinary research teams.

When should I use Cross Disciplinary Bridge Finder?

Cross Disciplinary Bridge Finder fits situations like: identifying collaboration opportunities across fields; finding experts in complementary disciplines; translating methodologies between scientific domains; building interdisciplinary research teams.

How do I install Cross Disciplinary Bridge Finder in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill cross-disciplinary-bridge-finder -a claude-code`. Or copy the skill folder (scientific-skills/Evidence Insight/cross-disciplinary-bridge-finder in aipoch/medical-research-skills) into .claude/skills/cross-disciplinary-bridge-finder in your project. Claude Code loads it when a task matches its description.

How do I install Cross Disciplinary Bridge Finder in Codex?

Run `npx skills add aipoch/medical-research-skills --skill cross-disciplinary-bridge-finder -a codex`. Or copy the skill folder (scientific-skills/Evidence Insight/cross-disciplinary-bridge-finder in aipoch/medical-research-skills) into .agents/skills/cross-disciplinary-bridge-finder in your project. Codex loads it when a task matches its description.

Can I use Cross Disciplinary Bridge Finder in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill cross-disciplinary-bridge-finder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cross-disciplinary-bridge-finder, .gemini/skills/cross-disciplinary-bridge-finder, .github/skills/cross-disciplinary-bridge-finder and .opencode/skills/cross-disciplinary-bridge-finder in your project.

What does Cross Disciplinary Bridge Finder need to run?

Going by SKILL.md and its folder, Cross Disciplinary Bridge Finder needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Cross Disciplinary Bridge Finder access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Cross Disciplinary Bridge Finder safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Cross Disciplinary Bridge Finder use?

Cross Disciplinary Bridge Finder is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cross Disciplinary Bridge Finder use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 255 tokens, read only when the agent opens those files.

What are the alternatives to Cross Disciplinary Bridge Finder?

Skills that share tags, products or a category with Cross Disciplinary Bridge Finder: Humanities Writing Companion (tizzy916/humanities-writing-companion, 436 stars), Academic Prose De-AI Editor (heise3/academic-deai, 254 stars), Academic Humanizer (Yila-AI/awesome-research-skills, 133 stars) and Academic Paper Polish (HKUSTDial/Supervisor-Skills, 8.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cross Disciplinary Bridge Finder?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,937 GitHub stars. The repository holds 578 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.