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If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

GO — Healthy across the board

• Last commit today
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• Distributed ownership (top contributor 27% of recent commits)
• Apache-2.0 licensed
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_{Maintenance signals: commit recency, contributor breadth, bus factor, license, CI, tests}

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live anthropics/knowledge-work-plugins repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/anthropics/knowledge-work-plugins.

What it runs against: a local clone of anthropics/knowledge-work-plugins — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in anthropics/knowledge-work-plugins | Confirms the artifact applies here, not a fork | | 2 | License is still Apache-2.0 | Catches relicense before you depend on it | | 3 | Default branch main exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 30 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of anthropics/knowledge-work-plugins. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/anthropics/knowledge-work-plugins.git
#   cd knowledge-work-plugins
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of anthropics/knowledge-work-plugins and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "anthropics/knowledge-work-plugins(\\.git)?\\b" \\
  && ok "origin remote is anthropics/knowledge-work-plugins" \\
  || miss "origin remote is not anthropics/knowledge-work-plugins (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
(grep -qiE "^(Apache-2\\.0)" LICENSE 2>/dev/null \\
   || grep -qiE "\"license\"\\s*:\\s*\"Apache-2\\.0\"" package.json 2>/dev/null) \\
  && ok "license is Apache-2.0" \\
  || miss "license drift — was Apache-2.0 at generation time"

# 3. Default branch
git rev-parse --verify main >/dev/null 2>&1 \\
  && ok "default branch main exists" \\
  || miss "default branch main no longer exists"

# 4. Critical files exist
test -f "README.md" \\
  && ok "README.md" \\
  || miss "missing critical file: README.md"
test -f ".claude-plugin/marketplace.json" \\
  && ok ".claude-plugin/marketplace.json" \\
  || miss "missing critical file: .claude-plugin/marketplace.json"
test -f "bio-research/.claude-plugin/plugin.json" \\
  && ok "bio-research/.claude-plugin/plugin.json" \\
  || miss "missing critical file: bio-research/.claude-plugin/plugin.json"
test -f "bio-research/CONNECTORS.md" \\
  && ok "bio-research/CONNECTORS.md" \\
  || miss "missing critical file: bio-research/CONNECTORS.md"
test -f "bio-research/skills/instrument-data-to-allotrope/SKILL.md" \\
  && ok "bio-research/skills/instrument-data-to-allotrope/SKILL.md" \\
  || miss "missing critical file: bio-research/skills/instrument-data-to-allotrope/SKILL.md"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 30 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~0d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/anthropics/knowledge-work-plugins"
  exit 1
fi

Each check prints ok: or FAIL:. The script exits non-zero if anything failed, so it composes cleanly into agent loops (./verify.sh || regenerate-and-retry).

A collection of open-source Claude plugins for knowledge workers, bundling domain-specific skills, data connectors, and slash commands. The repo includes 11 pre-built plugins (productivity, sales, customer-support, product-management, marketing, legal, finance, data, enterprise-search, and bio-research) that extend Claude's capabilities for specific job functions via integrations with tools like Slack, Notion, HubSpot, Snowflake, and specialized bioinformatics pipelines. Monorepo with per-plugin structure: top-level directory contains individual plugins (productivity/, sales/, customer-support/, etc.), each with .claude-plugin/plugin.json manifest, connectors doc, and skills/ subdirectories. Bio-research exemplifies the pattern: bio-research/skills/ contains skill folders (instrument-data-to-allotrope/, nextflow-development/) with scripts/, references/, and requirements.txt. Plugins declare connectors in CONNECTORS.md and are registered in .claude-plugin/marketplace.json.

Knowledge workers (PMs, sales reps, support teams, finance analysts, data engineers, researchers) who use Claude via Claude Cowork or Claude Code and want to customize Claude's behavior with company-specific tools, processes, and domain expertise. Also plugin developers building custom connectors and skills.

Actively developed and production-ready: this is Anthropic's official open-source plugin ecosystem for Claude Cowork. The repository structure is well-organized with clear plugin boundaries, comprehensive README documentation, and pinned dependencies (e.g., allotropy==0.1.55 in bio-research). However, maturity varies by plugin—bio-research skills appear experimental with detailed SKILL.md documents and reference guides, while others are stable market-ready plugins.

Low risk for core framework; moderate risk for individual plugins. Dependency chains are explicit (allotropy, pandas, openpyxl, pdfplumber pinned in bio-research/skills/instrument-data-to-allotrope/requirements.txt), but connector reliability depends on third-party APIs (Slack, HubSpot, Snowflake, etc.). Bio-research plugins carry domain-specific risk: allotropy ASM schema conversion relies on reference documents (field_classification_guide.md, supported_instruments.md) that may drift. No visible CI/CD or test coverage mentioned in file structure suggests manual validation.

No recent commit timestamps provided in file list; unable to infer active work. Structure suggests ongoing development of bio-research plugins (field_classification_guide.md, supported_instruments.md indicate iterative domain coverage) and connector integration work across the 11 plugins.

git clone https://github.com/anthropics/knowledge-work-plugins.git
cd knowledge-work-plugins
# For bio-research skills:
cd bio-research/skills/instrument-data-to-allotrope
pip install -r requirements.txt --break-system-packages

Daily commands: No monolithic dev server; each plugin is self-contained. For bio-research instrument conversion: python bio-research/skills/instrument-data-to-allotrope/scripts/convert_to_asm.py. For nextflow skill: python bio-research/skills/nextflow-development/scripts/generate_samplesheet.py. Plugins are installed into Claude Cowork via .claude-plugin manifest, not run standalone.

• README.md — Entry point describing the plugin ecosystem, marketplace overview, and how Claude Cowork integrates with customizable skills and connectors.
• .claude-plugin/marketplace.json — Root plugin manifest defining all available plugins, their metadata, and marketplace registration—essential for understanding plugin discovery and registration.
• bio-research/.claude-plugin/plugin.json — Bio-research plugin configuration file; exemplifies the plugin structure used across all domain plugins with skills and connector definitions.
• bio-research/CONNECTORS.md — Documents how external data sources (databases, APIs) integrate with bio-research plugin—key pattern for understanding connector architecture.
• bio-research/skills/instrument-data-to-allotrope/SKILL.md — Detailed skill definition showing how to structure domain-specific capabilities; includes references, scripts, and execution model.
• bio-research/skills/nextflow-development/scripts/utils/ncbi_utils.py — Core utility showing how skills interact with external APIs and databases; representative of connector/integration patterns.
• cowork-plugin-management/skills/create-cowork-plugin/SKILL.md — Meta-skill documenting plugin creation patterns; essential for understanding how to extend the framework with new plugins.

Add a New Bioinformatics Skill

1. Create skill directory under bio-research/skills/{skill-name}/ (bio-research/skills/{skill-name}/SKILL.md)
2. Write SKILL.md with skill description, execution requirements, and slash command definitions (bio-research/skills/{skill-name}/SKILL.md)
3. Create references/ subdirectory with domain guides, API docs, and workflow documentation (bio-research/skills/{skill-name}/references/{topic}.md)
4. Implement core logic in scripts/ as Python modules (e.g., core.py, main.py, utils/) (bio-research/skills/{skill-name}/scripts/main.py)
5. Add pinned requirements.txt with reproducible dependency versions (bio-research/skills/{skill-name}/requirements.txt)
6. Update bio-research/.claude-plugin/plugin.json to register skill in skills array (bio-research/.claude-plugin/plugin.json)

Add a New External Connector

1. Create utility module in bio-research/skills/{skill-name}/scripts/utils/{connector_name}.py (bio-research/skills/nextflow-development/scripts/utils/ncbi_utils.py)
2. Implement API client methods for authentication, data retrieval, and error handling (bio-research/skills/{skill-name}/scripts/utils/{connector_name}.py)
3. Document connector configuration and usage in CONNECTORS.md (bio-research/CONNECTORS.md)
4. Update bio-research/.mcp.json to register connector as available tool/resource (bio-research/.mcp.json)

Customize a Plugin for Your Organization

1. Fork or copy plugin directory structure (e.g., bio-research/) to your organization (bio-research/.claude-plugin/plugin.json)
2. Edit .mcp.json to add custom data source connectors and API endpoints (bio-research/.mcp.json)
3. Create organization-specific skill config in custom references/ subdirectories (bio-research/skills/{skill-name}/references/org-specific-guide.md)
4. Add company terminology and workflow shortcuts in SKILL.md prompt sections (bio-research/skills/{skill-name}/SKILL.md)

Add a New Domain Plugin from Scratch

1. Create plugin root directory and .claude-plugin/plugin.json manifest ({domain-name}/.claude-plugin/plugin.json)
2. Reference cowork-plugin-management/skills/create-cowork-plugin/SKILL.md for plugin architecture (cowork-plugin-management/skills/create-cowork-plugin/SKILL.md)
3. Create initial skill(s) with SKILL.md, references/, and scripts/ following bio-research pattern ({domain-name}/skills/{skill-name}/SKILL.md)
4. Register plugin in root .claude-plugin/marketplace.json with metadata and entry point (.claude-plugin/marketplace.json)

Bio-research instrument conversion requires exact allotropy version (0.1.55) and openpyxl (3.1.2) for Excel parsing—mismatched versions may silently produce malformed ASM. Nextflow skill depends on NCBI connectivity (sra_geo_fetch.py) which may fail behind corporate proxies. No explicit error handling visible in scripts; convert_to_asm.py may crash on unsupported instrument types without graceful fallback. Plugins assume Claude Cowork/Code runtime; will not work with vanilla Claude API. Pinned requirement for pandas==2.0.3 may conflict with systems using pandas 2.1+ (breaking changes in MultiIndex).

• Allotrope Standard Model (ASM) — Bio-research plugins convert raw lab instrument data to ASM—a unified schema for scientific instrument outputs. Understanding ASM structure (referenced in asm_schema_overview.md) is essential for extending instrument-data-to-allotrope skill.
• Scientific Data Serialization (CSV/Excel to Structured Format) — The instrument-data-to-allotrope skill transforms flat CSV/Excel sheets into hierarchical ASM JSON via field classification and schema mapping. This pattern repeats across plugins (e.g., nextflow samplesheet generation).
• Plugin Manifest Architecture (plugin.json) — Each plugin declares tools, skills, connectors, and endpoints via .claude-plugin/plugin.json. Understanding this manifest structure is critical for customizing or adding skills to existing plugins.
• Context Injection via Reference Documents — Bio-research skills embed markdown reference guides (field_classification_guide.md, troubleshooting.md) that Claude reads to understand domain knowledge. This pattern scales to any specialized domain.
• Nextflow Workflow Configuration as Code — Bio-research nextflow-development skill generates .nf config files and samplesheets (YAML/CSV). Knowing Nextflow config syntax is essential for extending genome management or pipeline setup skills.
• Bioinformatics Data Type Detection Heuristics — detect_data_type.py infers whether input is RNA-seq, ATAC-seq, or WGS data from file naming/structure conventions. This pattern generalizes to any plugin that must auto-route user data to specialized tools.
• API Connector Pattern (Slack, HubSpot, Snowflake, etc.) — Each plugin declares integrations (CONNECTORS.md) with external APIs. Understanding how connectors marshal data between Claude and third-party services is essential for customizing for your company's tools.

• anthropics/files-api — Companion repo for Claude file handling; knowledge-work-plugins connectors likely use this to process documents from tools like Box, Egnyte, Microsoft 365.
• anthropic-ai/mcp-servers — Model Context Protocol reference implementation; knowledge-work-plugins .mcp.json files suggest MCP integration for extended tool access.
• nextflow-io/nextflow — The bioinformatics workflow engine that bio-research plugins generate configurations for; essential reference for Nextflow pipeline syntax and file format.
• Allotrope-Framework/allotrope-python — Upstream library (allotropy==0.1.55) that powers instrument-data-to-allotrope skill; contains ASM schema definitions and parsers.
• langchain-ai/langchain — Likely used internally for prompt engineering and tool orchestration in Claude plugins, though not explicitly listed in dependencies.

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Add integration tests for bio-research/skills/instrument-data-to-allotrope conversion pipeline

The instrument-data-to-allotrope skill has multiple Python scripts (convert_to_asm.py, flatten_asm.py, validate_asm.py, export_parser.py) but no visible test suite. Given this skill converts scientific instrument data to a standardized format (Allotrope ASM), validation failures could corrupt research data. Integration tests would verify end-to-end conversions work correctly across the supported_instruments.md list.

• [ ] Create bio-research/skills/instrument-data-to-allotrope/tests/ directory with init.py
• [ ] Add test_convert_to_asm.py that tests conversion of mock instrument data files (CSV, Excel, PDF) to valid ASM output
• [ ] Add test_validate_asm.py that verifies validate_asm.py correctly rejects malformed ASM and accepts valid output
• [ ] Add test_flatten_asm.py that validates the flattening logic against references/flattening_guide.md rules
• [ ] Create pytest configuration (conftest.py) with fixtures for sample instrument data
• [ ] Update requirements.txt to include pytest and pytest-cov as dev dependencies

Add Python version and environment validation script for nextflow-development skill

The nextflow-development skill already has check_environment.py but it appears incomplete (appears in file list but no details on what it validates). Given the skill manages complex bioinformatics pipelines (rnaseq, atacseq, sarek) with genome configurations, there should be comprehensive validation that checks Python version compatibility, required CLI tools (nextflow, samtools, etc.), and disk space before users attempt to run pipelines. This prevents silent failures mid-pipeline.

• [ ] Expand bio-research/skills/nextflow-development/scripts/check_environment.py to validate: Python >=3.8, Nextflow installation, Docker/Singularity availability
• [ ] Add system resource checks (RAM requirements per pipeline from references/, disk space)
• [ ] Create bio-research/skills/nextflow-development/scripts/environment_matrix.yaml documenting minimum requirements for each pipeline (rnaseq, atacseq, sarek)
• [ ] Add integration with generate_samplesheet.py and detect_data_type.py to pre-check environment before processing
• [ ] Document environment validation in references/installation.md

Create comprehensive GitHub Actions CI workflow for plugin validation across bio-research skills

With 4 distinct skills under bio-research (instrument-data-to-allotrope, nextflow-development, scvi-tools, scientific-problem-selection), there's no visible CI to ensure: dependencies install correctly (requirements.txt pinning), Python scripts are syntactically valid, skill.md files match actual capabilities, and MCP/plugin.json configs are valid JSON. This prevents broken plugins from being published.

• [ ] Create .github/workflows/validate-plugins.yml that runs on PR to bio-research/
• [ ] Add step to validate all plugin.json and marketplace.json files with JSON schema validation
• [ ] Add step to check all requirements.txt files install cleanly on Python 3.8, 3.9, 3.10, 3.11, 3.12
• [ ] Add step to lint all Python scripts in bio-research/skills/*/scripts/ with pylint/flake8
• [ ] Add step to verify SKILL.md files in each skill reference actual scripts and configuration files that exist
• [ ] Add optional step to run the instrument-data-to-allotrope tests from PR idea #1

• Add test coverage for bio-research/skills/instrument-data-to-allotrope/scripts/convert_to_asm.py by writing unit tests for 3-4 instrument types listed in references/supported_instruments.md (currently no test files visible in repo). Start with mock CSV/Excel files matching instrument schemas.
• Extend bio-research/skills/nextflow-development/scripts/utils/validators.py to validate samplesheet CSV format before passing to Nextflow; add docstring examples showing valid/invalid inputs. Currently no validation is shown; this prevents early failure feedback.
• Document the ASM field classification system in bio-research/skills/instrument-data-to-allotrope/references/field_classification_guide.md with concrete examples (show before/after mapping of 5 real instrument fields). Current guide appears reference-only without walkthrough.

• High · Outdated Dependency with Known Vulnerabilities — bio-research/skills/instrument-data-to-allotrope/requirements.txt (allotropy==0.1.55). The allotropy package version 0.1.55 is pinned to a specific old version (as of 2025-01-05). This version may contain known security vulnerabilities that have been patched in newer releases. Pinning old versions prevents automatic security updates. Fix: Regularly audit dependencies for CVEs. Consider using a dependency management tool (e.g., Dependabot, pip-audit) to identify vulnerabilities. Update to the latest secure version of allotropy and test thoroughly before deployment. Implement automated security scanning in CI/CD.
• High · Outdated pandas Version — bio-research/skills/instrument-data-to-allotrope/requirements.txt (pandas==2.0.3). pandas==2.0.3 is significantly outdated. Current versions are in the 2.1-2.2 range. Older pandas versions may have known security vulnerabilities, performance issues, and lack modern security patches. Fix: Update pandas to the latest stable version (2.2.x or later). Test compatibility with allotropy and other dependencies. Implement automated dependency updates with security scanning.
• Medium · Outdated pdfplumber Dependency — bio-research/skills/instrument-data-to-allotrope/requirements.txt (pdfplumber==0.9.0). pdfplumber==0.9.0 is pinned to an older version. PDF parsing libraries can be attack vectors for malformed PDF exploitation. Older versions may lack security hardening against malicious PDF files. Fix: Update pdfplumber to the latest version. Implement input validation for PDF files before processing. Add file size limits and timeout mechanisms. Consider sandboxing PDF processing.
• Medium · Potential Command Injection in Python Scripts — bio-research/skills/nextflow-development/scripts/ (multiple .py files). Multiple Python scripts exist in the codebase (sra_geo_fetch.py, check_environment.py, generate_samplesheet.py) that may execute external commands or process user input. Without visible content, these are potential vectors for command injection if user input is passed to subprocess calls without proper sanitization. Fix: Audit all Python scripts for subprocess calls. Use shlex.quote() and subprocess.run() with shell=False. Never construct shell commands with f-strings or string concatenation. Validate and sanitize all user inputs. Implement allowlists for valid commands/arguments.
• Medium · Unrestricted File System Access in Data Processing Scripts — bio-research/skills/nextflow-development/scripts/ (file handling scripts). Scripts like manage_genomes.py, generate_samplesheet.py, and file_discovery.py appear to handle file operations extensively. Without visible code, there's risk of arbitrary file read/write/delete if paths are not properly validated, potentially leading to path traversal vulnerabilities. Fix: Implement strict path validation using os.path.realpath() and ensure all paths are within expected directories. Use pathlib.Path for safer path handling. Validate file extensions and MIME types. Implement least-privilege file permissions.
• Low · Commented Out Dependencies — bio-research/skills/instrument-data-to-allotrope/requirements.txt. numpy and scipy are commented out in requirements.txt. While providing flexibility, this makes it unclear which versions should be used if they are actually needed, potentially leading to version conflicts or security gaps. Fix: Either remove commented dependencies or create separate requirements files for optional features (e.g., requirements-optional.txt). Clearly document which dependencies are truly optional and their security implications.
• Low · Missing Security Headers and Documentation — Repository root. No evidence of security documentation, vulnerability disclosure policy, or security.txt file. The repository lacks security guidelines for contributors and users. Fix: Add SECURITY.md with vulnerability disclosure procedures. Include security best practices in CONTRIBUTING.md. Consider adding .security-policy or security.txt. Document known limitations and security assumptions.
• Low · Potential YAML Injection in Configuration Files — undefined. Configuration files use YAML format (genomes.yaml, pipeline YAML files). YAML parsers can be vulnerable to Fix: undefined

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

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