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benchling-integration

Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

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Benchling Integration

Overview

Benchling is a cloud platform for life sciences R&D. Access registry entities (DNA, RNA, proteins), inventory, electronic lab notebooks, and workflows programmatically via the Python SDK and REST API.

Version note: Examples target benchling-sdk 1.25.0 (latest stable on PyPI). Docs: benchling.com/sdk-docs. Platform guide: docs.benchling.com.

When to Use This Skill

This skill should be used when:

Working with Benchling's Python SDK or REST API
Managing biological sequences (DNA, RNA, proteins) and registry entities
Automating inventory operations (samples, containers, locations, transfers)
Creating or querying electronic lab notebook entries
Building workflow automations or Benchling Apps
Syncing data between Benchling and external systems
Querying the Benchling Data Warehouse for analytics
Setting up event-driven integrations with AWS EventBridge

Core Capabilities

Seven capability areas, each with code, are in references/core_capabilities.md:

Authentication and setup — API key and OAuth app auth; see references/authentication.md.
Registry and entity management — DNA and AA sequences, custom entities, schemas, and registration.
Inventory management — containers, boxes, plates, locations, and transfers.
Notebook and documentation — entries, day-to-day notes, and structured tables.
Workflows and automation — tasks, flowcharts, and assay runs.
Events and integration — EventBridge subscriptions; see references/eventbridge.md.
Data warehouse and analytics — SQL access to the warehouse.

Endpoint and SDK detail is in references/api_endpoints.md and references/sdk_reference.md.

Best Practices

Error Handling

The SDK automatically retries failed requests:

# Automatic retry for 429, 502, 503, 504 status codes
# Up to 5 retries with exponential backoff
# Customize retry behavior if needed
from benchling_sdk.retry import RetryStrategy

benchling = Benchling(
    url=tenant_url,
    auth_method=ApiKeyAuth(api_key),
    retry_strategy=RetryStrategy(max_retries=3),
)

Pagination Efficiency

Use generators for memory-efficient pagination:

# Generator-based iteration
for page in benchling.dna_sequences.list():
    for sequence in page:
        process(sequence)

# Check estimated count without loading all pages
total = benchling.dna_sequences.list().estimated_count()

Schema Fields Helper

Use the fields() helper for custom schema fields:

# Convert dict to Fields object
custom_fields = benchling.models.fields({
    "concentration": "100 ng/μL",
    "date_prepared": "2025-10-20",
    "notes": "High quality prep"
})

Forward Compatibility

The SDK handles unknown enum values and types gracefully:

Unknown enum values are preserved
Unrecognized polymorphic types return UnknownType
Allows working with newer API versions

Security Considerations

Never commit API keys or OAuth secrets to version control
Read only named environment variables (BENCHLING_TENANT_URL, BENCHLING_API_KEY, etc.)
Route network calls exclusively to your tenant URL
Rotate keys if compromised; use OAuth for multi-user production apps
Grant minimal necessary permissions for apps in the Developer Console

Resources

references/

Detailed reference documentation for in-depth information:

authentication.md - Comprehensive authentication guide including OIDC, security best practices, and credential management
sdk_reference.md - Detailed Python SDK reference with advanced patterns, examples, and all entity types
api_endpoints.md - REST API endpoint reference for direct HTTP calls without the SDK
eventbridge.md - EventBridge setup, event payload schema, rule examples, Lambda handler, validation, and recovery

Load these references as needed for specific integration requirements.

Common Use Cases

1. Bulk Entity Import:

# Import multiple sequences from FASTA file
from Bio import SeqIO

for record in SeqIO.parse("sequences.fasta", "fasta"):
    benchling.dna_sequences.create(
        DnaSequenceCreate(
            name=record.id,
            bases=str(record.seq),
            is_circular=False,
            folder_id="fld_abc123"
        )
    )

2. Inventory Audit:

# List all containers in a specific location
containers = benchling.containers.list(
    parent_storage_id="box_abc123"
)

for page in containers:
    for container in page:
        print(f"{container.name}: {container.barcode}")

3. Workflow Automation:

# Update all pending tasks for a workflow
tasks = benchling.workflow_tasks.list(
    workflow_id="wf_abc123",
    status="pending"
)

for page in tasks:
    for task in page:
        # Perform automated checks
        if auto_validate(task):
            benchling.workflow_tasks.update(
                task_id=task.id,
                workflow_task=WorkflowTaskUpdate(
                    status_id="status_complete"
                )
            )

4. Data Export:

# Export all sequences with specific properties
sequences = benchling.dna_sequences.list()
export_data = []

for page in sequences:
    for seq in page:
        if seq.schema_id == "target_schema_id":
            export_data.append({
                "id": seq.id,
                "name": seq.name,
                "bases": seq.bases,
                "length": len(seq.bases)
            })

# Save to CSV or database
import csv
with open("sequences.csv", "w") as f:
    writer = csv.DictWriter(f, fieldnames=export_data[0].keys())
    writer.writeheader()
    writer.writerows(export_data)

Additional Resources

Official Documentation: https://docs.benchling.com
Python SDK Reference: https://benchling.com/sdk-docs/
API Reference: https://benchling.com/api/reference
Support: [email protected]

Voir la source ↗

Métadonnées du fichier

name: benchling-integration
description: Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
license: MIT
allowed-tools: Read Write Edit Bash
compatibility: Requires a Benchling account, tenant URL, and API key or OAuth app credentials. Install benchling-sdk with uv pip install.
metadata:
  version: "1.4"
  skill-author: K-Dense Inc.
  openclaw:
    primaryEnv: BENCHLING_API_KEY
    envVars:
    - name: BENCHLING_TENANT_URL
      required: true
      description: Benchling tenant base URL.
    - name: BENCHLING_API_KEY
      required: false
      description: API key auth (alternative to OAuth).
    - name: BENCHLING_CLIENT_ID
      required: false
      description: OAuth app client id.
    - name: BENCHLING_CLIENT_SECRET
      required: false
      description: OAuth app client secret.
    - name: BENCHLING_PROD_TENANT_URL
      required: false
      description: Production tenant URL (multi-env setups).
    - name: BENCHLING_PROD_API_KEY
      required: false
      description: Production API key (multi-env setups).
    - name: BENCHLING_STAGING_TENANT_URL
      required: false
      description: Staging tenant URL (multi-env setups).
    - name: BENCHLING_STAGING_API_KEY
      required: false
      description: Staging API key (multi-env setups).

Voir le texte original

---
name: benchling-integration
description: Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
license: MIT
allowed-tools: Read Write Edit Bash
compatibility: Requires a Benchling account, tenant URL, and API key or OAuth app credentials. Install benchling-sdk with uv pip install.
metadata:
  version: "1.4"
  skill-author: K-Dense Inc.
  openclaw:
    primaryEnv: BENCHLING_API_KEY
    envVars:
    - name: BENCHLING_TENANT_URL
      required: true
      description: Benchling tenant base URL.
    - name: BENCHLING_API_KEY
      required: false
      description: API key auth (alternative to OAuth).
    - name: BENCHLING_CLIENT_ID
      required: false
      description: OAuth app client id.
    - name: BENCHLING_CLIENT_SECRET
      required: false
      description: OAuth app client secret.
    - name: BENCHLING_PROD_TENANT_URL
      required: false
      description: Production tenant URL (multi-env setups).
    - name: BENCHLING_PROD_API_KEY
      required: false
      description: Production API key (multi-env setups).
    - name: BENCHLING_STAGING_TENANT_URL
      required: false
      description: Staging tenant URL (multi-env setups).
    - name: BENCHLING_STAGING_API_KEY
      required: false
      description: Staging API key (multi-env setups).
---

# Benchling Integration

## Overview

Benchling is a cloud platform for life sciences R&D. Access registry entities (DNA, RNA, proteins), inventory, electronic lab notebooks, and workflows programmatically via the Python SDK and REST API.

**Version note:** Examples target **benchling-sdk 1.25.0** (latest stable on PyPI). Docs: [benchling.com/sdk-docs](https://benchling.com/sdk-docs/). Platform guide: [docs.benchling.com](https://docs.benchling.com/).

## When to Use This Skill

This skill should be used when:
- Working with Benchling's Python SDK or REST API
- Managing biological sequences (DNA, RNA, proteins) and registry entities
- Automating inventory operations (samples, containers, locations, transfers)
- Creating or querying electronic lab notebook entries
- Building workflow automations or Benchling Apps
- Syncing data between Benchling and external systems
- Querying the Benchling Data Warehouse for analytics
- Setting up event-driven integrations with AWS EventBridge

## Core Capabilities

Seven capability areas, each with code, are in
[references/core_capabilities.md](references/core_capabilities.md):

1. **Authentication and setup** — API key and OAuth app auth; see
   [references/authentication.md](references/authentication.md).
2. **Registry and entity management** — DNA and AA sequences, custom entities, schemas,
   and registration.
3. **Inventory management** — containers, boxes, plates, locations, and transfers.
4. **Notebook and documentation** — entries, day-to-day notes, and structured tables.
5. **Workflows and automation** — tasks, flowcharts, and assay runs.
6. **Events and integration** — EventBridge subscriptions; see
   [references/eventbridge.md](references/eventbridge.md).
7. **Data warehouse and analytics** — SQL access to the warehouse.

Endpoint and SDK detail is in
[references/api_endpoints.md](references/api_endpoints.md) and
[references/sdk_reference.md](references/sdk_reference.md).

## Best Practices

### Error Handling

The SDK automatically retries failed requests:
```python
# Automatic retry for 429, 502, 503, 504 status codes
# Up to 5 retries with exponential backoff
# Customize retry behavior if needed
from benchling_sdk.retry import RetryStrategy

benchling = Benchling(
    url=tenant_url,
    auth_method=ApiKeyAuth(api_key),
    retry_strategy=RetryStrategy(max_retries=3),
)
```

### Pagination Efficiency

Use generators for memory-efficient pagination:
```python
# Generator-based iteration
for page in benchling.dna_sequences.list():
    for sequence in page:
        process(sequence)

# Check estimated count without loading all pages
total = benchling.dna_sequences.list().estimated_count()
```

### Schema Fields Helper

Use the `fields()` helper for custom schema fields:
```python
# Convert dict to Fields object
custom_fields = benchling.models.fields({
    "concentration": "100 ng/μL",
    "date_prepared": "2025-10-20",
    "notes": "High quality prep"
})
```

### Forward Compatibility

The SDK handles unknown enum values and types gracefully:
- Unknown enum values are preserved
- Unrecognized polymorphic types return `UnknownType`
- Allows working with newer API versions

### Security Considerations

- Never commit API keys or OAuth secrets to version control
- Read only named environment variables (`BENCHLING_TENANT_URL`, `BENCHLING_API_KEY`, etc.)
- Route network calls exclusively to your tenant URL
- Rotate keys if compromised; use OAuth for multi-user production apps
- Grant minimal necessary permissions for apps in the Developer Console

## Resources

### references/

Detailed reference documentation for in-depth information:

- **authentication.md** - Comprehensive authentication guide including OIDC, security best practices, and credential management
- **sdk_reference.md** - Detailed Python SDK reference with advanced patterns, examples, and all entity types
- **api_endpoints.md** - REST API endpoint reference for direct HTTP calls without the SDK
- **eventbridge.md** - EventBridge setup, event payload schema, rule examples, Lambda handler, validation, and recovery

Load these references as needed for specific integration requirements.

## Common Use Cases

**1. Bulk Entity Import:**
```python
# Import multiple sequences from FASTA file
from Bio import SeqIO

for record in SeqIO.parse("sequences.fasta", "fasta"):
    benchling.dna_sequences.create(
        DnaSequenceCreate(
            name=record.id,
            bases=str(record.seq),
            is_circular=False,
            folder_id="fld_abc123"
        )
    )
```

**2. Inventory Audit:**
```python
# List all containers in a specific location
containers = benchling.containers.list(
    parent_storage_id="box_abc123"
)

for page in containers:
    for container in page:
        print(f"{container.name}: {container.barcode}")
```

**3. Workflow Automation:**
```python
# Update all pending tasks for a workflow
tasks = benchling.workflow_tasks.list(
    workflow_id="wf_abc123",
    status="pending"
)

for page in tasks:
    for task in page:
        # Perform automated checks
        if auto_validate(task):
            benchling.workflow_tasks.update(
                task_id=task.id,
                workflow_task=WorkflowTaskUpdate(
                    status_id="status_complete"
                )
            )
```

**4. Data Export:**
```python
# Export all sequences with specific properties
sequences = benchling.dna_sequences.list()
export_data = []

for page in sequences:
    for seq in page:
        if seq.schema_id == "target_schema_id":
            export_data.append({
                "id": seq.id,
                "name": seq.name,
                "bases": seq.bases,
                "length": len(seq.bases)
            })

# Save to CSV or database
import csv
with open("sequences.csv", "w") as f:
    writer = csv.DictWriter(f, fieldnames=export_data[0].keys())
    writer.writeheader()
    writer.writerows(export_data)
```

## Additional Resources

- **Official Documentation:** https://docs.benchling.com
- **Python SDK Reference:** https://benchling.com/sdk-docs/
- **API Reference:** https://benchling.com/api/reference
- **Support:** [email protected]

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Licence: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
SKILL.md lacks an explicit limitations or troubleshooting section, so safe operating boundaries are not fully described.
The metadata marks BENCHLING_API_KEY as primaryEnv while also listing it as not required, which could confuse agents about which auth path to use.
The skill does not warn agents to treat Benchling API responses as untrusted data, which is relevant for prompt-injection safety when processing external records.
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Commencer par une petite tâche

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Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: K-Dense-AI/scientific-agent-skills
Licence: MIT
Version: 1.0.0
Dernier push GitHub: 30 août 2026
Registre mis à jour: 1 sept. 2026
Chemin des instructions: skills/benchling-integration/SKILL.md

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

89/100

Excellent

Confiance

59/100

Do not auto-install

Audit

79/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Dependency or permission surface needs review
Permission surface may require sandboxing
SKILL.md lacks an explicit limitations or troubleshooting section, so safe operating boundaries are not fully described.
The metadata marks BENCHLING_API_KEY as primaryEnv while also listing it as not required, which could confuse agents about which auth path to use.
The skill does not warn agents to treat Benchling API responses as untrusted data, which is relevant for prompt-injection safety when processing external records.
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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    "best_for": [
      "data-analysis",
      "agent-skill"
    ],
    "known_risks": [
      "SKILL.md lacks an explicit limitations or troubleshooting section, so safe operating boundaries are not fully described.",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 79,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "SKILL.md lacks an explicit limitations or troubleshooting section, so safe operating boundaries are not fully described.",
      "The metadata marks BENCHLING_API_KEY as primaryEnv while also listing it as not required, which could confuse agents about which auth path to use.",
      "The skill does not warn agents to treat Benchling API responses as untrusted data, which is relevant for prompt-injection safety when processing external records.",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 89,
    "label": "Excellent"
  },
  "supply": {
    "track": "Data, BI, and analytics",
    "scenario": "Research agents",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "SKILL.md lacks an explicit limitations or troubleshooting section, so safe operating boundaries are not fully described.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The metadata marks BENCHLING_API_KEY as primaryEnv while also listing it as not required, which could confuse agents about which auth path to use.",
    "The skill does not warn agents to treat Benchling API responses as untrusted data, which is relevant for prompt-injection safety when processing external records."
  ],
  "agent_contract": {
    "task_input": "Use benchling-integration in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 67/100 Manual review",
      "Audit: 79/100 Needs review",
      "Safety: 35/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "k-dense-ai-benchling-integration (benchling-integration)",
      "install_command": "npx skills add K-Dense-AI/scientific-agent-skills --skill benchling-integration",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "k-dense-ai-benchling-integration",
      "task": "Use benchling-integration in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/k-dense-ai-benchling-integration",
    "api": "https://www.openagentskill.com/api/agent/skills/k-dense-ai-benchling-integration",
    "audit": "https://www.openagentskill.com/skills/k-dense-ai-benchling-integration/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=k-dense-ai-benchling-integration&task=Use%20benchling-integration%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20benchling-integration%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20benchling-integration%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/k-dense-ai-benchling-integration/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/k-dense-ai-benchling-integration"
  }
}

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Créateur: K-Dense-AI
Source: K-Dense-AI/scientific-agent-skills
Indexé par: Index communautaire OpenAgentSkill

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