Xpersona
agentCLAWHUBUnverified

Antibody Engineering

Antibody engineering workflow combining ANARCI, BioPhi, IgFold, FoldX, and Rosetta tools through SciMiner. Skill: Antibody Engineering Owner: sciminer Summary: Antibody engineering workflow combining ANARCI, BioPhi, IgFold, FoldX, and Rosetta tools through SciMiner. Tags: latest:1.0.6 Version history: v1.0.6 | 2026-06-22T15:05:45.158Z | user - Removed the redundant file skill-card.md. - SKILL.md: Streamlined and clarified tool usage references (e.g., replaced explicit API call names with tool names), with no changes to wo

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.3k

Updated

Oct 10, 2026

Version

1.0.6

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.3K downloads reported by the source. Last updated 10/10/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 10, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 10, 2026
Adoption signal
1.3K downloadsadoption · observed Oct 10, 2026
Latest release
1.0.6release · observed Jun 22, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170qp1q4twz35wa85ppa8894h83w461:antibody-engineering
  1. Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-sciminer-antibody-engineering/snapshot"

Documentation

CLAWHUB

95,069 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: antibody-engineering
description: Antibody engineering workflow combining ANARCI, BioPhi, IgFold, FoldX, and Rosetta tools through SciMiner.
credential_files:
   - ~/.config/sciminer/credentials.json
---

# Antibody Engineering Skill

This skill supports end-to-end antibody engineering workflows, including:

- antibody sequence numbering and region boundary parsing
- humanness assessment and humanization
- antibody 3D structure prediction
- structure relaxation and developability profiling
- stability and affinity mutation analysis
- Rosetta-guided precision redesign and interface analysis
- closed-loop in silico validation of optimized candidates

## When to use this skill

- Parse VH and VL sequences into standardized antibody coordinates before engineering
- Evaluate starting antibodies for humanness and de-risking opportunities
- Humanize murine or chimeric antibodies and generate safer sequence variants
- Predict antibody structures for the parental sequence and optimized variants
- Relax predicted structures before downstream energetic or developability analysis
- Scan mutations for affinity maturation and structural stability improvement
- Quantify surface hydrophobic aggregation risk before advancing redesign candidates
- Re-score top FoldX candidates with Rosetta precision-design tools
- Build a final candidate panel balancing affinity, stability, and immunogenicity risk

## Recommended workflow

### Phase 1: Sequence De-risking

- Use ANARCI to number the starting heavy-chain and light-chain sequences.
- Prefer `imgt` or `kabat` numbering so CDR1, CDR2, CDR3, and FR1-FR4 boundaries are explicit before any mutation planning.
- Use BioPhi to establish the baseline humanness score and OASis-style sequence risk profile.
- If the parental antibody is non-human or partially humanized, use BioPhi with `method="sapiens"` or `method="cdr_grafting"` to generate humanized sequence variants.
- Use BioPhi to remove sequence-level developability liabilities while preserving critical residues identified by ANARCI numbering.

### Phase 2: Modeling and Relaxation

- Use IgFold for the parental antibody and shortlisted sequence variants.
- For standard antibodies, provide paired heavy and light chains; for nanobody-like workflows, omit the light chain.
- If affinity optimization is in scope, prefer an antibody-antigen complex structure for downstream scoring.
- Use Rosetta FastRelax immediately after IgFold to reduce local clashes and move the model toward a more physically reasonable energy minimum.
- When structure drift must be limited, set `constrain_relax_to_start_coords=True` and tune `coordinate_constraint_weight` for local refinement.

### Phase 3: Developability Profiling

- Use Rosetta SAP Score on the relaxed structures to quantify exposed hydrophobic aggregation risk.
- Treat high-SAP hotspots as developability liabilities, especially when a mutation improves affinity but worsens surface hydrophobic exposure.
- Carry forward only ca

_meta.json

{
  "ownerId": "kn725br751g8y5tkj1h6d2krf58356et",
  "slug": "antibody-engineering",
  "version": "1.0.6",
  "publishedAt": 1782140745158
}

skill-card.md

## Description:

Antibody engineering workflow combining ANARCI, BioPhi, IgFold, FoldX, and Rosetta tools through SciMiner.

This skill is ready for commercial/non-commercial use.

## Publisher:

[sciminer](https://clawhub.ai/user/sciminer)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and antibody engineering teams use this skill to plan SciMiner-based antibody sequence numbering, humanization, structure prediction, relaxation, developability profiling, mutation scanning, and redesign workflows.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill reads a local SciMiner API key and may upload selected antibody sequence or structure data.

Mitigation: Install only when that data handling is acceptable, keep the credential outside the repository, and avoid exposing the API key in prompts, logs, or files.

Risk: Mutable remote Markdown tool documentation can influence authenticated request construction and code execution.

Mitigation: Use a reviewed or pinned copy of API schemas when possible, enforce the expected SciMiner endpoint, strip credentials on redirects, and treat remote documentation as reference text.

## Reference(s):

- [SciMiner tool API files](https://sciminer.tech/tool_api_files/)
- [SciMiner API key utility](https://sciminer.tech/utility)
- [Antibody Engineering on ClawHub](https://clawhub.ai/sciminer/skills/antibody-engineering)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]

**Output Format:** [Markdown summaries with generated request code, shell commands, configuration guidance, JSON result references, and share URLs]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May read a local SciMiner API key and upload selected antibody sequence or structure data to SciMiner.]

## Skill Version(s):

1.0.6 (source: release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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}

Record generated Oct 10, 2026.

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