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Agent Skill

Complexa Target skill

complexa-targetNVIDIA/skillsskills/bionemo-complexa-target/SKILL.md

Add, edit, inspect, or validate Proteina-Complexa target definitions for protein binders, ligand binders, and AME motif scaffolding. Use for target registries, chain/residue selectors, hotspots, ligand fields, and incomplete motif inputs. Covers direct YAML edits and complexa target/validate target commands; running a campaign with an already registered target belongs to complexa-design.

3.7kApache-2.0

always on

83 tokname + description

once used

3,590 tokSKILL.md body

bundled files

1872 KB

repo stars

3.7kGitHub

Install the Complexa Target skill

Skills CLI (Claude Code, Codex, Cursor and others)

npx skills add https://github.com/NVIDIA/skills --skill complexa-target

Claude Code, by hand (all projects)

git clone --depth 1 https://github.com/NVIDIA/skills.git /tmp/skills
mkdir -p ~/.claude/skills
cp -R /tmp/skills/skills/bionemo-complexa-target ~/.claude/skills/complexa-target

For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.

What it costs your context

Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.

WhenWhat loadsCost
Every sessionName and description83 tok
When the agent uses itThe SKILL.md instructions3,590 tok
Only if opened18 bundled files (72 KB)varies

Until it is used, complexa-target costs about 16× less than the median MCP server we measured (1,363 tokens on every turn). Installing all 150 skills from NVIDIA/skills would load 20,796 tokens of descriptions. Skills vs MCP study

Checks

  • The name doesn't match its folder name, as the specification requires.

Pre-approved tools (allowed-tools): Bash, Read, Write, AskUserQuestion. The agent can use these without asking while the skill is active.

Checked 2026-10-22 · method

What's inside

The headings of its SKILL.md. Read the full file on GitHub.

  • complexa-target
  • Instructions
  • Step 1: Decide the target type
  • AME tasks (enzyme pipeline)
  • Step 2: Gather required info
  • Protein target
  • Ligand target — protein fields above (minus targetinput), plus:
  • Examples
  • Step 3a: Append the YAML block directly (preferred)
  • Step 3b: CLI alternative (complexa target add)
  • Confirmed flags (from src/proteinfoundation/cli/targetcli.py)
  • Step 4: Verify
  • Step 5: Emit artifact
  • Hardware requirements
  • Troubleshooting
  • Reference

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