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NVIDIA

Complexa Target skill

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.

$npx skills add NVIDIA/skills --skill complexa-target
View this skill on GitHubNVIDIA/skills/skills/bionemo-complexa-target/SKILL.md
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Updated
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License
Apache-2.0
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NVIDIA

Complexa Target

Install Complexa Target

Pick your agent. Every command is ready to paste.

$npx skills add NVIDIA/skills --skill complexa-target

The open skills CLI detects your agents (Claude Code, Cursor, Codex and 70+ more) and installs to the right folder. Add -g to install globally.

01

Install it

Run the command above, or upload the folder in Claude.

02

Ask naturally

Describe the task. Your agent loads complexa-target when the request matches its description.

03

Name it if needed

If it does not trigger, say “use the complexa-target skill”. In Codex, type $complexa-target.

The skill itself

SKILL.md

FolderGitHub

complexa-target

Add or edit a design target in Proteina-Complexa. Targets live in three YAML files, one per complexa design pipeline:

  • configs/targets/targets_dict.yaml — protein binder (default pipeline)
  • configs/targets/ligand_targets_dict.yaml — ligand binder
  • configs/design_tasks/ame_dict_v2.yaml — AME / enzyme scaffolding

The complexa target CLI only manages the first two. AME tasks use an extended schema (the same core fields as ligand targets plus contig_atoms for hand-curated per-residue motif atom selections) and are file-edit-only.

Instructions

complexa target add is a thin wrapper around "load YAML → build dict → append a block" (see src/proteinfoundation/cli/target_manager.py:add_target_cli and append_target_to_dict). For agentic use, just edit the targets dict directly — the schema is short, the existing entries are great copy templates, and you skip 14 CLI flags / SMILES shell-escaping. Use the CLI only if you explicitly want its YAML auto-quoter or its overwrite-prompt safety.

For an offline edit, work on the supplied registry or requested output copy. When a copy is requested, create its parent directory and copy into an absent destination. If the destination already exists, inspect and preserve it: reuse it when it is the intended working copy, otherwise report the path conflict. Do not delete the output tree to restart a configuration edit. An installed Complexa runtime, GPU, or target structure is not required to write the configuration. Use the field schema below, preserve unrelated entries, and read back the saved block. Coordinate validation remains pending when the structure is absent; finish the configuration task without installing a runtime. With Read/Edit tools, read the destination copy before editing it; reading the original input path does not satisfy Edit's requirement for the output path.

Use the schema below and the selected registry; the CLI reference is needed only for CLI use. Finish with the saved entry, its consumer pipeline, the downstream generation.task_name, and the validation status.

Step Direct file edit (preferred) CLI
Look at existing targets Read configs/targets/{,ligand_}targets_dict.yaml (or configs/design_tasks/ame_dict_v2.yaml for AME) complexa target list (protein/ligand only)
Look at one target Read the dict and grep for the key complexa target show NAME
Add a new target Append a YAML block (Step 3a) complexa target add ... (Step 3b)
Verify configuration Parse the saved YAML and compare the changed entry and untouched keys complexa target show NAME --dict PATH
Check runtime PDB-path resolution Requires the full pipeline and environment complexa validate target CONFIG --target NAME

Step 1: Decide the target type

Targets live in three different files, one per complexa design pipeline. Pick the file by working out which pipeline the user will run, then add the entry to that file. Each file is consumed by exactly one pipeline.

User intent Pipeline Targets dict file This skill?
Bind a protein surface (PD-L1, IFNAR2, TNF-α, …) — default configs/search_binder_local_pipeline.yaml configs/targets/targets_dict.yaml Yes — Step 2 (protein)
Bind a small-molecule pocket (FAD, SAM, OQO, …) configs/search_ligand_binder_local_pipeline.yaml configs/targets/ligand_targets_dict.yaml Yes — Step 2 (ligand)
AME / enzyme scaffolding (motif + ligand, M####_<pdb> names) configs/search_ame_local_pipeline.yaml configs/design_tasks/ame_dict_v2.yaml Partial — see "AME tasks" below

AME tasks (enzyme pipeline)

AME tasks live under motif_target_dict_cfg: with source, target_filename, ligand, contig_atoms, binder_length, and use_bonds_from_file. The contig_atoms string contains curated chain/residue/atom selections. Existing entries illustrate the field structure, not the selections for a new motif. If motif atoms, ligand selection, or structure location are missing, list the missing inputs and write a separate, explicitly incomplete template if useful. Leave the registry unchanged until those scientific inputs are supplied. A task name or PDB ID alone does not determine them. In the missing-input handoff, explain that registration will be a direct edit under motif_target_dict_cfg once the curated selections are supplied; complexa target add cannot register AME tasks. Completed entries are selected with ++generation.task_name=<NAME> and configs/search_ame_local_pipeline.yaml.

Step 2: Gather required info

Use the supplied values and ask only for missing required information. Required fields differ for protein, ligand, and AME definitions.

Protein target

Field Question Example Required
name "Target name (used as the dict key and task_name)?" 02_PDL1, MyTarget_v1 yes
source "Source directory under $DATA_PATH/target_data/?" bindcraft_targets, custom_targets yes (or target_path)
target_filename "PDB filename (no .pdb extension)?" PD-L1, IFNAR2 yes (or target_path)
target_input "Chain + residue range — see reference for grammar." A1-115, A1-50,B1-50 yes
hotspot_residues "Hotspot residues (interface contact residues)?" ["A33", "A95", "A102"] optional, recommended
binder_length "Binder length range [min, max] or single [length]?" [80, 150], [100] optional (default [60, 120])
pdb_id "Reference PDB ID (optional, metadata only)?" "2lag" optional

Hotspot selectors encode chain + residue number, not amino-acid identity. For example, Tyr42 (Y42) on explicitly specified chain B becomes B42; already supplied chain/residue selectors stay unchanged. Preserve the user's residue numbering. Without coordinates, report that residue identities and membership in the structure are unverified.

Ligand target — protein fields above (minus target_input), plus:

Field Question Example Required
ligand "3-letter PDB ligand residue code?" FAD, OQO, SAM yes (presence marks target as ligand)
SMILES "SMILES string for the ligand?" "O=C2C3=Nc1cc(c(...)..." recommended; uppercase YAML key
ligand_only "Generate pocket around ligand only (no protein-protein interface)?" true / false optional (default true)
use_bonds_from_file "Use bond info from the input PDB/CIF?" true / false optional (default true)
target_input not required for ligand targets — no

Check whether the proposed key already exists in the selected YAML mapping. Preserve an existing entry unless the user requested that entry's update.

Copy a supplied SMILES exactly. Chain-specific ligand selection belongs to preparation of the input structure; do not invent a chain field or protein target_input to represent it in the ligand entry.

Examples

Step 3a: Append the YAML block directly (preferred)

Open configs/targets/targets_dict.yaml (or ligand_targets_dict.yaml for ligand targets), find a similar existing entry as a style template, and append the new block under target_dict_cfg:. Two-space indent, single blank line between entries.

Protein template
  02_PDL1:
    source: bindcraft_targets
    target_filename: PD-L1
    target_input: "A1-115"
    hotspot_residues: ["A37", "A39", "A49", "A98"]
    binder_length: [64, 155]
    pdb_id: "4z18"

Rules to match the existing file style (mirrors what complexa target add would emit):

  • Quote chain/residue ranges ("A1-115", "A33") and any SMILES — flow-style strings get tripped up by : and brackets otherwise.
  • Use flow-style lists (["A33", "A95"], [64, 155]) — that's what the on-disk dump produces.
  • target_input, source, target_filename are required for protein. target_path (absolute path) can replace source + target_filename if the PDB lives outside $DATA_PATH/target_data/.
Ligand template
  41_7BKC_LIGAND:
    source: ligand_targets
    target_filename: 7BKC_ligand_centered
    hotspot_residues: [null]
    binder_length: [100]
    pdb_id: 7BKC
    ligand: 'FAD'
    ligand_only: True
    SMILES: "O=C2C3=Nc1cc(c(cc1N(C3=NC(=O)N2)CC(O)C(O)C(O)COP(=O)(O)OP(=O)(O)OCC6OC(n5cnc4c(ncnc45)N)C(O)C6O)C)C"
    use_bonds_from_file: True

The presence of the ligand: key flips the target into ligand mode — there is no separate is_ligand flag. target_input is not required for ligands.

After saving, skip to Step 4 to verify.

Step 3b: CLI alternative (complexa target add)

Use the CLI when you want its automatic chain/residue quoting, the overwrite-confirm prompt, or to wire target creation into a non-Python script. Prefer non-interactive mode for agentic use (-i / --editor opens an editor and blocks).

Confirmed flags (from src/proteinfoundation/cli/target_cli.py)

Read target_schema.md for the flag table and worked CLI examples when this path is needed. Select the intended dictionary with --dict; --source and --target-filename should be explicit when they differ from the CLI defaults. AME registration remains a direct edit.

Step 4: Verify

First parse the saved YAML with an available YAML parser and check the selected mapping, field types, requested values, and preservation of other entries. Read back the resulting block. This verifies configuration editing only.

With a prepared Complexa installation, pipeline configs, environment, and target structure, additionally check runtime path resolution from that checkout:

# 1. Select the dictionary that was actually edited.
complexa target show 02_PDL1 --dict configs/targets/targets_dict.yaml

# 2. Resolve the PDB path and validate it exists on disk.
#    This traverses Hydra defaults to find the target dict.
complexa validate target configs/search_binder_local_pipeline.yaml --target 02_PDL1

complexa validate target (from src/proteinfoundation/cli/validate.py::validate_target) checks:

  • DATA_PATH is set and target_data/ exists.
  • The target name resolves in target_dict_cfg.
  • Either target_path exists, or $DATA_PATH/target_data/<source>/<target_filename>.pdb exists.
  • target_input, hotspot_residues, binder_length are reported back so a human can sanity-check them.

For ligand targets, use configs/targets/ligand_targets_dict.yaml for show and configs/search_ligand_binder_local_pipeline.yaml for validation; --target is the target key, not a config path. This validator does not check residue identities, ligand chemistry, or AME contig_atoms; do not describe a path check as coordinate or scientific validation.

Step 5: Emit artifact

Save the edited entry and validation status at the user's requested output location (otherwise ./target_<name>/). For an offline edit, the saved YAML block is sufficient; no CLI-generated artifact is required. On a prepared installation, a target-show record can also be saved:

mkdir -p target_02_PDL1
complexa target show 02_PDL1 > target_02_PDL1/target_show.txt

If a standalone definition is requested, save the actual entry under its registry key in target_definition.yaml. The handoff should include the saved path, target key, consumer pipeline from Step 1, and ++generation.task_name=<NAME>, together with what validation actually ran. For an incomplete AME request, report the separate template, missing inputs, and the direct registry edit needed after those inputs arrive.

Hardware requirements

None for target definition — this is a YAML edit, not a training/inference step. Disk impact is a few KB appended to the targets dict (plus an automatic .yaml.bak backup written by save_targets_dict).

For the downstream design / evaluate runs that consume the target, defer to complexa-design and references/hardware.md.

Troubleshooting

Symptom Cause Fix
Target PDB file: File not found from validate target <source>/<target_filename>.pdb does not exist under $DATA_PATH/target_data/ Confirm the PDB stem and source dir. Use --target-path /full/path.pdb if the file lives outside target_data/.
"Target 'X' already exists! Overwrite? (y/N)" Name collision in dict Either pick a new name, or pass -f / --force to overwrite.
Hotspot residue not in PDB Wrong chain or residue number Open the PDB, re-check chain letters (case-sensitive) and residue indices. Hotspots use the format <CHAIN><RESNUM> — see reference.
Chain not found target_input references a chain that does not exist in the PDB Inspect the PDB with grep "^ATOM" target.pdb | awk '{print $5}' | sort -u.
Ligand code missing from PDB The 3-letter ligand code does not appear as a HETATM residue name in the file Open the PDB and check HETATM lines; you may need a _ligand_centered variant of the PDB.
SMILES parse failure downstream Bad SMILES string Validate with rdkit.Chem.MolFromSmiles(smiles) before adding. Quote SMILES in shell to escape brackets and parens.
Target name with leading digit breaks Hydra interpolation YAML treats 02_PDL1 as a string fine; some override syntaxes need quoting Use ++generation.task_name=02_PDL1; quote if the shell strips characters.
target_input appears ignored for a ligand target By design — ligand targets do not use target_input; pocket is defined by the ligand Leave it unset for ligand targets.

Reference

  • references/target_schema.md — every field, chain-spec grammar, AME task-name grammar, three worked examples.
  • configs/targets/targets_dict.yaml — live protein entries (copy a known-good one as a template).
  • configs/targets/ligand_targets_dict.yaml — live ligand entries.
  • configs/design_tasks/ame_dict_v2.yaml — AME task definitions (file-edit only, not exposed via complexa target CLI).
  • src/proteinfoundation/cli/target_cli.py — argparse source of truth.
  • src/proteinfoundation/cli/target_manager.py — add_target_cli, list_targets, show_target, schema in TARGET_FIELDS.
  • src/proteinfoundation/cli/validate.py — validate_target implementation.

Tips for Complexa Target

  • • Start a fresh session after installing so your agent picks up the new skill.
  • • It bundles reference files. The agent reads them only when needed, so they cost no context until then.
  • • It is part of a pack with 30 skills. Install only the ones you need with --skill to keep triggers precise.
  • • If two skills overlap, mention the one you want by name.

Questions about Complexa Target

What does the Complexa Target skill do?

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.

How do I install Complexa Target?

Run "npx skills add NVIDIA/skills --skill complexa-target" in your project. The skills CLI installs it for Claude Code, Cursor, Codex and 70+ other agents. In Claude on the web, upload the skill folder as a ZIP under Customize > Skills.

Does Complexa Target work in Cursor and Codex?

Yes. Complexa Target uses the open SKILL.md format, so it works in Claude Code, Claude, Cursor, OpenAI Codex, Gemini CLI, GitHub Copilot and other agents that support agent skills.

Is Complexa Target free?

Complexa Target is open source on GitHub under the Apache-2.0 license. It is free to install; check the license before commercial use.

Who made Complexa Target?

Complexa Target is published by NVIDIA in the NVIDIA/skills repository, together with 29 other skills.

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