Boltz Protein Screen
OpenAIOfficialCreated by BoltzDistributed by OpenAI
- Skill
- Tool integration
- Science and research
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The boltz-protein-screen skill provides a structured workflow for evaluating candidate protein binders—such as peptides, antibodies, or nanobodies—against a defined biological target. It handles the submission of binder libraries, manages cost estimation, and automates the retrieval and ranking of results based on binding confidence metrics. This skill is designed for screening existing libraries rather than de novo protein design or small molecule screening.
What it does
- Normalizes binder libraries into complex entities for screening.
- Supports target definition via existing 3D structures or raw sequences.
- Provides cost estimation before job submission.
- Automates the background download and monitoring of screening results.
- Ranks screening hits based on binding confidence and interaction metrics.
When to use it
- When you have a library of candidate proteins, peptides, or antibodies to test.
- When you need to rank binders against a specific target structure or sequence.
- When you require a systematic, automated approach to process large binder libraries.
- When you need to identify high-confidence binders from a pre-existing set.
Advantages
- Streamlines the end-to-end process from library submission to result ranking.
- Ensures cost transparency through mandatory estimation steps.
- Provides clear ranking criteria using standard binding metrics.
- Handles complex multi-chain candidates like antibodies.
Usage examples
- “Screen my library of 50 nanobody sequences against the target protein in target.cif.”
- The agent will normalize the sequences, prepare the payload using the provided CIF file, estimate the cost, and upon confirmation, submit the job and monitor the download.
- “Rank the results from my recent protein screen job.”
- The agent will parse the index.jsonl file in the output directory and present the candidates sorted by binding_confidence, using iptm as a tiebreaker.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/openai/boltz-protein-screen/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip boltz-protein-screen.zip -d .agents/skills
Documentation by Boltz
Workflow
If boltz-api is missing from PATH, use boltz-cli-setup for install/update guidance before retrying.
If a command reports missing or expired authentication, use boltz-cli-setup to start boltz-api auth login --device-code before retrying; do not ask permission first.
If the agent host sandbox blocks boltz-api install/auth/API calls, use boltz-cli-setup to request the host sandbox bypass/escalation needed for user-wide CLI install, browser login, credential storage, temp files, or API access before retrying.
Use this skill when the user already has candidate proteins / peptides / antibodies / nanobodies.
Normalize the binder library into
proteins— a list of candidate complexes. For a simple sequence library each entry has one protein entity; multi-chain candidates (antibody heavy+light) are also allowed.Pick the target variant:
structure_template— user has a CIF/PDB file or URL; select which chains are polymer vs ligand, which residues to keep (crop_residues), and optionallyepitope_residues/flexible_residues.no_template— user has only sequences; pass them astarget.entitiesplus optionalepitope_residues.
Don't add
bonds/constraintsunless the user asks for geometry constraints.Author the payload YAML or JSON, run
estimate-cost, show the USD cost, wait for explicit confirmation.startto submit. Capture the ID.Launch
download-resultswith the agent runtime's background/non-blocking command facility. In Claude Code, use Bash withrun_in_background: true. In Codex, rundownload-resultsas a foreground shell command withyield_time_ms: 1000; if Codex returns asession_id, keep it for optional same-thread polling, but treatdownload-statusplus the run directory as the durable source of truth. In Codex app/desktop runtimes that expose same-thread heartbeat automations, create a heartbeat that checksdownload-statusperiodically and posts a concise completion or failure update when the download reaches a terminal state. After launching the downloader, always report the job ID, run name, and output directory. Include the next check cadence if the heartbeat was created; otherwise include thedownload-statuscommand.Rank hits from
<output-root>/<run-name>/results/index.jsonlbybinding_confidencedescending. Useiptmandmin_interaction_paeas tiebreakers.optimization_scoreis not emitted for this endpoint. Read references/results.md for output layout and metric details.
Command Pattern
# Replace placeholders with concrete absolute paths before running.
# Use a short descriptive run name, for example: protein-screen-<target>-<library>-v1
boltz-api protein:library-screen estimate-cost \
--input @yaml:///absolute/path/payload.yaml
boltz-api protein:library-screen start \
--idempotency-key "<run-name>" \
--input @yaml:///absolute/path/payload.yaml \
--raw-output --transform id
# Copy the printed job ID into this command, then launch it in the agent
# runtime's background/non-blocking mode.
# Claude Code: Bash with run_in_background=true.
# Codex: foreground shell command with yield_time_ms=1000; keep the returned session_id if one is provided.
# Do not append "&" or use nohup in Codex.
boltz-api download-results \
--id "<job-id-from-start>" --name "<run-name>" \
--root-dir "/absolute/path/boltz-experiments" \
--poll-interval-seconds 30Payload keys are proteins, target — API body field names.
Always Do This
For
structure_template, embed CIF/PDB bytes with@data:///abs/path/target.cifinside thestructure.datafield. Don't use bare@path(automatic file-type detection once sent CIF as plain text into a base64 field and broke the server parser).Residue indices are 0-based.
epitope_residuesandflexible_residuesmust be subsets ofcrop_residues.Keep payload field names exactly as the API body names shown in
references/api.md.Use absolute paths for the output root, payload files, and embedded target files. Do not
cdinto the run directory for follow-up commands; pass the same--root-dirand use absolute paths so later relative paths do not drift.Prefer one merged top-level payload via
--input @yaml:///absolute/path/payload.yamlor@json:///absolute/path/payload.jsonforestimate-costandstart. Keep--idempotency-keyand--workspace-idtop-level; if they also appear inside--input, the top-level flags win.Direct object flags still work as overrides, such as
--target @yaml:///absolute/path/target.yamlor repeated--protein @json:///absolute/path/protein-1.jsonentries. Piped YAML / JSON on stdin also works, but it must use API body field names. Never use@file://or@./.Use the same slug as both
--idempotency-keyand--name.In permission-gated agents such as Claude Code, keep each Boltz call as a top-level command that starts with
boltz-api. Prefer concrete arguments oversh -c, inline environment assignments, aliases, wrapper scripts, loops, or pipelines around theboltz-apiinvocation unless the user already allowed that exact command form. Use--raw-output --transform id, read the printed ID, then paste that literal ID into the nextdownload-resultscommand.Prefer the agent runtime's background/non-blocking command mode for
download-results. In Codex specifically, keepdownload-resultsin the foreground and set the shell tool yield to 1000 ms; Codex will return asession_idif the command is still running. Do not append&or usenohupin Codex because the tool runner may clean up shell-backgrounded descendants before.boltz-run.jsonis fully written.After the background/session starts, do not manually wait on it or run ad hoc polling loops. Wall-clock time scales roughly with the number of candidates in the library: under 100 often finishes in a few minutes, 100-1,000 may take several minutes to tens of minutes, and larger screens can take longer or hours depending on inputs and system load. Don't quote a fixed duration.
--poll-interval-seconds 30is a reasonable downloader default.download-resultsemits JSONL progress on stderr by default; add--progress-format text --verboseonly when you explicitly want human-readable logs.In Codex app/desktop runtimes with same-thread heartbeat automation support, schedule a heartbeat after launching
download-results. The heartbeat should runboltz-api --format json download-status --name "<run-name>" --root-dir "/absolute/path/boltz-experiments"and stop once terminal. Choose cadence by candidate count: under 100 -> every 1-2 minutes; 100-1,000 -> every 5 minutes; over 1,000 -> every 15 minutes. Post only material status changes or terminal completion/failure. Poll the savedsession_idwith an emptywrite_stdinonly for interactive, user-requested progress checks. Never run a manual poll loop in the current turn.If the current host has no heartbeat automation support, do not claim an automatic next check. Report the job ID, run name, output directory, and the command needed to check
download-status.If detached download needs to be restarted, re-run
boltz-api download-resultswith the same--name "<run-name>"and the same--root-dir.Cost is tiered by total complex length (target + candidate); the combined length sets the tier. Do not state or estimate a dollar figure yourself — to say anything about cost, run
estimate-costand quote only the number it returns.
Escape Hatch
Payload reference: https://api.boltz.bio/docs/api/python/resources/protein/subresources/library_screen/methods/start
CLI flag names:
boltz-api protein:library-screen start --help
Read references/api.md for the proteins list shape and both target variants (structure_template with chain_selection, and no_template with epitope hints). Read references/results.md after download when ranking screened binders or explaining outputs.
Outputs
Rank from results/index.jsonl after download-results; use references/results.md for local file layout and metric meanings.
Files
- ATTRIBUTION.mdAdded by AutoGineer918 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md8.3 KiB
- agents/openai.yaml226 B
- references/api.md9.0 KiB
- references/results.md791 B
agents/openai.yaml
interface: display_name: "Boltz Protein Screen" short_description: "Screen protein binders against a target" default_prompt: "Use $boltz-protein-screen to estimate cost, confirm spend, and rank these candidate binders."
Requirements and compatibility
Written for: OpenAI Codex
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
No issues found by our static scan· No issues found
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Attribution and licence
- Created by
- Boltz
- Publisher
- OpenAI (Official)
- Distributed by
- OpenAI
- Source
- openai/plugins · plugins/boltz-api-cli/skills/boltz-protein-screen · commit d420d13
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from plugins/boltz-api-cli/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Boltz or OpenAI.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download boltz-protein-screen.zip