본문으로 건너뛰기
cccskills
무료GitHub에 공개

boltz-protein-screen

Screen supplied proteins, peptides, antibodies, or nanobodies against a target with Boltz. Prepares inputs, obtains a cost estimate for approval, downloads results, and ranks candidates by binding confidence, a score for predicted binding reliability.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Screen existing protein binders with Boltz. Use when ranking a supplied protein, peptide, antibody, nanobody, or binder library against a target. Not for designing new proteins or screening small molecules.

설치 방법 보기

이럴 때 유용합니다

  • Rank existing protein binders against a target
  • Compare antibody or nanobody candidates
  • Screen candidates using target sequences alone
  • Estimate screening cost before submitting a job

포함된 파일(4)

  • SKILL.md8.3 KB
  • agents/openai.yaml226 B
  • references/api.md9.0 KB
  • references/results.md791 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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.

  1. 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.
  2. 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 optionally epitope_residues / flexible_residues.
    • no_template — user has only sequences; pass them as target.entities plus optional epitope_residues.
  3. Don't add bonds / constraints unless the user asks for geometry constraints.
  4. Author the payload YAML or JSON, run estimate-cost, show the USD cost, wait for explicit confirmation.
  5. start to submit. Capture the ID.
  6. Launch download-results with the agent runtime's background/non-blocking command facility. In Claude Code, use Bash with run_in_background: true. In Codex, run download-results as a foreground shell command with yield_time_ms: 1000; if Codex returns a session_id, keep it for optional same-thread polling, but treat download-status plus the run directory as the durable source of truth. In Codex app/desktop runtimes that expose same-thread heartbeat automations, create a heartbeat that checks download-status periodically 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 the download-status command.
  7. Rank hits from <output-root>/<run-name>/results/index.jsonl by binding_confidence descending. Use iptm and min_interaction_pae as tiebreakers. optimization_score is 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 30

Payload keys are proteins, target — API body field names.

Always Do This

  • For structure_template, embed CIF/PDB bytes with @data:///abs/path/target.cif inside the structure.data field. 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_residues and flexible_residues must be subsets of crop_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 cd into the run directory for follow-up commands; pass the same --root-dir and use absolute paths so later relative paths do not drift.
  • Prefer one merged top-level payload via --input @yaml:///absolute/path/payload.yaml or @json:///absolute/path/payload.json for estimate-cost and start. Keep --idempotency-key and --workspace-id top-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.yaml or repeated --protein @json:///absolute/path/protein-1.json entries. 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-key and --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 over sh -c, inline environment assignments, aliases, wrapper scripts, loops, or pipelines around the boltz-api invocation 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 next download-results command.
  • Prefer the agent runtime's background/non-blocking command mode for download-results. In Codex specifically, keep download-results in the foreground and set the shell tool yield to 1000 ms; Codex will return a session_id if the command is still running. Do not append & or use nohup in Codex because the tool runner may clean up shell-backgrounded descendants before .boltz-run.json is 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 30 is a reasonable downloader default. download-results emits JSONL progress on stderr by default; add --progress-format text --verbose only 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 run boltz-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 saved session_id with an empty write_stdin only 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-results with 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-cost and quote only the number it returns.

Escape Hatch

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.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Review charts, diagrams, and dashboards for accessibility, including color contrast, keyboard access, and text alternatives. Get specific guidance for preserving key findings in screen readers, static exports, and reduced-motion views.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Reviewing dashboard colors and keyboard access
  • Writing chart alt text and long descriptions
  • Preparing accessible figures for PDF reports
openai/plugins7.5천2026년 10월 8일 업데이트

Give a photo collection a consistent look using Adobe tools to adjust color, tone, presets, and optional crops. Review a before-and-after sample before processing the full set, then receive links to the edited images.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Giving travel photos a consistent look
  • Unifying photos taken in mixed lighting
  • Applying a warm or cinematic style across a photo set
openai/plugins7.5천2026년 10월 8일 업데이트

Visualize a logo, sketch, or design on products such as mugs, shirts, and business cards. Review a pilot image, generate a visually consistent set, and collect the results on a shareable Firefly Board.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Preview a logo on mugs and shirts
  • Turn a hand-drawn sketch into product mockups
  • Create consistent brand presentation images
openai/plugins7.5천2026년 10월 8일 업데이트

Prepare images and videos for social platforms using Adobe tools. Create image crops that keep the subject in view, expand backgrounds when available, preview a full-set sample, and resize videos while preserving their aspect ratio.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Creating image variants for multiple social platforms
  • Cropping photos for Instagram Stories
  • Preparing LinkedIn images and YouTube thumbnails
openai/plugins7.5천2026년 10월 8일 업데이트

Create flyers, social posts, business cards, and other visuals from Adobe Express templates. Choose a template, customize its text and background color, and receive a design link, with additional editing and export options depending on the available tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Creating an event flyer
  • Designing an Instagram post
  • Customizing business card text and colors
openai/plugins7.5천2026년 10월 8일 업데이트

Turn an uploaded video into three highlight edits with Adobe Quick Cut. Choose a target length and style, then compare versions that feature different moments and download the ones you prefer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Creating a short highlight video for social media
  • Condensing interview footage into a highlight reel
  • Comparing three edits with different moment selections
openai/plugins7.5천2026년 10월 8일 업데이트

openai의 스킬 모두 보기

이 스킬의 문제 신고