Agent-Skills.md

Agent Skills: Tamarind Bio

Access a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecular dynamics. Use when the user mentions Tamarind or tamarind.bio, wants to run any of these open-source tools in the cloud, references app.tamarind.bio/api or the x-api-key header, or needs to submit batches of sequences for structural or biophysical characterization.

UncategorizedID: K-Dense-AI/claude-scientific-skills/tamarind

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K-Dense-AI/claude-scientific-skills
K-Dense-AILicense: MIT
30,2803,052

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pnpm dlx add-skill https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/tamarind

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skills/tamarind/SKILL.md

Skill Metadata

Name
tamarind
Description
Tamarind Bio API key sent as the x-api-key header.

Tamarind Bio

Tamarind runs molecular-design and structural-biology tools on managed compute: structure prediction, protein and antibody design, docking, binding-affinity prediction, MSA generation, and molecular dynamics. Use it when the user requests Tamarind, its REST API/MCP server, or cloud execution of these scientific tools. For local sequence processing or molecular descriptors, use a local library.

Sources and review scope

The REST contracts and public catalog were reviewed on 2026-09-30. Examples are illustrative until validated against the user's account; this review did not run authenticated jobs or establish scientific accuracy for any model.

  • API index and complete guide.
  • Current OpenAPI includes discovery, validation, jobs, files, and newer pipeline/custom-tool surfaces. openapi.yaml is also available. Check that needed paths exist: the merged spec can return HTTP 200 with only its classic surface when the backend spec cannot be fetched.
  • Public catalog needs no key; query ?type= or ?tag=. It documents public tools and conditional required settings, not every optional parameter or account entitlement.
  • Product documentation index links to Markdown pages, including the MCP guide.

Fetch the account's current schemas before composing a run. Where the prose guide and OpenAPI differ, prefer the operation/schema for field shapes, and record any unresolved difference rather than guessing.

Access

  1. Use the user's Tamarind deployment. The shared base is https://app.tamarind.bio/api; a dedicated organization deployment has its own host and account data. Every relative REST path below is under /api.
  2. Obtain a key from the deployment's API settings and read it from TAMARIND_API_KEY; send it as x-api-key. Keep keys out of files and logs.
  3. Check the account's current allowance and compute budget before scaling up. Free usage is a monthly allowance, not an unconditional promise of ten jobs forever; billing and entitlements can change.
# Public discovery requires no credential.
curl --fail-with-body 'https://app.tamarind.bio/tools.json?type=alphafold'
# Account-scoped discovery:
curl --fail-with-body 'https://app.tamarind.bio/api/tools' \
  -H "x-api-key: $TAMARIND_API_KEY"

For REST examples install requests in the execution environment. The official CLI distribution is tamarind-cli, and its Custom Tools Python client imports as from tamarind import Tamarind; the unrelated package named tamarind is not this client. See the SDK reference. Core job recipes below use HTTP directly.

Workflow

  1. Discover. Read GET /tools and match the user's scientific task to the tool description. Built-ins return an array; ?custom=true lists legacy custom tools only. Current custom deployments can be missing from this list: use the known deployed name and its schema before concluding that it is unavailable.
  2. Read the schema. GET /tools/{name}/schema returns a JSON Schema for the settings object. GET /tools also supplies a trimmed settings parameter list. Check task-dependent fields, file extensions, list values, and defaults.
  3. Validate. Send POST /validate-job with type, settings, and optional jobName. Check HTTP status first, then JSON valid. On success, inspect and use normalized as the settings to submit. Address unrecognized_settings if returned, even alongside valid: true: an optional-field typo can otherwise silently leave the default in effect. Validation checks fields, not all submit policies, queue limits, or deployment readiness.
  4. Submit once. POST /submit-job takes jobName, type, settings, optional version for a custom-tool build, and optional/organization-required projectTag. Persist the submitted name and settings. A successful response is plain text, not a JSON receipt. Use the returned stored name.
  5. Poll. GET /jobs?jobName=... returns a row directly. Single-job terminal states are Complete, Stopped, and Failed; handle legacy Deleted or an exact-lookup error without looping indefinitely. Poll batch parents using batchStatus, and poll newer pipelines on their own run endpoint.
  6. Download and inspect. POST /result returns a JSON string URL on 200, or 202 with status: "preparing". Retry result retrieval after 202, without resubmitting compute. GET the signed URL without the Tamarind API-key header. Download the archive only for successful runs; request fileName: "output.log" for stopped/failed jobs. Verify the scientific outputs after downloading.

Workflow recipes implement validation, stored names, bounded polling, 202 handling, batch validation, and pagination. They are locally smoke-tested with simulated responses; authenticated execution remains untested.

Picking tools and interpreting results

Select by inputs, intended output, and modeling assumptions, then confirm the candidate in the live catalog. These are anchors, not a guaranteed catalog:

| Task | Candidates and decisions | |---|---| | Protein/complex structure | alphafold for AF2 monomers/multimers; boltz, chai, or protenix for cofolding including ligands/nucleic acids; esmfold for fast single-sequence protein folding. Check esmfold2 separately: its current catalog includes protein, DNA, RNA, and ligand complexes. | | Binder/motif design | bindcraft, boltzgen, rfdiffusion; choose by target type, scaffold constraints, and required structure inputs. | | Inverse folding | proteinmpnn/ligandmpnn consume structures and design sequences. Re-fold designs and compare to the intended backbone/interface. | | Small-molecule docking | autodock-vina for a fixed receptor and search box; diffdock for diffusion docking; boltz/chai for cofolding. Choose the modeling approach for the task, not to avoid supplying a required input. | | Antibody/developability/MSA/MD | Filter descriptions and schemas for the specific task; availability and inputs differ by tool. |

Confidence scores describe model confidence, not experimental binding, specificity, or affinity. Compare designed backbones, interfaces, clashes, chain/residue mapping, and developability. Check ligand chemistry and stereochemistry; docking scores are not interchangeable with measured binding free energies. Record tool/model, input provenance, chain mapping, seeds/samples, MSA/template choices, normalized settings, and any user-selected filtering thresholds.

Honor the user's selected tool and budget. Use authorized defaults for routine choices; surface unresolved choices that materially affect the scientific task or compute scope before a large campaign. Never silently substitute a different scientific task because its inputs are easier to supply.

File inputs and chaining

  • Upload a structure with PUT /upload/{filename} (binary body; follow the documented redirect), then reference the registered relative name, e.g. target.pdb or inputs/target.pdb when ?folder=inputs was used.
  • Confirm names using GET /files; it returns a non-paginated array for the selected folder, not a list of a job's outputs.
  • Prefer these paths over inline file content. The current guide says redundant account-email prefixes are stripped; there is no longer a universal double-prefix failure. Arbitrary strings are not necessarily file references.
  • Reuse a completed job's file as JobName/path/to/file.ext, matching the next parameter's supported extensions and list/scalar shape. Do not guess filenames.
  • ProteinMPNN designs must feed a folding tool's sequence field. A structural template field does not mean "fold this designed sequence". Read generated FASTA/CSV sequences and validate one folding settings object per sequence.
  • Do not author internal fields such as submit_method, msa, or monomer_msa.

Batches and pipelines

POST /submit-batch accepts one type, a nonempty settings array, batchName, and optional parallel jobNames. Validate every row using array-mode /validate-job (up to 1,000 rows per call), and use each row's normalized settings. Submission allows up to 30,000 expanded jobs, counting design fan-out, and has a separate approximately 4.5 MB request limit. Split on both constraints. Do not assume old weightedHoursBudget, maxRuntimeSeconds, or gpuType request fields enforce a cap: they are absent from the current batch schema. Use confirmed account controls and an agreed job/sample count.

Poll GET /jobs?jobName=<batchName> until batchStatus is Complete, Stopped, or AggregationFailed. Subjobs can finish before aggregation. Fetch the archive through /result and handle 202; resultUrl is optional and is not a reliable readiness signal. Page GET /jobs?batch=... using startKey to inspect children.

For new saved workflows use the template/run API under /pipelines: read the current pipeline graph contract, validate the proposed run with POST /pipelines/validate, submit with POST /pipelines/submit (required name, bindings, and one of templateId/pipeline), and poll GET /pipelines/runs/{run_id}. Its statuses are lowercase and separate from job statuses. The legacy /submit-pipeline and /run-pipeline remain documented; API reference gives their actual required fields.

MCP alternative

Connect to https://mcp.tamarind.bio/mcp with OAuth 2.1 or the x-api-key header. The official guide confirms submitJob, submitBatch, getJobs, getResult, uploadFile, and getFiles. Read the connected server's tools/list schemas before using signatures or interpreting result envelopes.

If the connection advertises discovery/validation helpers such as getAvailableTools, getJobSchema, or validateJob, use their current schemas. Extra helpers, filter vocabularies, submitBatch(fromJob=...), and upload-through- MCP variants are not guaranteed by the public guide. This review's anonymous tools/list request returned 401, so their current contracts were not verified. Use the documented REST equivalents when needed.

Recovery

HTTP auth failures differ by route: classic endpoints can answer 400, jobs can answer 401 or gateway 403, and usage can answer 401. A 403 is not proof of a budget error. Check status and the actual response body before changing settings. Submission errors may be JSON or plain text regardless of Content-Type.

A timeout/5xx on submit does not prove that nothing queued. Look up the persisted name before retrying; for campaigns use POST /jobs/search with up to 1,000 names per request. Respect rate limits and avoid one-request-per-job polling at scale. A 413 rejects the oversized request before creating jobs; split the body or upload file content separately. DELETE /delete-job is a soft delete: it hides the job and leaves stored result files intact.

Reference files

  • API reference: endpoint shapes, validation, pagination, authentication differences, and legacy/new pipeline boundaries.
  • Tool catalog: schema interpretation and discovery.
  • Examples: current catalog-backed settings examples and tool-specific caveats, explicitly bounded by verification scope.
  • Workflows: executable HTTP recipes with local mocked verification; no authenticated scientific jobs were run for this review.