Implements a new bioinformatics tool wrapper in proto-tools using a parallelized agent pipeline. Orchestrates phases: Research, Contract (core tool file), Fan-out (5 parallel subagents), Verify, then the decisive gates — Config Field Audit, Temp Integration & Stress, Docs Verification, Self-Audit & Full PR Review — and Ship. Use when creating tools from GitHub issues, wrapping models, or implementing new tool wrappers end-to-end.
التثبيت
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Implements a new bioinformatics tool wrapper in proto-tools using a parallelized agent pipeline. Orchestrates phases: Research, Contract (core tool file), Fan-out (5 parallel subagents), Verify, then the decisive gates — Config Field Audit, Temp Integration & Stress, Docs Verification, Self-Audit & Full PR Review — and Ship. Use when creating tools from GitHub issues, wrapping models, or implementing new tool wrappers end-to-end.
You are implementing a new bioinformatics tool in the proto-tools codebase. This skill orchestrates the full lifecycle using parallel subagents for speed.
The Phase 4.x gates are decisive and non-negotiable — they catch the failure modes that the fast fan-out reliably misses (invented config parameters, untested real workloads, hallucinated citations/licenses, missing license.yaml/links.yaml). Do not skip them to ship faster. Ground every decision in upstream source or a credible reference — never assume.
Key principle: The core tool file (Input/Config/Output + @tool() + run_*()) is the contract everything else depends on. Write it first (sequential), then fan out to subagents (parallel).
CRITICAL: Standalone scripts run in isolated environments and MUST NOT import from proto_tools:
from proto_tools.utils import ... — will fail at runtime
from proto_tools.entities import ... — will fail at runtime
from standalone_helpers import get_subprocess_device_env — auto-copied by worker bootstrap (OK)
Standard library imports: import json, import subprocess, etc. (OK)
Dependencies from requirements.txt: import torch, import numpy, etc. (OK)
NEVER install proto_tools in standalone environments (creates circular dependency, breaks isolation)
Phase 0: Parse Input
The user provides EITHER:
A GitHub issue URL/number (e.g., #<issue-number> or https://github.com/evo-design/proto-tools/issues/<issue-number>)
License — read the repo's actual LICENSE/COPYING file (not just the README badge) and the weights license if it differs. Record the SPDX id (or note it's non-standard) and the file URL. Feeds license.yaml and the README License callout.
Canonical links — the repo URL, project homepage/docs, paper DOI or preprint URL, HuggingFace model page, affiliated orgs. Feeds links.yaml. Verify each resolves.
The full upstream parameter surface — which parameters the upstream CLI/Python API actually exposes to end users, with their defaults and valid ranges. This is the ground truth for Phase 4.5's config audit; capture it now while reading the source.
Model weights location (HuggingFace, GitHub releases, etc.) — determines which weight management pattern to use:
HuggingFace from_pretrained → no weight code needed (HF_HOME set automatically). Example: tools/masked_models/esm2/
Direct download in setup.sh → must implement PROTO_MODEL_CACHE pattern. Example: tools/inverse_folding/fampnn/standalone/setup.sh
Foundry install → must implement PROTO_MODEL_CACHE pattern. Example: tools/inverse_folding/ligandmpnn/standalone/setup.sh
Cannot be auto-downloaded (the asset is too large to auto-download, or there's no programmatic fetch path at all — e.g. license requires a manual approval flow before the user receives a one-time download link). Distinct from gated-but-fetchable assets like HF gated repos, where setup.sh can still download once a token is present (use proto_check_gated_hf_repo for those). For not-fetchable assets, the user must place files on disk themselves; setup.sh doesn't fetch. → use proto_resolve_asset_availability as a fail-fast precheck at the top of setup.sh. Tests skip cleanly on hosts without weights; misconfigured PROTO_<TOOLKIT>_WEIGHTS_DIR still fails. Example: tools/structure_prediction/alphafold3/standalone/setup.sh. See PATTERNS.md → "standalone/setup.sh for tools whose assets are NOT automatically downloaded".
Read key source files — Find and read the research repo's inference/prediction scripts to understand:
How the model is loaded
What inputs it expects (sequences, structures, files)
What outputs it produces (scores, sequences, structures)
Device handling (CUDA/CPU)
Read existing tools in the same category — Find the reference implementation:
ls proto_tools/tools/{category}/
Read the reference tool's:
Main tool file (the @tool() decorated function)
standalone/inference.py (or run.py)
standalone/setup.sh
standalone/requirements.txt
standalone/python_version.txt (required — the Python version pin for the env)
__init__.py (at tool and category level)
README.md
cite.bib
Test file in tests/{category}_tests/
Check for shared data models — If the category has a shared_data_models.py, read it. The new tool should extend these base classes rather than creating new ones.
Output of Phase 1: Mental model of:
The tool's Python API (function signatures, class names)
Its dependencies and installation requirements
How it maps to the existing tool patterns
Which reference tool to use as the structural template
Phase 2: Contract (Write Core Tool File)
Goal: Write the core tool file that defines the API surface. Everything else depends on this.
This phase is sequential — no subagents. The orchestrator writes this directly.
Steps:
Placeholder glossary (used throughout this skill — all distinct concepts, not interchangeable):
{toolkit} — snake_case directory name shared by all tools in the family (e.g., evo2, pyrosetta). Strict — drives the directory path and the dispatch/worker identifier.
{tool_key} — kebab-case registration key, {toolkit}-{suffix} (e.g., evo2-sample, pyrosetta-energy). Strict — the value passed to @tool(key=...).
{tool_key_snake} — snake_case form of {tool_key} (e.g., evo2_sample). Strict — the core tool file name, the run_* function, and the test file.
{ToolName} — PascalCase class-name prefix for this tool's Input / Config / Output (e.g., Evo2Sample, ESMFoldPrediction). Developer's choice — typically the PascalCase of {tool_key}, but pick whatever reads cleanly (e.g., ESMFold over Esmfold) as long as it's specific to this tool.
{tool_display_name} — human-readable label (e.g., "Evo 2"). Used in docstrings, commit messages, and PR titles.
Write the core tool file proto_tools/tools/{category}/{toolkit}/{tool_key_snake}.py with:
Proper imports
Input class extending BaseToolInput (or shared base) with Field() — extra="forbid"
Config class extending BaseConfig (or shared base) with ConfigField() — extra="forbid". Use reload_on_change=True on fields that require worker restart (model checkpoint, etc.). Use include_in_key=False on fields that don't affect computation results (device, verbose, timeout are already excluded on BaseConfig; tool-level overrides of device must also set include_in_key=False). include_in_key defaults to True. Use xor_group="<slug>" to mark mutually exclusive sibling fields (see "Mutual-exclusion fields (XOR groups)" below).
Output class extending BaseToolOutput (or shared base) with Field() — extra="ignore" (set by BaseToolOutput; the base also emits a warning for unexpected fields so computed-field JSON round-trips can validate cleanly)
@tool() decorator with the 7 required kwargs (key, label, category, input_class, config_class, output_class, description) plus conventionally set uses_gpu. Optional: example_input, device_count, cacheable, stochastic, iterable_input_fields (a list — ["sequences"] for an ordinary iterable, or an index-parallel group like ["complexes", "msas"]), iterable_output_field, metrics_class, gpu_only, post_process_iterable
run_*() function that calls ToolInstance.dispatch()
If category has shared data models, use type aliases (e.g., ToolInput = InverseFoldingInput)
Metrics: if the tool emits scalar metric-like values (plDDT, perplexity, scores, etc.), route them through a Metrics subclass (from proto_tools/utils/tool_io.py). Prefer a shared per-category class (MaskedModelScoringMetrics, InverseFoldingScoringMetrics, CausalModelScoringMetrics, etc.) when the metric set matches siblings. Otherwise declare a per-tool <Tool>Metrics(Metrics) subclass in the tool file with a metric_spec: ClassVar[dict[str, MetricSpec]] documenting type/range and a better_values_are direction ("higher", "lower", "in-range", or "context-dependent") for each metric — test_metric_spec_consistency.py requires a valid better_values_are on every metric. Verification lives in each tool's e2e test via assert_metrics_in_spec(result) (helper at tests/tool_infra_tests/_metric_helpers.py).
Output must implement output_format_options, output_format_default, _export_output()
Iterable input/output cardinality must be 1:1: when iterable_input_fields and iterable_output_field are both set, len(output.{iterable_output_field}) must equal the (primary) input list length, by position. The framework's per-item cache stitching, dedup, and diversification testing all rely on this. If the tool produces N samples per input (e.g. a num_sequences_per_structure or num_designs config parameter), bundle the N samples inside a single per-input wrapper class — do NOT flatten N×inputs into the output list. Canonical patterns to follow: ProteinMPNNSequences / LigandMPNNSequences (per-structure sequence bundles, num_sequences_per_structure config) and RFdiffusion3Designs (per-spec design bundles, n_batches * diffusion_batch_size per spec). See PATTERNS.md → "Iterable cardinality" for the full rule and code shape.
Input = per-item, Config = shared: model each field by "is there one of these per item, or one for the whole batch?" Shared whole-batch params/collections → Config (which also keeps the per-item cache key correct by construction). Per-item values → Input, either bundled inside the iterable element or as a parallel sibling in iterable_input_fields. A per-item value that is usually uniform can be a broadcastable field — InputField(..., broadcastable=True), declared as list[...] | None, added to iterable_input_fields, normalized via broadcast_parallel_fields() (one value broadcasts to all, N is strict 1:1). Broadcast is opt-in per field and only when a single value applied to every item is genuinely correct (safe for binder_chain; wrong for msas). See PATTERNS.md → "Input = per-item, Config = shared".
cpus_per_instance opt-in: BaseConfig.cpus_per_instance defaults to None — every CPU tool stays off ToolPool's CPU scheduler and runs as a single direct call. Most CPU tools should leave this alone. Only opt in (override to a positive int) when per-call work is heavy enough to amortize spinning up N persistent worker subprocesses — each holds its own venv in RAM and pays a startup tax, so cheap tools (short per-item compute, internal threading, network IO) lose more than they gain. The canonical opt-in is PyRosetta (heavy init, multi-second per pose, embarrassingly parallel poses → cpus_per_instance = 1). GPU tools (gpus_per_instance > 0) ignore cpus_per_instance entirely.
Inherited field audit: When reusing a shared base config (e.g., InverseFoldingConfig) or base input, enumerate every inherited field and verify the target model can implement it. For each unsupported field, either implement support (e.g., logit masking for excluded_amino_acids) or override the field with a validator that raises ValueError("'{field_name}' is not supported by {tool_display_name}") when a non-default value is provided. Do not silently inherit fields that the model ignores.
Cloud fail-fast for local-resource configs: If a config setting needs a local resource that can't be staged to the hosted service — a local database/index, an on-disk weights/artifact directory, or a local file path — override BaseConfig.cloud_unsupported_reason(self) -> str | None so device='cloud' fails fast at dispatch with a clear message instead of a late runtime error (a missing-file crash, or silently ignoring the override). Return a user-facing reason when the offending setting is active, None otherwise (the default means cloud-compatible). Guard on the setting actually being non-default so ordinary cloud runs are untouched:
defcloud_unsupported_reason(self) -> str | None:
"""A local weights directory (``local_path``) isn't present on a hosted worker."""ifself.local_path: # optional override; unset by default → cloud-OKreturn"local_path points to a local weights directory not available on device='cloud'. Unset it, or run locally with device='cpu'."returnNone
A tool whose only mode needs a local file (e.g. a required local DB/HMM input, like blast-search local mode or pyhmmer-hmmscan) returns the reason unconditionally. Scope this to intrinsically local configs — do not encode deployment-specific hosting knowledge here (e.g. which model checkpoints are pre-warmed on the hosted service); that stays in the private hosting layer. The router invokes this only on the device='cloud' path, after the local_cpu no-op and the license gate.
Mutual-exclusion fields (XOR groups)
For "pick one" siblings: make each Optional with default=None, tag with the same xor_group="<slug>", and add a @model_validator(mode="after") to enforce at runtime.
mmseqs_db: str | None = InputField(default=None, xor_group="target",
description="Target DB (path/slug/AssetRef).")
target_sequences: list[str] | None = InputField(default=None, xor_group="target",
description="Inline target protein sequences.")
@model_validator(mode="after")defexactly_one_target(self) -> "Mmseqs2SearchProteinsInput":
if (self.mmseqs_db isNone) == (self.target_sequences isNone):
raise ValueError("provide exactly one of `mmseqs_db` or `target_sequences`")
returnself
Code Style Conventions
These ensure consistent formatting across all generated tools:
Module docstrings — Use one-liner format: """Tool description.""". No multi-line module docstrings.
No unused imports — Only import what's actually used. Don't speculatively include Path, Optional, numpy, etc.
Type alias labels — When using type aliases for shared data models, label them with comments:
ToolInstance import at top level — Import ToolInstance at module level, not lazily inside the run function:
from proto_tools.utils.tool_instance import ToolInstance
logger.debug() before main loop — Add a status message before the processing loop:
logger.debug("Using local venv for {toolkit} {operation}")
Don't redefine inherited fields — Fields like verbose, timeout, and device are inherited from BaseConfig. Don't redeclare them in tool-specific Config classes unless overriding the default value.
__init__.py files — Sort __all__ alphabetically.
For the complete tool file template, refer to .claude/skills/implement-tool/TEMPLATES.md.
For implementation patterns (CPU/GPU/compile-from-source), refer to .claude/skills/implement-tool/PATTERNS.md.
Phase 3: Fan-Out (5 Parallel Subagents)
Goal: Generate all supporting files in parallel. Each subagent gets the contract (tool file) + one reference file + a focused prompt.
IMPORTANT: Launch all 5 Task() calls in a single message. If sent in separate messages, the first blocks and the rest wait.
Before launching subagents:
Read the contract tool file you just wrote (Phase 2)
Read the matching reference file for each subagent from the reference tool
Then launch all 5 subagents simultaneously:
Subagent 1: Standalone Environment
What it produces:standalone/inference.py (or run.py), standalone/setup.sh, standalone/requirements.txt, standalone/python_version.txt, optionally standalone/env_vars.txt
Prompt template:
You are implementing the standalone execution environment for a bioinformatics tool.
## Your Task
Create the standalone files for the {tool_display_name} tool in:
proto_tools/tools/{category}/{toolkit}/standalone/
## Contract (the tool file this standalone serves)
<paste full tool file content here>
## Reference Implementation
Here is the standalone from {reference_tool} to use as a structural template:
<paste reference inference.py content>
<paste reference setup.sh content>
<paste reference requirements.txt content>
## Research: Source Repo API
<paste key findings about how the research code works — model loading, inference API, etc.>
## Files to Create
### 1. inference.py (for GPU/AI tools) or run.py (for CPU tools)
Structure:
- Module docstring
- Imports (json, sys, os at top; heavy imports inside functions)
- **Module-level logger** (REQUIRED — see "Logger Convention" below):
```python
from standalone_helpers import get_logger
logger = get_logger(__name__)
Model class with lazy loading pattern:
__init__: set self._loaded = False
load(device): import heavy deps, load model weights
Core method(s) matching the operations from the contract
serialize_output() from standalone_helpers for tensor → list conversion
dispatch(input_dict) function as entry point:
Global model instance (lazy-initialized)
Routes by input_dict["operation"] to model methods
Returns JSON-serializable dict
if __name__ == "__main__": block reading/writing JSON files
Logger Convention (enforced by tests/style_consistency_tests/test_standalone_logger_consistency.py)
Every .py file under proto_tools/tools/*/*/standalone/ — inference.py, run.py, binary_config.py, model.py, helper modules, etc. — must declare a module-level logger:
from standalone_helpers import get_logger
logger = get_logger(__name__)
Why: standalones run inside isolated micromamba subprocesses where proto_tools is NOT importable. get_logger is shipped via standalone_helpers/proto_logging.py (auto-copied at worker startup). Records emitted through it are bridged back to the parent process for re-emission under proto_tools.worker.{toolkit}.*. Plain logging.getLogger(__name__) produces a logger outside the bridge namespace and its records are silently dropped.
__init__.py is the only exemption (no log calls there). The consistency test enforces this on every other .py file in the standalone tree.
For status updates that should drive the parent's spinner subtitle (and never clutter console output), use the dedicated method:
logger.update_status("Loading checkpoint") # spinner subtitle update; not shown in console
logger.info("...") # normal log line
If the tool has file-format conversion helpers (e.g., writing MSA to Parquet, converting complexes to YAML/FASTA), implement them as plain-type functions in helpers.py at the tool directory level. These functions must be self-contained (no proto_tools imports) and take only plain types (str, list, dict). The tool layer imports and wraps them with typed signatures. Keeping them dependency-free lets the hosted execution backend reuse the same files, preserving a single source of truth. See esmfold, chai1, boltz2 for examples.
CRITICAL RULES:
Every .py file in standalone/ (except __init__.py) MUST declare logger = get_logger(__name__) from standalone_helpers — see "Logger Convention" above. Plain logging.getLogger(__name__) is forbidden and enforced by a consistency test.
Heavy imports (torch, model libraries) ONLY inside methods, never at module level
The dispatch() function is the entry point for both persistent-worker and one-shot execution
All tensor/array outputs must be converted to Python lists via serialize_output() from standalone_helpers
Match the operation names used in the contract's ToolInstance.dispatch() calls
Device handling: accept device from input_dict, pass to model.load()
Seed handling: pass config.seed (raw Optional[int]) through the dispatch dict. In inference.py, call set_torch_seed(seed) unconditionally — the helper's None-check gates the expensive cuDNN flags. For any downstream sampler that needs a concrete int, do sampling_seed = seed if seed is not None else get_random_int() using the helper from standalone_helpers.
stochastic=True: set when outputs depend on config.seed. Cacheable unseeded calls skip cache; iterable dispatches skip dedup so duplicate items diverge via per-item RNG advancement. The framework does not unroll multi-item dispatches — per-item seed handling is the tool's responsibility. Do not set for tools that accept but ignore the seed.
Audit hardcoded values in the reference/research code (chain IDs, model paths, default parameters, assumed dimensions). If a value could vary across valid inputs, parameterize it — accept it from the dispatch input dict rather than hardcoding. For example, chain_id="A" should come from the input, not be a constant.
Required Device Management Protocol Functions
Sync note: These patterns are duplicated in PATTERNS.md (templates + reference section) because subagents can't see PATTERNS.md. If the protocol changes, update both files.
All standalone scripts MUST implement two module-level protocol functions for DeviceManager integration. Add these AFTER the dispatch() function (or after operation functions for CPU tools), BEFORE if __name__.
1. to_device(device: str) -> dict
Enables DeviceManager to move models between GPUs and CPU.
Import standalone_helpers (auto-copied by worker bootstrap) — do NOT import from proto_tools
2. setup.sh
All setup.sh scripts source standalone_helpers.sh (auto-copied alongside standalone_helpers.py) for shared infrastructure functions. Reference: tools/inverse_folding/fampnn/standalone/setup.sh.
#!/bin/bashset -euo pipefail
source standalone_helpers.sh
pip install uv
# PyTorch tools (add extras like torchvision if needed):
proto_install_pytorch
# proto_install_pytorch "" torchvision # torch + version-matched torchvision# JAX tools (with optional tool-specific override prefix):# proto_install_jax MYTOOL# If CUDA toolkit needed (for JIT compilation):# proto_install_cuda_toolkit
uv pip install -r requirements.txt
# Non-HF tools that download weights:
proto_resolve_weights_dir {toolkit}
if [ ! -f "$WEIGHTS_DIR/model.pt" ]; then
wget -q -O "$WEIGHTS_DIR/model.pt""https://example.com/model.pt"fi# Gated HF models (validates access before pip install):# proto_check_gated_hf_repo "org/model-name" "https://huggingface.co/org/model-name"
Available functions (see utils/standalone_helpers_source/standalone_helpers.sh):
proto_install_pytorch [spec] [extras...] — install PyTorch via RECOMMENDED_TORCH_SPEC, with optional version-matched extras (e.g., torchvision)
proto_install_jax [TOOL_PREFIX] — install JAX with tool-specific overrides
proto_install_cuda_toolkit [constraint] [extras...] — micromamba CUDA toolkit
proto_resolve_weights_dir <toolkit> — set $WEIGHTS_DIR via PROTO_MODEL_CACHE
proto_resolve_asset_availability <toolkit> <pattern> [license_url] [asset_kind] — fail-fast precheck for gated / manually-provisioned weights / databases. Sentinel exit (64) on default-cache-empty signals a test skip; misconfigured PROTO_<TOOLKIT>_WEIGHTS_DIR is an exit-1 failure. Pair with the MissingAssetError plumbing in tool_instance.py.
List all Python dependencies with version pins. Do NOT include torch/jax — those are installed by setup.sh using hardware-aware detection.
4. python_version.txt
Required. Every tool must pin its Python version — ToolInstance raises FileNotFoundError at setup time for a tool without this file, and the style consistency tests fail if any tool is missing it. Always use the keyed format with a required default key. Comments (# to end of line) and blank lines are allowed.
default: 3.12
Per-platform overrides (rarely needed): use only when an upstream dependency is unavailable for the default Python on a specific platform. Three-tier lookup, most specific wins:
{system}-{machine} (e.g. linux-aarch64)
{system} (e.g. linux)
default (required catch-all)
default: 3.11
linux-aarch64: 3.10 # only when forced by dep availability — see PyRosetta
Pick default to match the reference tool unless the upstream library has explicit Python-version constraints. The full spec lives in notes/tool-environments.md. Validation: every python_version.txt is checked by tests/style_consistency_tests/test_python_version_consistency.py, which enforces the format and the required default key on every shipped file.
5. env_vars.txt (only if needed)
[passthrough]
HF_TOKEN
[set]
Only if the tool needs LD_LIBRARY_PATH or other env vars
**10C: Run all tests for the new tool**
Run all tests (functional + infra) filtered to the new tool:
```bash
pytest --all --ext -k "{tool_key}" -v
This runs the tool's functional tests AND the parametrized infra tests (example_input, device consistency, registry integration). A detailed log file is generated in logs/ (project root).
What it produces:README.md, cite.bib, license.yaml, links.yaml
license.yaml and links.yaml are required for every toolkit — tests/style_consistency_tests/test_license_consistency.py enforces license.yaml's existence and schema, and the registry/docs surface both files. A toolkit missing either fails CI. Every value in all four files must be grounded in a credible source (the upstream repo, its LICENSE/COPYING, the paper, the SPDX registry) — never guess a license, DOI, or URL. The deeper triple-check happens in Phase 4.7; this subagent's job is to get them right the first time.
Prompt template:
You are writing the documentation and metadata for a bioinformatics tool.
## Your Task
Create README.md, cite.bib, license.yaml, and links.yaml for the {tool_display_name}
tool in:
proto_tools/tools/{category}/{toolkit}/
## Contract (the tool's API)
<paste full tool file content here>
## Reference files
Here are the same files from {reference_tool} to use as structural templates:
<paste reference README.md, cite.bib, license.yaml, links.yaml content>
## Research Context
<paste tool description, paper info, biological context, AND the verified upstream
license + canonical links from Phase 1>
## README.md Structure
Follow the structured template in TEMPLATES.md exactly. Four canonical H2 sections in order:
1. `# {Toolkit Display Name}` (plus the badge row above the title and a `> [!NOTE] License: ...` callout)
2. `## Overview` — 2–3 sentences: who built it, what it does, why useful (link the upstream repo on first mention)
3. `## Background` — 1–3 paragraphs of biological/algorithmic context with inline paper citations; optional `### Learning Resources` subsection for user-facing explainers (blogs, talks, courses)
4. `## Tools` — one `### {Tool Display Name} (\`{tool-key}\`)` block per registered tool, each with `#### Applications` and `#### Usage Tips` (critical-parameter tips in bold)
5. `## Toolkit Notes` — Toolkit-wide guide badges row + bulleted notes that apply to every tool in the toolkit
DO NOT add other H2 sections — schemas, configs, and output specs are auto-generated from Pydantic field descriptions. Read `gene_annotation/pyhmmer/README.md` for the canonical example before drafting. The `> [!NOTE] License: ...` callout MUST agree with `license.yaml`.
## cite.bib
Look up the paper's BibTeX citation using the DOI (verify the DOI resolves — do not fabricate). Format:
@article{firstauthor_year_toolname,
title={...},
author={...},
journal={...},
year={...},
doi={...}
}
## license.yaml
Capture the upstream license verified from the repo's LICENSE/COPYING (and any
weights/data license, which can differ from the code license). Schema and the
SPDX allowlist are in TEMPLATES.md → "license.yaml Template". SPDX-allowlisted
licenses must NOT inline text (it lives in proto_tools/tools/_licenses/{spdx}.txt);
non-SPDX terms use a "Custom (...)" spdx string with inline `text:`.
## links.yaml
Canonical upstream links, verified to resolve. Common keys: `github`, `website`,
`paper` / `preprint` (DOI or arXiv URL), `huggingface`, `organizations` (list).
See TEMPLATES.md → "links.yaml Template".
Subagent 3: Example Notebook
What it produces:examples/example.ipynb
Prompt template:
You are creating an example Jupyter notebook for a bioinformatics tool.
## Your Task
Create examples/example.ipynb for the {tool_display_name} tool in:
proto_tools/tools/{category}/{toolkit}/examples/
## Contract (the tool's API)
<paste full tool file content here>
## Instructions
Create a Jupyter notebook (.ipynb JSON) with these cells:
1. **Markdown title cell** — Tool name, brief description, paper link
2. **Code: Imports** — Exact imports from proto_tools.tools.{category}.{toolkit}
3. **Code: Input API Reference** — Call `display_api_reference("{tool_key}", "input", "run_{tool_key_snake}")` to auto-render the table. Never hand-write the table.
4. **Code: Config API Reference** — Same pattern with `"config"`.
5. **Code: Output API Reference** — Same pattern with `"output"`.
6. **Code: Basic Usage** — Minimal working example with realistic biological data
7. **Code: Advanced Usage** — Example with non-default config parameters
8. **Code: Export** — Demonstrate result.export()
Notebook metadata:
- kernelspec must be exactly `{"name": "python3", "display_name": "proto-tools"}`. Custom names like `proto-language` raise `NoSuchKernel` outside the original author's machine.
- `language_info.name` is `"python"`.
- Use realistic biological data (real protein sequences, not placeholder text)
- Include comments explaining each step
- Show output inspection (printing key fields)
Write the notebook as valid .ipynb JSON (the raw JSON format with cells array, metadata, etc.).
Note: the notebook will be executed during Phase 4 via `scripts/run_example_notebooks.py`, which strips widget/Plotly/Bokeh mime types that don't render in static viewers.
Subagent 4: Tests
What it produces:tests/{category}_tests/test_{tool_key_snake}.py
Prompt template:
You are writing tests for a bioinformatics tool.
## Your Task
Create tests/{category}_tests/test_{tool_key_snake}.py
## Contract (the tool's API)
<paste full tool file content here>
## Reference Tests
Here are the tests from {reference_tool} as a structural template:
<paste reference test file content>
## Test Structure
```python
"""Tests for {tool_display_name} tool."""
from pathlib import Path
import pytest
from proto_tools.entities.structures.structure import Structure # if needed
from proto_tools.tools import run_{tool_key_snake}, {ToolName}Input, {ToolName}Config
TEST_PDB_FILE = Path(__file__).parent.parent / "dummy_data" / "renin_af3.pdb" # if needed
# ── Validation ────────────────────────────────────────────────────────────────
def test_{tool_key_snake}_input_normalizes_single_item():
"""Test that single inputs are normalized to lists (custom validator)."""
# ── Integration ───────────────────────────────────────────────────────────────
@pytest.mark.uses_gpu # or @pytest.mark.integration for CPU tools
def test_{tool_key_snake}_basic_execution():
"""Test basic tool execution with default config."""
# Create input with realistic data
# Run tool
# Assert result.success
# Assert result.tool_id == "{tool_key}"
# Assert specific output fields are populated
@pytest.mark.uses_gpu
def test_{tool_key_snake}_export(tmp_path):
"""Test output export to supported formats."""
CRITICAL RULES:
Flat functions only — no class Test*, use descriptive function names
No trivial tests — do NOT test Pydantic built-in behavior. Tests that verify required fields exist, extra="forbid" rejects unknown fields, or config stores default values are worthless — Pydantic already guarantees these. Only test custom validators, computed properties, normalization logic, and real tool behavior.
Do NOT write tests like: test_*_rejects_missing_*, test_*_rejects_extra_fields, test_*_config_defaults, test_*_rejects_invalid_* (for simple ge=/le= constraints). These just re-test Pydantic.
Import from proto_tools.tools (top-level), not from deep paths
Use @pytest.mark.uses_gpu for any test that runs the actual tool on GPU
Use @pytest.mark.integration for CPU dispatch tests
Use realistic biological data (real sequences/structures from dummy_data/)
Check result.success and result.tool_id
Test both default config and non-default parameters
(Plain pytest runs everything the host can handle. Add --cpu-only to force-skip GPU tests, or --gpu-only to filter to GPU-marked tests.)
Validate tool registration — Check the tool appears in the registry:
python3 -c "from proto_tools.tools.tool_registry import ToolRegistry; specs = [s for s in ToolRegistry.list_all() if '{tool_key}' in s.key]; print([(s.key, s.label) for s in specs])"
Execute the example notebook — Always route notebook execution through the sanitizer script, never jupyter nbconvert --execute directly. The script strips widget / Plotly / Bokeh mime types that a kernel emits but static viewers (VS Code, JupyterLab, GitHub, nbviewer) can't render, leaving only the text/plain / text/html fallbacks that survive the round-trip:
@tool(): stochastic=True is set when outputs depend on config.seed; the tool advances its own RNG per item (framework does not unroll)
@tool(): metrics_class=<Tool>Metrics is set when the tool emits scalar metric-like values
No try/except wrapping tool logic
Google-style docstrings with Attributes (for classes) and Args/Returns/Examples (for functions)
__init__.py exports at all 4 levels
README.md exists with correct import paths
cite.bib exists with valid BibTeX
Example notebook exists
Tests written and importable
Standalone script implements to_device(device: str) -> dict at module level
Standalone script implements get_memory_stats() -> dict at module level
Biological coordinates are 1-indexed, inclusive (if applicable)
Weight management: If the tool downloads model weights:
HF tools: no weight code needed (HF_HOME set automatically)
Non-HF tools: setup.sh implements PROTO_MODEL_CACHE pattern (see fampnn/standalone/setup.sh)
Non-HF tools: inference.py calls resolve_weights_dir() from standalone_helpers (see fampnn/standalone/inference.py)
Phase 4.5: Config Field Audit
Goal: Guarantee every Input/Config field is real, correctly typed, correctly defaulted, and earns its place. The fast fan-out tends to invent parameters the upstream tool doesn't have, copy defaults/ranges blindly, and leave types loose (str where a Literal belongs).
When: After Phase 4 passes. Before Phase 4.6 — pruning/retyping a field changes the surface the temp tests exercise.
How: Walk every field, one at a time — do NOT batch-approve. Check each against the upstream parameter surface captured in Phase 1 (re-read the upstream source if Phase 1 didn't fully capture it). Make no assumptions; ground each decision in the source.
Per-field checklist:
Real upstream parameter? Is this parameter actually exposed to end users upstream? If it's an internal implementation detail (a managed path, a fixed buffer, a constant the upstream API never surfaces), delete it — do not manufacture complexity the tool doesn't have. Keep only what a user would meaningfully set.
Default matches upstream. The default must equal the upstream default, or be a deliberate deviation noted in the description. Never invent a default.
Range/constraints match upstream.ge/le/gt/lt bounds must reflect the real valid range from the source, not a guess.
Type is as tight as the domain allows. Prefer Literal[...] over str, and list[Literal[...]] over list[str], whenever upstream accepts a fixed enumerated set. Use bounded int/float over unbounded numerics. Use X | None only when None is genuinely meaningful.
Description is concise + dual-audience. One line, accurate, useful to both a human and an agent: what the field does and (when non-obvious) the effect of changing it. No restating the field name; no prose paragraphs.
Flags set deliberately (decide each, don't default-copy):
reload_on_change=True only for fields that require a worker restart (model checkpoint / anything that reloads the model).
include_in_key=False for fields that don't affect results (device, verbose, timeout are already excluded on BaseConfig; a tool-level device override must re-set it).
xor_group="<slug>" for mutually exclusive siblings, paired with the @model_validator.
Inherited fields — confirm the Phase 2 inherited-field audit holds: every field from a shared base is implemented or rejected with ValueError, never silently ignored.
Local-resource field → cloud hook. If the field points at a local file/database/index or an on-disk weights/artifact path, confirm the config overrides cloud_unsupported_reason() so device='cloud' fails fast (see "Cloud fail-fast for local-resource configs" in Phase 2). Skip for AssetRef-stageable uploads and Structure-typed inputs (the gateway stages those).
Output: edit the contract directly. If you remove/rename/retype a field, propagate to standalone dispatch(), README, notebook, tests, and the export chain — leave no dangling references. Re-run Phase 4 import/test checks. Do this deliberately yourself, or delegate to a focused subagent given the Phase 1 upstream param surface + the contract.
Phase 4.6: Temp Integration & Stress Tests
Goal: Prove the tool behaves correctly under a real user's workload — realistic data, batch/large inputs, edge sizes — beyond the committed unit tests. These are throwaway: write, run, read, delete. The committed suite stays as Subagent 4 wrote it.
When: After the config surface is final (4.5), on a host that can actually run the tool (GPU if uses_gpu=True).
How:
Direct-call driver script — write a temp script (e.g. scratch/drive_{toolkit}.py) that imports run_{tool_key_snake} and calls it exactly as a user would: realistic biological inputs, default config, then a couple of non-default configs exercising the parameters you just audited. Print key output fields and result.success. Run it; confirm the output is scientifically sensible, not just non-erroring.
Temp integration + stress tests emulating real usage — run them:
A realistic end-to-end call on representative data.
Batch / large-input behavior (many sequences, a long sequence, multiple structures) to surface batching or memory bugs.
Boundary inputs (minimal, maximal-within-range) and the validation / xor_group paths.
Concise and meaningful only — every test asserts something real about behavior. No slop, no bloat, no re-testing Pydantic. Delete any test that doesn't earn its place.
Trace the logic against upstream source — follow the dispatch path end-to-end and confirm it matches what the upstream code does (argument order, units, 1-indexed coordinates, output shape). Don't assume — read the source. Strip overly-defensive code (the @tool decorator owns errors; no try/except in tool logic) and keep comments/docstrings brief.
Clean up — delete the temp script and temp tests. Fold any genuinely valuable case into the committed suite (Subagent 4's file); do not leave scratch artifacts in the PR.
Goal: Triple-check every external claim in cite.bib, links.yaml, license.yaml, and README.md against credible, verifiable sources. Hallucinated DOIs, wrong licenses, dead links, and invented capabilities are common fan-out failures and are user-facing.
How — verify, never trust:
cite.bib — resolve the DOI; it must point to the actual paper (confirm title/authors/year/venue). No DOI → use the arXiv/bioRxiv id. Don't fabricate volume/pages.
license.yaml — open the upstream repo's real LICENSE/COPYING and confirm the SPDX id, commercial_use, redistribution, and attribution_required. Confirm separate weights/data licenses where they differ. Confirm _licenses/{spdx}.txt exists for any SPDX id, and that the README > [!NOTE] License: callout agrees.
links.yaml — every URL resolves (github, homepage, paper, huggingface). No placeholders, no links copied from the reference tool.
README.md — Overview/Background claims are accurate (who built it, what it does, the science); inline paper citations resolve; no invented benchmarks or capabilities.
Use WebFetch / WebSearch to verify and ground each fact in a source. Fix discrepancies in place, then re-run test_license_consistency.py / test_readme_consistency.py if touched.
Phase 4.8: Self-Audit & Full PR Review
Goal: Catch convention violations, dead fields, hardcoded assumptions, and consistency gaps — then do a final file-by-file review of the entire PR diff against the quality established in the rest of the repo. Last gate before Ship.
When: After Phases 4.5–4.7. Before Phase 5 (Ship).
Part A — Convention audit
Launch a single audit agent via Task():
You are auditing a newly implemented bioinformatics tool for quality issues.
## Your Task
Read all generated files for the {tool_display_name} tool and check for issues.
## Files to Read
- proto_tools/tools/{category}/{toolkit}/{tool_key_snake}.py (core tool file)
- proto_tools/tools/{category}/{toolkit}/standalone/inference.py (or run.py)
- proto_tools/tools/{category}/{toolkit}/standalone/setup.sh
- proto_tools/tools/{category}/{toolkit}/standalone/requirements.txt
- proto_tools/tools/{category}/{toolkit}/standalone/python_version.txt
- proto_tools/tools/{category}/{toolkit}/README.md
- proto_tools/tools/{category}/{toolkit}/examples/example.ipynb
- tests/{category}_tests/test_{tool_key_snake}.py
## Reference Tool (for comparison)
Also read the same files from {reference_tool} to compare patterns.
## Checks
1. **Module-level imports** — Heavy dependencies (torch, model libraries) must only be imported inside functions in standalone scripts, never at module level
2. **Inherited fields** — Every field inherited from base classes (shared_data_models.py) must be either implemented in the standalone code or raise ValueError when non-default values are provided
3. **Hardcoded values** — Flag any values in standalone code that are hardcoded but should vary with input (chain IDs, model paths, assumed dimensions, fixed sequence lengths)
4. **Contract-standalone consistency** — Every Config field with `reload_on_change=True` must be read and used in the standalone code. Every operation dispatched in the tool file must be handled in standalone dispatch()
5. **Notebook accuracy** — Import paths, class names, and field names in the example notebook must exactly match the contract
6. **Seed propagation** — If the tool uses PyTorch/JAX RNG, Config inherits `seed: int | None` from `BaseConfig`. Pass `config.seed` (raw `Optional[int]`) through the dispatch dict as `"seed": config.seed`. In `standalone/inference.py`, call `set_torch_seed(seed)` unconditionally — the helper no-ops when `seed is None`, so the expensive cuDNN determinism flags are only set on the explicit-seed path. If the tool's internal sampling code needs a concrete int (e.g., a model sampler that doesn't accept `None`), use `seed if seed is not None else get_random_int()` to fall back to a fresh random int from `standalone_helpers.get_random_int`
7. **Test coverage** — Every operation (sample, score, etc.) should have at least one test
## Output Format
Print a JSON array of issues:
[
{"severity": "fix", "file": "path", "line": "~N", "issue": "description"},
{"severity": "cosmetic", "file": "path", "line": "~N", "issue": "description"}
]
"fix" = must fix before shipping. "cosmetic" = nice to fix, won't cause bugs.
If no issues found, print: []
Part B — Full PR diff review (parallel, file-by-file)
Review the actual diff, not just the files in isolation, against the quality bar set by the rest of the repo.
Launch review subagents in parallel (single message), each owning a slice of the diff. Give each agent the file's diff, the equivalent file from {reference_tool}, and instructions to (a) flag any drift from established repo conventions, (b) re-check everything flagged earlier in this session, and (c) confirm the change reads like world-class code already in the repo. Prefer the repo's pr-review-toolkit agents where the diff warrants — code-reviewer (conventions/correctness), silent-failure-hunter (error handling), type-design-analyzer (the audited types), comment-analyzer (docstring/comment accuracy) — falling back to generic Task() review agents otherwise. Each returns the same JSON issue array as Part A.
After the audit and review agents return:
Read every agent's output and consolidate the issues
Fix all "fix" severity issues directly
Fix "cosmetic" issues if quick (< 1 minute each)
Re-run Phase 4 import/test checks if any fixes touched the tool file or standalone code
Log findings to auto-memory — If any "fix" issues were found, save them to persistent memory so we can track how the pipeline fails over time. Write (or append) to the auto-memory directory at implement-tool-audits.md:
## {toolkit} audit — {date}
- {N} fix issues, {M} cosmetic
- Fixes: {brief list of fix issues}
- These indicate systemic gaps in the pipeline.
This builds a dataset of pipeline failure modes that informs future skill improvements.
