Menü
Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.
Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.
Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.
Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.
Plan publishable constraint-based metabolic modelling studies when the user has a broad biological or metabolic-engineering topic but no concrete dataset, organism, model, or hypothesis. Selects feasible BiGG/COBRA models, objectives, perturbations, analyses, metrics, figures, and risk controls before FBA code is generated.
Orchestrate the full metabolic flux analysis pipeline from model loading to phenotype prediction and publication figures. Triggers when the user provides an organism name, BIGG model ID, or custom reaction list and wants end-to-end metabolic modelling run automatically.
| name | researchclaw |
| description | Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory. |
Run ResearchClaw's 23-stage autonomous research pipeline. Given a research topic, this skill orchestrates the entire research workflow: literature review → hypothesis generation → experiment design → code generation & execution → result analysis → paper writing → peer review → final export.
Activate this skill when the user:
ls config.yaml || ls config.researchclaw.example.yaml
config.yaml, create one from the example:
cp config.researchclaw.example.yaml config.yaml
config.yaml under llm.api_key or via llm.api_key_env environment variable.Option A: CLI (recommended)
researchclaw run --topic "Your research topic here" --auto-approve
Options:
--topic / -t: Override the research topic from config--config / -c: Config file path (default: config.yaml)--output / -o: Output directory (default: artifacts/rc-YYYYMMDD-HHMMSS-HASH/)--from-stage: Resume from a specific stage (e.g., PAPER_OUTLINE)--auto-approve: Auto-approve gate stages (5, 9, 20) without human inputOption B: Python API
from researchclaw.pipeline.runner import execute_pipeline
from researchclaw.config import RCConfig
from researchclaw.adapters import AdapterBundle
from pathlib import Path
config = RCConfig.load("config.yaml", check_paths=False)
results = execute_pipeline(
run_dir=Path("artifacts/my-run"),
run_id="research-001",
config=config,
adapters=AdapterBundle(),
auto_approve_gates=True,
)
# Check results
for r in results:
print(f"Stage {r.stage.name}: {r.status.value}")
Option C: Iterative Pipeline (multi-round improvement)
from researchclaw.pipeline.runner import execute_iterative_pipeline
results = execute_iterative_pipeline(
run_dir=Path("artifacts/my-run"),
run_id="research-001",
config=config,
adapters=AdapterBundle(),
max_iterations=3,
convergence_rounds=2,
)
After a successful run, the output directory contains:
artifacts/<run-id>/
├── stage-1/ # TOPIC_INIT outputs
├── stage-2/ # PROBLEM_DECOMPOSE outputs
├── ...
├── stage-10/
│ └── experiment.py # Generated experiment code
├── stage-12/
│ └── runs/run-1.json # Experiment execution results
├── stage-14/
│ ├── experiment_summary.json # Aggregated metrics
│ └── results_table.tex # LaTeX results table
├── stage-17/
│ └── paper_draft.md # Full paper draft
├── stage-22/
│ └── charts/ # Generated visualizations
│ ├── metric_trajectory.png
│ └── experiment_comparison.png
└── pipeline_summary.json # Overall pipeline status
| Mode | Description | Config |
|---|---|---|
simulated | LLM generates synthetic results (no code execution) | experiment.mode: simulated |
sandbox | Execute generated code locally via subprocess | experiment.mode: sandbox |
ssh_remote | Execute on remote GPU server via SSH | experiment.mode: ssh_remote |
researchclaw validate --config config.yamlllm.base_url and API keyexperiment.sandbox.python_path exists and has numpy installed--auto-approve or manually approve at stages 5, 9, 20