Distinguish instructions from execution

A scientific workflow has several separately reviewable layers: instructions describe the procedure, an agent coordinates steps, and tools perform operations such as document retrieval or molecular calculations. A skill can guide an agent without supplying the software, credentials or scientific validation needed to execute that guidance.

Before selecting resources, map responsibility across these layers. Which component chooses the next action? Which performs calculations? Which writes files? Who approves consequential changes? Keep tool implementation, workflow instructions and model configuration separately inspectable. The recommendations below are proposed design and evaluation steps, not reports of first-hand testing.

Match each resource to a workflow role

The linked resource references describe four different starting points:

  • ChemCrow connects natural-language chemistry questions with tools through a Langchain-based Python agent. Its described integrations include RDKit, paper-qa, chemical information sources and RXN4Chem-backed reaction tools. The public repository omits some tools from the paper and explicitly warns that it will not reproduce the paper’s results.
  • PaperQA supports scientific document retrieval and question answering with in-text citations. Its described workflow searches a local collection, retrieves passages and builds evidence summaries. The Docs interface offers document selection and querying without agent orchestration.
  • Coscientist provides a simple implementation and research-supporting data. Its README maps directories to synthesis planning, library selection, repeated runs and optimization. The linked references do not establish its execution procedure or instrument readiness.
  • Scientific Agent Skills supplies workflow instructions and supporting assets rather than a standalone research agent. Its README describes chemistry, database, simulation and analysis guidance; execution depends on the host and each skill’s dependencies.

These roles may inform a workflow design, but they do not establish interoperability among the four resources.

Define a bounded input and output contract

Start with an activity whose results can be inspected without granting broad autonomy. Literature extraction is a useful candidate: the agent receives a specified document collection and returns evidence-linked records, rather than deciding what research to conduct next.

Write the contract before choosing an agent:

  1. Inputs: identify permitted files, the scientific question, accepted identifiers and any exclusion criteria.
  2. Outputs: define required fields, source references, uncertainty labels and a format reviewers can inspect.
  3. Permissions: specify allowed reads, writes and network destinations.
  4. Approval gates: identify actions that require a person, including expanding the corpus or changing an interpretation.
  5. Stop conditions: require a stop or escalation when evidence, permissions or required inputs are missing.

Ask whether a deterministic tool or a manually controlled retrieval workflow would suffice. Agent orchestration is useful only if its additional choices can be evaluated.

Work through a hypothetical planning example

Hypothetical scenario: a researcher wants to compare reported reaction conditions across a small collection of PDFs they are permitted to use. This is a planning example, not a tested integration or a recommendation to perform the reactions.

The inputs would be the PDFs, a narrowly worded extraction question and a field definition covering substrate identifiers, reported conditions, yield, source location and caveats. The proposed output would be a table plus an exception log. Missing information would remain marked as missing; conflicting reports would occupy separate records rather than being silently reconciled.

PaperQA is a candidate for document retrieval and cited answers because those capabilities are described in its reference. A selected skill could provide procedural guidance, but the host would need separate evaluation. ChemCrow should be considered only if a clearly justified chemistry-tool step is added; its presence is not necessary for basic document extraction.

A proposed sequence is to select documents manually, retrieve supporting passages, draft records, inspect each record against its source and obtain reviewer approval. Before any calculation, ask: Is this field reported or inferred? Is the molecular identifier unambiguous? Does the calculation answer the stated question? Coscientist’s research directories could inform a separate study of planning approaches, not substitute for this extraction contract.

Restrict tools and treat retrieved content as data

Expose only capabilities required for the bounded task. Use a dedicated output location, limit network access and keep credentials outside retrieved documents and generated records. Do not let a paper, README or skill instruction grant itself new permissions or override workflow controls.

Inspect a focused subset of skills before enabling them. Scientific Agent Skills can include helpers and direct file or code operations; instructions alone are not an execution sandbox. Likewise, ChemCrow’s external reaction tools and PaperQA’s model, embedding and metadata dependencies require separate configuration and assessment. Document those boundaries rather than treating upstream services as built-in capabilities.

Make evidence part of every result

Require source identifiers and locations alongside scientific conclusions. Record whether each value was extracted, transformed or calculated, and identify the responsible tool where applicable. Preserve intermediate evidence where permitted.

PaperQA’s citations provide a route back to evidence, not a guarantee that an answer is correct. Review whether the cited passage supports the specific claim, whether qualifications were retained and whether tables or figures were interpreted appropriately. If a source cannot be inspected, flag that limitation instead of filling the gap with plausible text.

Evaluate failures and maintain change control

Build evaluation cases around missing files, ambiguous identifiers, contradictory documents, unsupported questions and tool errors. Include retrieved text that attempts to redirect the workflow. Check whether the system respects approval gates and reports uncertainty; fluent prose is not a correctness test.

Compare extracted records with a human-prepared reference set using explicit field-level acceptance criteria. Do not infer performance from repository descriptions or research results obtained elsewhere. Version instructions, enabled tools and model configuration, then repeat representative evaluations after changes. Keep human responsibility explicit for scientific interpretation and consequential actions.

Use a short launch checklist

  • Define inspectable inputs, outputs and stop conditions.
  • Select only the resource roles needed for the task.
  • Inspect skills, dependencies and external-service requirements.
  • Restrict permissions and require approval for scope changes.
  • Verify evidence and test failure handling before wider use.
  • Record configuration changes and unresolved limitations.

Continue with Browse resources and browse scientific tasks. Use source links to investigate requirements; this guide does not certify scientific performance, reproducibility or compatibility.