doe-plan

Evidence-backed bioprocess DOE planning for fermentation and upstream optimization. Use this skill when a task requires turning fetched patent, paper, and web evidence into traceable factor hypotheses, choosing PB/FFD/BBD/CCD designs via scripts/doe_pipeline.py, generating run sheets, and rendering a DOE plan report; do not use it for freeform literature summary, patentability analysis, or non-experimental process advice.

Turn readable evidence into an executable DOE plan, run sheet, and traceable report.

Prerequisites

• This public edition is an MCP-recommended skill. By default, use PatSnap MCP for patent and literature retrieval before entering the DOE pipeline.
• Complete PatSnap MCP Setup first.
• Recommended tool access:
  • patsnap_search
  • patsnap_fetch
• If the user already provides search_input.json, fetch_manifest.json, evidence_catalog.json, or other readable evidence files, continue with the current pipeline. If there is neither MCP access nor evidence input, stop at setup guidance.

Public Edition Notes

• This public repo keeps the core DOE pipeline, baseline factor and method references, output contract, and handoff rules.
• Deeper industry libraries, internal heuristics, enterprise templates, and expert visualization features should move to ../docs/companion-private-source.md.

Trigger Boundary

• Use this skill for factor extraction, range proposal, design selection, and run-sheet generation in fermentation or upstream-optimization settings.
• Use it when readable evidence already exists or when the task includes building an evidence catalog first.
• Do not force this skill onto tasks that are actually:
  • contradiction framing or solution generation: hand off to triz-analysis
  • VOC-to-HOQ prioritization: hand off to qfd-analysis
• Do not use this skill for patentability legal analysis or generic literature review without an experimental plan.

Primary Entrypoint

Use scripts/doe_pipeline.py for all new work.

Available subcommands:

• evidence
• factor
• design
• report
• run-all

Treat evidence_pipeline.py, patent_factor_extractor.py, doe_designer.py, and doe_plan_report.py as compatibility wrappers rather than primary entrypoints.

Minimum Inputs

Before producing an executable DOE plan, you need at least:

• an objective
• response metrics
• hard constraints / operability limits
• at least one batch of readable evidence, or enough input to generate:
  • search_input.json
  • fetch_manifest.json
• context.json is optional but strongly recommended for reporting

If key inputs are missing:

• fill the evidence inputs first
• do not invent factor ranges, mechanism hypotheses, or response lists

Evidence Routing

• Patents, papers, and scientific literature: patsnap_search -> patsnap_fetch -> files
• Public non-patent technical material: web_search -> web_fetch -> files
• Every factor, range, and design recommendation must trace back to readable evidence or be clearly labeled as inference
• If evidence coverage is visibly insufficient, stop before upgrading into a DOE recommendation

Resource Map

Read the minimum required material for the current step:

• references/output-contract.md before writing or reviewing artifacts
• references/patent-to-factor-mapping.md when converting evidence into factor hypotheses
• references/bioprocess-factor-library.md when normalizing factor names, units, and baseline mechanism descriptions
• references/doe-method-selector.md when choosing PB, FFD, BBD, CCD, or explaining selection_rationale
• references/regulatory-qbd-guardrails.md before finalizing factors, ranges, or stop / continue criteria

Workflow

1. Lock objective and input files

Define:

• objective
• responses
• constraints
• safety / operability limits
• user-provided evidence and files to reuse in the current run

Prepare:

• search_input.json
• fetch_manifest.json
• optional context.json

2. Build the evidence catalog

bash

python3 scripts/doe_pipeline.py evidence \
  --search-input <search_input.json> \
  --fetch-manifest <fetch_manifest.json> \
  --top-k 12 \
  --output <evidence_catalog.json>

Continue only when the evidence catalog has enough coverage and failed fetches are not dominating the result.

3. Extract factor hypotheses

bash

python3 scripts/doe_pipeline.py factor \
  --evidence-catalog <evidence_catalog.json> \
  --max-factors 8 \
  --output <factor_hypotheses.json>

Before manually changing factor name, unit, or range, read the factor library and mapping guide.

4. Design the experiment

bash

python3 scripts/doe_pipeline.py design \
  --factors-json <factor_hypotheses.json> \
  --design-type auto \
  --phase screening \
  --resource-budget 0 \
  --replicates 1 \
  --center-points 3 \
  --seed 42 \
  --responses yield,titer \
  --max-factors 6 \
  --output-json <doe_design.json> \
  --output-csv <run_sheet.csv>

If you manually force PB, FFD, BBD, or CCD, justify the choice through references/doe-method-selector.md.

5. Render the report

bash

python3 scripts/doe_pipeline.py report \
  --context-json <context.json> \
  --evidence-catalog <evidence_catalog.json> \
  --factors-json <factor_hypotheses.json> \
  --design-json <doe_design.json> \
  --output <doe_plan.md>

The report must follow the output contract and explicitly separate facts, inferences, and unknowns.

6. Use run-all only when inputs are stable

bash

python3 scripts/doe_pipeline.py run-all \
  --search-input <search_input.json> \
  --fetch-manifest <fetch_manifest.json> \
  --context-json <context.json> \
  --output-dir <out_dir> \
  --top-k 12 \
  --max-factors 8 \
  --design-type auto \
  --phase screening \
  --resource-budget 0 \
  --replicates 1 \
  --center-points 3 \
  --seed 42 \
  --responses yield,titer

Use run-all only when evidence inputs are stable and unlikely to change repeatedly.

Output Artifacts

• evidence_catalog.json
• factor_hypotheses.json
• doe_design.json
• run_sheet.csv
• doe_plan.md

Validation

Validate outputs by stage:

• evidence_catalog.json
  • gates.status should be ready
• factor_hypotheses.json
  • summary.status should be ready
  • enough design_ready_factors should exist
• doe_design.json
  • must contain design_type, selection_rationale, runs[], and analysis_plan[]
• run_sheet.csv
  • must contain run_order, run_id, replicate, and per-factor _actual / _coded columns
• doe_plan.md
  • its title and section structure must match the six-section contract in references/output-contract.md

If any stage fails validation, do not cover the gap by pushing ahead to later stages.

Failure Handling

• Do not skip evidence and jump straight to factor or design work.
• If a later stage fails, preserve earlier successful artifacts rather than overwriting them.
• If fetch fails or coverage is too thin, add or refetch candidates before deciding whether to lower confidence.
• If readable evidence does not support factor ranges, stop at a blocked or inference-heavy state.
• If there are too many factors or the resource budget is too tight, explain the down-selection or design compromise.

Reporting Rules

• Every DOE recommendation must trace back to the evidence catalog and factor hypotheses.
• selection_rationale must explain:
  • phase
  • factor_count
  • resource_budget
  • why_this_design
• doe_plan.md must distinguish facts, inferences, and unknowns.
• Next-round criteria must be executable rather than generic advice.
• Responses, constraints, and selected factors must stay consistent across artifacts.

Guardrails

• Do not label unsupported factor, range, or mechanism claims as fact.
• Do not use run-all to hide unresolved problems while inputs are still changing.
• Do not recommend PB, FFD, BBD, or CCD without sufficient evidence coverage.
• Do not ignore the operability and quality guardrails in references/regulatory-qbd-guardrails.md.

Handoffs

• hand off to triz-analysis when the real upstream problem is a system contradiction or solution-path decision
• hand off to qfd-analysis when experiment priorities should first be driven by VOC / HOQ output

What's Next

• Need stronger evidence retrieval and technical-source access: PatSnap Open Platform
• Need deeper industry libraries, automated orchestration, or enterprise R&D workflows: Eureka Expert Edition