1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported as.
Use this skill when the task needs 1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported as.
Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.
Key Features
Scope-focused workflow aligned to: 1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported as.
Packaged executable path(s): scripts/main.py.
Reference material available in references/ for task-specific guidance.
Structured execution path designed to keep outputs consistent and reviewable.
Verwandte Skills
Dependencies
Python: 3.10+. Repository baseline for current packaged skills.
Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.
Example Usage
cd "20260318/scientific-skills/Academic Writing/adverse-event-narrative"
python -m py_compile scripts/main.py
python scripts/main.py --help
Example run plan:
Confirm the user input, output path, and any required config values.
Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
Run python scripts/main.py with the validated inputs.
Review the generated output and return the final artifact with any assumptions called out.
Implementation Details
See ## Workflow above for related details.
Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
Primary implementation surface: scripts/main.py.
Reference guidance: references/ contains supporting rules, prompts, or checklists.
Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.
Quick Check
Use this command to verify that the packaged script entry point can be parsed before deeper execution.
python -m py_compile scripts/main.py
Audit-Ready Commands
Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.
Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.
Overview
Regulatory-grade narrative generation tool that transforms adverse event case data into CIOMS-compliant ICSR narratives suitable for submission to FDA, EMA, and other health authorities.
Key Capabilities:
CIOMS I Compliance: Standardized narrative structure per international guidelines
ICH E2B Integration: Electronic submission format compatibility
Temporal Analysis: Timeline reconstruction and causality assessment
Medical Accuracy: Clinical terminology and MedDRA coding
Multi-Case Processing: Batch narrative generation for periodic reporting
Quality Validation: Automated checks for completeness and consistency
Core Capabilities
1. CIOMS I Narrative Structure
Generate standardized sections per CIOMS guidelines:
Reconstruct timeline and assess temporal plausibility:
# Analyze temporal relationships
timeline = generator.analyze_timeline(
drug_start="2024-01-15",
drug_stop="2024-02-01",
ae_onset="2024-01-28",
dechallenge_date="2024-02-01",
rechallenge_date=None
)
# Output shows temporal assessment
# "AE onset 13 days after drug initiation, positive dechallenge within 24h"
Assessments Generated:
Time to onset (latency period)
Dechallenge response (positive/negative/unknown)
Rechallenge response (if applicable)
Temporal plausibility (consistent with known drug profile)
3. Causality Evaluation Support
Structure causality assessment per WHO-UMC criteria:
# Generate causality section
causality = generator.assess_causality(
case_data=case,
criteria="who_umc", # or "naranjo", "cochrane"
include_rationale=True
)
# Output structured assessment with points for each criterion
WHO-UMC Categories:
Certain - Event reproduced on rechallenge
Probable/Likely - Reasonable time, positive dechallenge, alternative causes unlikely
Possible - Compatible time, but alternative causes possible
Unlikely - Incompatible time or alternative cause probable
Conditional/Unclassified - Insufficient information
Unassessable/Unclassifiable - Data contradictory or incomplete
4. Multi-Format Output
Generate narratives for different regulatory contexts:
# FDA MedWatch Form 3500A
fda_narrative = generator.generate(
case_data=case,
format="fda_medwatch",
max_length=2000 # Character limit
)
# EMA E2B(R3) electronic format
ema_narrative = generator.generate(
case_data=case,
format="ich_e2b",
version="R3"
)
# CIOMS I paper format
cioms_narrative = generator.generate(
case_data=case,
format="cioms_i"
)
Quality Checklist
Pre-Generation:
Case ID unique and formatted per SOP
Patient age/sex complete
Suspect drug(s) clearly identified
Adverse event(s) coded with MedDRA PT
Dates consistent (no future dates)
Reporter information included
Narrative Content:
All CIOMS I sections present
Temporal sequence clear and logical
Dechallenge/rechallenge described (if applicable)
Lab values with reference ranges
Concomitant medications listed
Medical history relevant to event
Outcome clearly stated
Causality assessment justified
Post-Generation:
MedDRA terms accurate and current
No contradictory information
Language objective and factual
No speculation or opinion (except causality section)
Patient identifiers removed or de-identified
CRITICAL: Medical review completed
CRITICAL: Causality assessment by qualified physician
Common Pitfalls
Completeness Issues:
❌ Missing dechallenge information → Cannot assess causality
✅ Always document effect after drug discontinuation
❌ Vague temporal information → "Recently started" vs. specific dates
✅ Use exact dates when available
❌ Incomplete concomitant medication list → Alternative causes missed
✅ Include all medications within relevant timeframe
Medical Accuracy Issues:
❌ Incorrect MedDRA coding → Wrong medical concept
✅ Use current MedDRA version; verify with medical reviewer
❌ Confusing correlation with causation → Temporal = causal
✅ Clearly state "temporally associated" vs. "causally related"
❌ Omitting alternative diagnoses → Biased toward drug causation
✅ Include all differential diagnoses considered
Regulatory Issues:
❌ Opinion in narrative body → "Clearly caused by drug"
✅ Reserve opinion for causality section; narrative should be factual
❌ Patient identifiers → HIPAA/privacy violation
✅ De-identify per regulatory requirements
❌ Abbreviations not defined → Assumes reader knowledge
✅ Spell out on first use in each narrative
References
Available in references/ directory:
cioms_i_guidelines.pdf - CIOMS I international reporting standards
ich_e2b_specifications.md - ICH E2B(R3) electronic format details
meddra_coding_guide.md - MedDRA terminology and coding principles
who_umc_causality.md - WHO causality assessment criteria
fda_medwatch_guide.md - FDA Form 3500A instructions
gvp_module_vi.md - EU Good Pharmacovigilance Practices
narrative_templates.md - Example narratives by case type
Medical Review Required: Generates draft only; requires physician review before submission
Causality Assessment: Structures reporter's assessment; does not perform independent causality evaluation
MedDRA Version: Uses installed MedDRA version; may not have latest terms
Language: Optimized for English; other languages may need translation
Literature Integration: Does not automatically search literature for similar cases
Signal Detection: Individual case narratives only; aggregate analysis requires other tools
Legal Proceedings: Not suitable for litigation support or expert witness reports
⚠️ CRITICAL: This tool generates draft narratives for efficiency. All adverse event narratives require review by qualified drug safety physicians before regulatory submission. Causality assessment must be performed by healthcare professionals with access to complete medical records.
Output Requirements
Every final response should make these items explicit when they are relevant:
Objective or requested deliverable
Inputs used and assumptions introduced
Workflow or decision path
Core result, recommendation, or artifact
Constraints, risks, caveats, or validation needs
Unresolved items and next-step checks
Error Handling
If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
Do not fabricate files, citations, data, search results, or execution outcomes.
Input Validation
This skill accepts requests that match the documented purpose of adverse-event-narrative and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
adverse-event-narrative only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
Response Template
Use the following fixed structure for non-trivial requests:
Objective
Inputs Received
Assumptions
Workflow
Deliverable
Risks and Limits
Next Checks
If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.