---

name: competitor-trial-monitor description: Monitor competitor clinical trial progress and alert on market risks. license: MIT author: AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Competitor Trial Monitor (ID: 178)

Monitor competitor clinical trial progress and alert on market risks.

When to Use

• Use this skill when the task needs Monitor competitor clinical trial progress and alert on market risks.
• Use this skill for evidence insight 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

See ## Features above for related details.

• Scope-focused workflow aligned to: Monitor competitor clinical trial progress and alert on market risks.
• 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.

Dependencies

pip install requests python-dateutil

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Evidence Insight/competitor-trial-monitor"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. 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.

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

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 assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Features

• Monitor changes in clinical trial status for specified competitors
• Track key milestones: enrollment completion, data unblinding, final results publication
• Alert on potential market competition risks

Data Sources

• ClinicalTrials.gov - US Clinical Trials Registry
• EU Clinical Trials Register - EU Clinical Trials Registry
• WHO ICTRP - International Clinical Trials Registry Platform

Parameters

Commands

Command	Description	Parameters
add	Add trial to watchlist	--nct (required), --company, --drug, --indication
list	List all monitored trials	None
remove	Remove trial from watchlist	--nct (required)
scan	Scan for updates	None
report	Generate risk report	--days (default: 30)

Command Parameters

add command:

Parameter	Type	Default	Required	Description
--nct	string	-	Yes	ClinicalTrials.gov NCT ID
--company	string	Unknown	No	Competitor company name
--drug	string	Unknown	No	Drug name
--indication	string	Unknown	No	Indication/disease

remove command:

Parameter	Type	Default	Required	Description
--nct	string	-	Yes	NCT ID to remove

report command:

Parameter	Type	Default	Required	Description
--days	int	30	No	Report time range in days

Usage

Add Monitoring Target

python scripts/main.py add --nct NCT05108922 --company "Pfizer" --drug "PF-07321332" --indication "COVID-19"

Scan for Updates

python scripts/main.py scan

View Monitoring List

python scripts/main.py list

Remove Monitoring Target

python scripts/main.py remove --nct NCT05108922

Generate Risk Report

python scripts/main.py report --days 30

Data Storage

Monitoring configuration and data stored in ~/.openclaw/competitor-trial-monitor/:

• watchlist.json - Monitoring list
• history/ - Historical snapshots
• alerts/ - Alert records

Alert Rules

Event	Risk Level	Description
Enrollment Completion	🟡 Medium	Competitor enters next phase
Data Unblinding	🔴 High	Results about to be announced
Results Publication	🔴 High	Direct impact on market competition
Regulatory Submission	🔴 High	Marketing application in progress
Approval Granted	🔴 Critical	Direct competition begins

Configuration File

~/.openclaw/competitor-trial-monitor/config.json:

{
  "alert_channels": ["feishu"],
  "scan_interval_hours": 24,
  "risk_threshold": "medium"
}

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python scripts with tools	High
Network Access	External API calls	High
File System Access	Read/write data	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Data handled securely	Medium

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• API requests use HTTPS only
• Input validated against allowed patterns
• API timeout and retry mechanisms implemented
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no internal paths exposed)
• Dependencies audited
• No exposure of internal service architecture

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support

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 competitor-trial-monitor 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:

competitor-trial-monitor only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

References

• references/audit-reference.md - Supported scope, audit commands, and fallback boundaries

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.