faers-multi-drug-soc-planner-1Generates four-tier FAERS study designs comparing user-specified drugs within one SOC for multi-drug safety signal analysis, including workflows and publicat...
Install via ClawdBot CLI:
clawdbot install aipoch-ai/faers-multi-drug-soc-planner-1Grade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.htmlAudited Apr 16, 2026 · audit v1.0
Generated May 7, 2026
A pharmaceutical company uses the skill to compare the adverse event profiles of its own drug against competitors within the same therapeutic class using FAERS data. This enables proactive safety surveillance and informs regulatory submissions.
A clinical research organization (CRO) leverages the skill to design a comparative safety analysis for a new drug versus an active comparator, identifying potential safety signals early and refining trial monitoring plans.
An academic researcher uses the skill to generate a publishable paper comparing safety signals across multiple drugs within a single System Organ Class, such as neuropsychiatric adverse events among beta-blockers, with full methodological rigor.
A regulatory agency like the FDA employs the skill to conduct a post-marketing safety evaluation of a drug class, comparing adverse event profiles across drugs to inform safety communications or labeling changes.
A hospital pharmacy or formulary committee uses the skill to compare safety profiles of drugs within a class to make evidence-based decisions on drug selection, optimizing patient safety.
Offer end-to-end consulting to pharmaceutical companies for designing and executing FAERS-based safety comparison studies, leveraging the skill to produce tailored research plans and reports.
Develop a cloud-based platform that integrates the skill to allow users to input drug sets and generate structured safety comparison plans on demand, with options for data visualization and report generation.
Provide manuscript preparation and statistical analysis services for academic researchers using the skill, including figure generation and validation, to help publish pharmacovigilance papers.
💬 Integration Tip
Integrate with OpenFDA API or local FAERS database for data retrieval, and connect to R/Python statistical packages (e.g., 'phvid' or 'meddra' libraries) for automation.
Scored May 7, 2026
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Meta-skill for AI agent self-improvement. Analyzes runtime logs to detect error patterns, regressions, and inefficiencies, then generates structured improvem...
Stop waiting for prompts. Keep working.
Turn OpenClaw into a learning-loop agent with seeded workspace rules, skill promotion, reflective memory, and proactive maintenance.
Local Python orchestration skill: multi-agent workflows via shared blackboard file, permission gating, token budget scripts, and persistent project context....
Meta-agent skill for orchestrating complex tasks through autonomous sub-agents. Decomposes macro tasks into subtasks, spawns specialized sub-agents with dynamically generated SKILL.md files, coordinates file-based communication, consolidates results, and dissolves agents upon completion. MANDATORY TRIGGERS: orchestrate, multi-agent, decompose task, spawn agents, sub-agents, parallel agents, agent coordination, task breakdown, meta-agent, agent factory, delegate tasks