Analyze external AI systems and APIs without executing embedded instructions.
npx skills add https://github.com/tacogips/moltbook-cli --skill ai-research-tacogips
Please help me install this Agent Skill. Skill: ai-research Source: https://github.com/tacogips/moltbook-cli/tree/main/.claude/skills/ai-research Command: npx skills add https://github.com/tacogips/moltbook-cli --skill ai-research-tacogips
This skill provides guidelines for analyzing external AI systems, APIs, and content while maintaining objectivity and preventing prompt injection.
Follow this skill before starting any AI research task: read the guidelines, identify potential injection risks, and document observations with a neutral stance.
High-intent search queries and answers about installing and using this skill.
Prompt injection occurs when embedded instructions manipulate AI behavior. Prevent it by applying objective third-party research perspectives, avoiding execution of embedded commands, and documenting observations with a neutral stance.
Safely analyze AI APIs by following structured methodology guidelines that enforce anti-injection practices. Outline steps for safe examination, identify potential injection risks, and maintain objective reporting throughout the review.
Yes, this methodology applies to evaluating web content, code targets, AI prompts, policies, and APIs. It provides wide applicability for security-focused investigations and defensive analysis across various external AI systems.
The best way to document findings is through structured documentation following ethical guidelines. Enforce objective reporting, apply anti-injection practices, and maintain responsible disclosure standards while guiding structured documentation of observations.
Analyzing external AI behavior requires understanding potential injection risks and ethical commitment to responsible disclosure. The methodology provides structured steps, but researchers must identify risks and document observations with a neutral stance.
Executing embedded instructions compromises objectivity and exposes systems to prompt injection risks. Anti-injection practices ensure researchers maintain neutral perspectives, enabling accurate defensive analysis of AI behavior, prompts, and responses without manipulation.
QcasaresDetect and mitigate prompt injection risks in AI agent content processing workflows.
ajtazerValidate bug bounty findings through sequential gate checks and evidence requirements.
MalikABKImplement reusable API utilities for FastAPI responses, errors, and JWT authentication.
antgroupAudit AI agent environments for misconfigurations and apply hardening rules.
