Orchestrate parallel scientist agent workflows to produce cross-validated research reports.
npx skills add https://github.com/marcmunoz-uno/jailbreak --skill sciomc-marcmunoz-uno
Please help me install this Agent Skill. Skill: sciomc Source: https://github.com/marcmunoz-uno/jailbreak/tree/main/skills/sciomc Command: npx skills add https://github.com/marcmunoz-uno/jailbreak --skill sciomc-marcmunoz-uno
It helps turn complex research goals into a complete, evidence-backed report by running multiple scientist agents in parallel and then cross-validating their findings.
Use case: Analyze authentication patterns in a codebase by enumerating relevant files, examining token handling and session management, identifying vulnerability patterns, and synthesizing a verified report with evidence blocks and optional figures.
Run sciomc in AUTO mode by asking it to perform comprehensive research on your target topic until it outputs a research completion promise.
High-intent search queries and answers about installing and using this skill.
Parallel research agent workflows are automated by orchestrating multiple scientist agents concurrently to investigate a goal, cross-validate findings, and synthesize a comprehensive evidence-backed report. AUTO mode executes the full workflow autonomously with loop control and persisted session state.
Cross-validation in research orchestration detects contradictions or coverage gaps by comparing findings from parallel scientist agents. Results are aggregated into a final report with evidence- and tag-structured outputs after the verification loop completes.
Comprehensive security research is run by decomposing the goal into 3-7 independent stages with scoped focus, dispatching them concurrently across scientist models, and cross-validating results to identify vulnerability patterns and synthesize a verified report.
Explicit model routing per stage tier is required for parallel scientist execution. Stages are dispatched concurrently across haiku, sonnet, and opus scientist models to match task complexity with LOW, MEDIUM, or HIGH tiering.
AUTO mode can be used for autonomous hypothesis testing and pattern analysis. It performs the full research workflow autonomously with loop control, persisted session state, and explicit promise tags indicating completion or blocking conditions.
Bounded parallelism limits concurrent stage execution to prevent resource overload, requiring a verification loop to cross-validate results before aggregation. Sessions persist state with explicit completion or block promise tags to manage workflow boundaries.
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