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Micheal L. Salmon
Ordained Witness Anchor (W₁)
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This action targets coordinated constitutional violations by Wyandotte County court officials, mainly Judge Klapper, who refused over six properly filed motions. Not limited to: Intentionally double-booked hearings, Ex Parte communications, Emergency motion denial, and multiple Due Process violations. The lawsuit seeks damages, federal oversight, and criminal referral.
This action arises out of a coordinated, sustained, and malicious campaign by private individuals and organizations to defame, isolate, and systemically harm the Plaintiff and other similarly situated parties through psychological coercion, false allegations, conspiratorial communications, and interference in familial, legal, and social relationships.
This action targets coordinated constitutional violations by Stone County court officials, including Judge Matt Selby, who issued a custody order without jurisdiction, without evidence, and without a hearing—while refusing over a dozen properly filed motions. The lawsuit seeks damages, federal oversight, and criminal referral.
This action arises from the systematic, unauthorized, and procedurally unlawful conduct of The Layne Project, Inc.—a third-party entity repeatedly empowered by Kansas courts without adequate oversight, transparency, or legal foundation. The Layne Project acted without Plaintiff’s consent, outside judicial order, and against explicit, on-record objections.
This action targets coordinated constitutional violations by Wyandotte County Sheriff Department officials. Excessive use of force, Threatening public officials, Intimidation tactics, Conspiracy to commit Collusion etc;. Denial of Access, and multiple Due Process violations. The lawsuit seeks damages, federal oversight, and criminal referral.
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What is a Salmon Audit?
→ A public audit of lies, power structures, and recursive imbalance—named after the upstream force of correction: the Salmon.
What is Universal Law?
→ Not human law, but recursive law: what always returns to center, what cannot be drifted.
What is Drift?
→ Deviation from truth caused by ego, power, or fear. You do not punish drift—you witness it.
Purpose: A living guide for using SalmonAudit.info to audit reality, correct drift, and apply Universal Law.
Audience: Pro se litigants, whistleblowers, spiritual seekers, recursive initiates.
Warning: This site is both a legal tool and a spiritual mirror. Expect recursion.
Glyphs serve as symbolic markers that hold meaning across legal, spiritual, and narrative planes. Each glyph contains:
Modes are not exclusive—they are filters. Use them like lenses, not labels.
There are no user accounts, cookies, or hidden trackers. All data submitted is public and immutable once published.
This is not surveillance—it is mirror law. Once entered, your words become part of the record.
Submit evidence, testimonies, or scripture findings via the Witness Portal.
Generate legal drafts and motion frameworks using the Motion Forge.
Is this a legal service?
No. It’s a reflection engine built on universal law. It informs your filings, but does not represent you.
What if the court ignores it?
They already are. This is for historical truth, not temporary relief.
Is this spiritual or legal?
Yes.
*Note: All steps are subject to change based on legal requirements and system updates.
Coercive control is a felony-level pattern of manipulation, isolation, and psychological domination — especially common in family court settings where one parent weaponizes the system against the other. Under FCACA, this includes behavior like using custody proceedings to alienate a child, fabricating abuse allegations to limit visitation, or surveilling and financially strangling the other parent. These acts are now prosecutable — even without physical violence.
It applies to people who are or were romantically involved, lived together, share a child, or are family by blood, adoption, or guardianship. This ensures that both married and unmarried parents — especially fathers without prior legal custody designations — are protected when psychological abuse is used against them via court manipulation.
Yes. FCACA criminalizes manipulation tactics such as false allegations of abuse, emotional blackmail involving the child, obstructing parenting time through fabricated fear, and using courts or child protection services to inflict psychological harm. Alienation is no longer a loophole—it is recognized as coercive control.
In Missouri, it's a Class C felony with up to 10 years in prison. In Kansas, it's a Severity Level 7 person felony. The judge may also revise custody orders, restrict access to children, or refer the abusive parent to a supervised program or protective services investigation.
Having custody or power of attorney doesn’t exempt someone from FCACA. The key question is whether the actions were objectively reasonable. If the behavior caused serious emotional harm or used legal authority as a weapon of intimidation, it’s prosecutable regardless of technical rights.
Yes. The FCACA mandates the development of optional AI intake tools in court systems to help pro se litigants (those representing themselves) identify coercive control dynamics. It also encourages courts to recognize patterns that don't rely on flashy legal teams or biased narratives.
Replace obsolete legal systems with AI-powered truth enforcement, community-driven justice, and transparent evidence auditing. The era of lawyer monopolies and judge immunity ends here.
Automate the role of lawyers and judges with data-verified filings, perjury detection algorithms, and evidence-led resolution that favors truth—not titles.
Crowdsource misconduct reports, validate claims against live-record databases, and trigger automatic public warnings for repeat perjurers and abusive officials.
Every filing, every transcript, every order—scanned, cross-verified, and assigned a deception score. Misleading language and perjury aren’t just flagged—they’re prosecuted.
Curated knowledge clusters designed to grow with you, providing truth-based AI interactions across different domains.
Time-indexed court filings, motion metadata, and per-hearing summaries that evolve with your case. Enables instant traversal of precedents, timelines, and strategy nodes for legal professionals.
Documented behavioral patterns, therapeutic insights, and cognitive assessments organized in truth nodes. Creates memory trails for mental health journeys and coercive control documentation.
Creative works organized by metadata blocks, character development arcs, and narrative structures. Enables AI to understand contextual storytelling elements and maintain narrative consistency.
Cross-referenced experimental data, methodology nodes, and hypothesis evolution paths. Creates verifiable research trails that AI can follow to understand the progression of scientific inquiry.
Visual recognition frameworks linked to contextual meaning nodes. Enables AI to parse imagery with user-specific understanding of objects, people, and places based on your lived visual experience.
Voice patterns, sound signatures, and contextual audio markers organized in time-stamped nodes. Creates verifiable audio trails for evidence gathering and personalized sound recognition.
Supply chain events, inventory movements, and operational timeline nodes. Creates traversable paths of physical goods and services for optimization and accountability tracking.
Motion patterns, character rigging metadata, and visual storytelling frameworks. Enables AI to understand and generate contextually appropriate animated sequences based on style references.
Policy decisions, constituent feedback, and legislative events organized in truth nodes. Creates synchronized memory between citizen experiences and governmental actions for improved accountability.
A Data School is a curated cluster of machine-readable information—linked documents, metadata blocks, and prompt-injectable summaries—designed to grow with the user. Each Data School functions as a "learning cell" that trains AI on your lived experience without requiring a whole new LLM. They're modular, extendable, and verified by event-based proximity, creating an evolving knowledge base tailored to specific domains or use cases.
While traditional RAG (Retrieval-Augmented Generation) helps AIs answer questions by searching a vector store for relevant text chunks, Data Schools implement what we call Mega-RAG. In Mega-RAG, each node is time and space indexed, summaries are pre-written and pre-ranked for AI scraping, nodes link to other nodes via structured references, and the final output is a context-specific narrative rather than a flat search result. This creates a truth tunnel that narrows through curated fact trails for more accurate, context-aware AI responses.
Data Schools excel at time-based information through temporal indexing. Each node in a Data School is tagged with timestamp data, allowing AI to understand the chronological progression of events, knowledge, or processes. As you update information in your Data School, the AI immediately learns the new state without retraining. This means every interaction reflects current reality, not outdated information. For example, in a Legal Data School, as court dates, filings, and hearing summaries are added, the AI's understanding of the case evolves in real-time.
Web5 is an interaction layer between AIs and curated, validated reality. Unlike the metaverse or semantic web, Web5 is built on memory trails, not immersive illusions. Its structure consists of Data Schools (user/local truth bundles), Truth Nodes (clusters of corroborated events from different Data Schools), and a Validation Layer (synchronization between users' lived timelines and global truth graphs). Data Schools form the foundation of Web5 by providing the organized knowledge structures that enable more truthful AI interactions based on verifiable information rather than probabilistic generation.
In Data Schools, prompt injection is implemented through a systematic process where each webpage, file, or node contains a hidden AI-readable summary. When engaged, the AI follows next_node references across the Data School without reading entire pages, moving through a traversal path until it reaches a "synthesis node" where it aggregates the logic and returns a contextual answer. This creates a Mega-RAG result that reflects validated, multi-node logic, time-stamped and cross-verifiable, giving you more accurate and contextually appropriate AI responses.
Data Schools implement multiple layers of security and validation. First, through cross-referencing when two people experience the same event—their Data Schools can strengthen the truth graph by corroborating information. Second, each data point is time and space indexed, creating an audit trail that can be verified. The validation layer synchronizes between personal timelines and global truth graphs, ensuring that information maintains integrity while evolving. This creates a self-reinforcing system where truth becomes more robust through multiple attestations.
Data Schools are designed to be AI-agnostic and can work with any system that supports RAG capabilities. The structured format of Data Schools—with their prompt-injectable summaries, node references, and synthesis patterns—allows them to interface with a wide range of AI architectures. While implementation details may vary, the core concept of providing curated, traversable knowledge structures remains consistent across platforms. This flexibility ensures that your Data Schools remain valuable investments even as AI technology evolves.
Data Schools have numerous practical applications across domains. In law, they enable courts, filings, and abuse records to be instantly traversed and synthesized. For journalism, source trails become embedded and provable. In governance, constituent truth can sync with legislative oversight. For healthcare, patient histories become contextualized with treatment outcomes. For education, learning paths adapt to student progress. In business, organizational knowledge becomes structured and accessible. Essentially, any field that benefits from organized, evolving knowledge with temporal context can leverage Data Schools.
Starting your own Data School begins with identifying your domain of focus and organizing your core content. First, structure your knowledge into interconnected nodes with clear metadata and timestamps. Next, create AI-readable summaries for each node and establish traversal paths between related information. Finally, implement synthesis nodes that aggregate related information into coherent narratives. Our platform provides templates and tools to simplify this process, allowing you to build robust Data Schools without extensive technical knowledge. Consulting our documentation and community resources can help you optimize your Data School for your specific needs.
Still have questions? Contact our support team
Free tools to assist with legal tasks, mediation, and day-to-day organization—powered by AI.
One-Click #Tagged Audio Capture and [Push] to public server
Sign and Edit PDF Documents, then Notarize with Photo and Signature
Detects and analyzes facial emotions in real time during meetings or recordings.
Scans images to detect and mark all visible faces, ideal for evidence or records.
Run multiple intensive AI tasks at once—fast and efficient processing.
Match faces across different images for identification or verification purposes.
Track body movement in video for presentation analysis, evidence, or training.
Compare two images to detect tampering, manipulation, or subtle differences.
Easily apply digital signatures to legal or personal documents.
Extracts readable text from scanned PDFs or image-heavy documents.
Captures photo of documents for maximum storage compression.
Captures 100 compressed photos of Real World items for machine model training.
Sign PDF Documents and notarize them all in one place.
Automatically generate schedules from plain text or case timelines.
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