eatp-reference
Explains EATP concepts, trust establishment, and verification protocols.
Install
mkdir -p .claude/skills/eatp-reference && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/13259" && unzip -o skill.zip -d .claude/skills/eatp-reference && rm skill.zipInstalls to .claude/skills/eatp-reference
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Load EATP Framework technical reference. Use when explaining EATP concepts, trust lineage, attestation, verification gradient, trust postures, or comparing to other identity standards.Key capabilities
- →Explain EATP concepts
- →Describe trust lineage
- →Detail attestation and verification gradient
- →Outline trust postures
- →Compare EATP to other identity standards
How it works
This skill provides technical reference material for the Enterprise Agent Trust Protocol (EATP), explaining its core concepts, elements, verification gradient, and trust postures. It also differentiates EATP from existing identity standards.
Inputs & outputs
When to use eatp-reference
- →Explaining EATP concepts
- →Designing trust protocols
- →Verifying agent identity
About this skill
EATP Framework Reference
This skill provides the technical reference for the Enterprise Agent Trust Protocol (EATP) - the trust verification protocol for enterprise AI agents.
Knowledge Sources
This skill is self-contained — all essential EATP knowledge is distilled below from the EATP Core Thesis by Dr. Jack Hong and the EATP specification. If Foundation source docs exist in this repo, read them for additional depth.
What is EATP?
EATP is an open standard for establishing and verifying trust in enterprise AI agent systems. It separates trust establishment (human judgment, once) from trust verification (machine speed, continuously). Every action traces back to human decisions through verifiable cryptographic chains.
EATP operationalizes the CARE framework's governance philosophy as a concrete, implementable protocol.
The Core Insight
The problem conflates two distinct moments:
- Trust establishment: Should this agent be permitted to act within these boundaries? (Human judgment)
- Trust verification: Does this action fall within those boundaries? (Machine verification, milliseconds)
Traditional governance performs both together. EATP separates them.
The Five EATP Elements (Trust Lineage Chain)
1. Genesis Record
The organizational root of trust. A human executive cryptographically commits: "I accept accountability for this AI governance framework." No AI creates its own genesis record.
2. Delegation Record
Authority transfer with constraint tightening. Delegations can only reduce authority, never expand it. A manager with $50K authority can delegate $10K to an agent, not $75K. Mirrors how healthy organizations work.
3. Constraint Envelope
Multi-dimensional operating boundaries across five dimensions:
| Dimension | Examples |
|---|---|
| Financial | Transaction limits, spending caps, cumulative budgets |
| Operational | Permitted/blocked actions |
| Temporal | Operating hours, blackout periods, time-bounded auth |
| Data Access | Read/write permissions, PII handling, data classification |
| Communication | Permitted channels, approved recipients, tone guidelines |
4. Capability Attestation
Signed declaration of authorized capabilities. Prevents capability drift (agents gradually performing unauthorized tasks). Makes authorized scope explicit and verifiable.
5. Audit Anchor
Tamper-evident execution record. Each anchor hashes the previous; modifying any record invalidates the chain forward. Production should use Merkle trees or external checkpointing.
Verification Gradient
Verification is not binary:
| Result | Meaning | Action |
|---|---|---|
| Auto-approved | Within all constraints | Execute and log |
| Flagged | Near constraint boundary | Execute and highlight for review |
| Held | Soft limit exceeded | Queue for human approval |
| Blocked | Hard limit violated | Reject with explanation |
Focuses human attention where it matters: near boundaries and at limits.
Five Trust Postures
Graduated autonomy:
| Posture | Autonomy | Human Role |
|---|---|---|
| Pseudo-Agent | None | Human in-the-loop; agent is interface only |
| Supervised | Low | Human in-the-loop; agent proposes, human approves |
| Shared Planning | Medium | Human on-the-loop; co-planning |
| Continuous Insight | High | Human on-the-loop; agent executes, human monitors |
| Delegated | Full | Human on-the-loop; remote monitoring |
Postures upgrade through demonstrated performance. They downgrade instantly if conditions change.
EATP Operations
- ESTABLISH - Create agent identity and initial trust
- DELEGATE - Transfer authority with constraints
- VERIFY - Validate trust chain and permissions
- AUDIT - Record and trace all trust operations
The Traceability Distinction (Critical)
EATP provides traceability, not accountability.
- Traceability: Trace any AI action back to human authority. EATP delivers this.
- Accountability: Humans understand, evaluate, and bear consequences. No protocol can deliver this.
- Traceability is necessary for accountability but not sufficient.
How EATP Differs from Existing Standards
| Standard | Handles | EATP Adds |
|---|---|---|
| OAuth/OIDC | User authentication | Agent trust delegation |
| SPIFFE/SPIRE | Service identity | Agent autonomy governance |
| Zero-Trust | Network security | Agent governance with trust lineage |
| PKI | Hierarchical identity | Action-to-human traceability |
Existing standards verify identity and access. EATP verifies that actions are within human-established trust boundaries with unbroken chains to human authority.
Cascade Revocation
Trust revocation at any level automatically revokes all downstream delegations. No orphaned agents. Mitigations for propagation latency: short-lived credentials (5-minute validity), push-based revocation, action idempotency.
Quick Reference
Human Authority
|
v [Genesis Record + Capability Attestation]
Agent A
|
v [Delegation Record + Constraint Envelope]
Agent B
|
v [Action + Audit Anchor]
System Action
|
v [Trust Lineage Chain]
Traceable to Human
Verification: Auto-approved → Flagged → Held → Blocked
Postures: Pseudo-Agent → Supervised → Shared Planning → Continuous Insight → Delegated
Operations: ESTABLISH → DELEGATE → VERIFY → AUDIT
Relationship to Companion Frameworks
| Framework | Relationship |
|---|---|
| CARE | EATP operationalizes CARE's governance philosophy |
| COC | COC maps EATP concepts to development guardrails |
| Kailash | Reference implementation (Apache 2.0) |
SDK Implementation Reference
This repo contains the EATP SDK (the trust module). For SDK-specific knowledge, see these companion files:
- eatp-sdk-quickstart.md — Getting started with
pip install kailash, 4-operation lifecycle, store selection - eatp-sdk-api-reference.md — Complete API surface: all exports, module reference, type signatures
- eatp-sdk-patterns.md — Implementation patterns, critical gotchas, security findings, architecture patterns
- eatp-sdk-reasoning-traces.md — Reasoning trace extension: lifecycle, confidentiality, knowledge bridge integration
- eatp-budget-tracking.md — BudgetTracker API, SQLiteBudgetStore, reserve/record lifecycle, threshold callbacks, integer microdollars
- eatp-posture-stores.md — PostureStore protocol, SQLitePostureStore, PostureEvidence, PostureEvaluationResult, posture persistence
- eatp-security-patterns.md — EATP security patterns from red team: lock ordering, integer arithmetic, symlink rejection, fail-closed
- eatp-store-backends.md — Step-by-step guide for adding new TrustPlaneStore backends with 6-requirement security contract
- eatp-trust-plane-security.md — 11 hardened security patterns validated through 14 rounds of red teaming (TrustPlane-specific)
- eatp-trust-plane-enterprise.md — RBAC, OIDC, SIEM, Dashboard, Archive, Shadow mode, Cloud KMS reference
For Detailed Information
If Foundation source docs exist in this repo, read the EATP Core Thesis and EATP specification for additional depth. For comprehensive analysis, invoke the eatp-expert agent.
Limitations
- →Provides traceability, not accountability
How it compares
This skill provides a specific framework for establishing and verifying trust in AI agents, which differs from general identity and access management standards.
Compared to similar skills
eatp-reference side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| eatp-reference (this skill) | 0 | 4mo | No flags | Intermediate |
| red-team-tools-and-methodology | 7 | 6mo | Review | Advanced |
| idapython | 4 | 7mo | No flags | Advanced |
| identify-vault-protocol | 1 | 7mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
You might also like
red-team-tools-and-methodology
davila7
This skill should be used when the user asks to "follow red team methodology", "perform bug bounty hunting", "automate reconnaissance", "hunt for XSS vulnerabilities", "enumerate subdomains", or needs security researcher techniques and tool configurations from top bug bounty hunters.
idapython
mrexodia
IDA Pro Python scripting for reverse engineering. Use when writing IDAPython scripts, analyzing binaries, working with IDA's API for disassembly, decompilation (Hex-Rays), type systems, cross-references, functions, segments, or any IDA database manipulation. Covers ida_* modules (50+), idautils iterators, and common patterns.
identify-vault-protocol
tradingstrategy-ai
Identify an unknown vault protocol based on its smart contract address
hunt-focus-definition
OTRF
Define a focused hunt hypothesis by synthesizing completed system internals and adversary tradecraft research. Use this skill after research has been completed to narrow a high-level hunt topic into a single, concrete attack pattern with clear investigative intent. This skill produces a structured, testable hypothesis and should be used before selecting data sources, defining environment scope, or developing analytics.
hunt-research-system-and-tradecraft
OTRF
Research system internals and adversary tradecraft to ground a threat hunt in real system behavior and realistic abuse patterns. Use this skill at the start of hunt planning, when you are given a high-level hunt topic but lack a clear understanding of how the system normally operates or how adversaries are known to abuse it. This skill informs early hunt direction by producing candidate abuse patterns, key assumptions, and cited sources, and should be used before defining a concrete hunt hypothesis or selecting data sources.
security-research
protoLabsAI
Run a multi-step security intelligence workflow: scan CVE feeds, check Exploit-DB, aggregate security RSS, correlate with target intel, generate threat brief.