Determinability Checker
/install determinability-checker
Determinability Checker
Causal Sufficiency Determinability Checker
Algorithm implementation based on the paper Target Determinability under Partial Causal Observation (Wang, 2026).
Core Question
Before an Agent calls other skills, it asks itself:
"Based on current evidence, am I sufficient to make this judgment?"
Determinability Results
| Result | Meaning | Agent Action |
|---|---|---|
| DETERMINED | Evidence is sufficient; target is zero-error determinable | Execute immediately; no wasted tokens |
| NOT_DETERMINED | Evidence is insufficient; indistinguishable counterexample exists | Return missing-evidence list; guide next skill to call |
Theoretical Foundation
- Theorem 10.1 (Finite Model Checking): The algorithm returns Determined if and only if the target is zero-error determinable; returns NotDetermined with a counterexample pair certificate.
- Theorem 8.2 (Constrained Evidence Coverage): An evidence subset covers all conflict edges if and only if the target becomes determinable from the joint observation.
- Quotient Factorization (Lemma 7.1): D is determinable from Omega if and only if D is constant on every observation equivalence class, if and only if D = g composed with Omega.
Usage Example
Request
{
"session_id": "audit-001",
"question": "Does the final output have a valid verification event?",
"configs": [
{"config_id": "C1", "tool": "code", "has_verif": true, "verif_hash": "valid", "output": "correct", "target": 1},
{"config_id": "C2", "tool": "code", "has_verif": false, "verif_hash": "none", "output": "correct", "target": 0}
],
"omega_field": "output",
"target_field": "target",
"evidence_fields": ["tool", "has_verif", "verif_hash"]
}
Response
{
"session_id": "audit-001",
"question": "Does the final output have a valid verification event?",
"determinability": "NOT_DETERMINED",
"can_proceed": false,
"counterexample": {
"config1": "C1",
"config2": "C2",
"observation": "correct",
"target1": 1,
"target2": 0
},
"missing_evidence": ["tool", "has_verif", "verif_hash"],
"next_skill_suggestion": "Supplement the following evidence items: tool, has_verif, verif_hash",
"message": "Non-determinability proven: configs C1 and C2 share observation correct but differ on target (1 vs 0)."
}
Cognitive Emergence Lab
[email protected]
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install determinability-checker - After installation, invoke the skill by name or use
/determinability-checker - Provide required inputs per the skill's parameter spec and get structured output
What is Determinability Checker?
Causal Sufficiency Determinability Checker — Meta-Skill Gatekeeper based on JEP Paper CheckDeterminability Algorithm. It is an AI Agent Skill for Claude Code / OpenClaw, with 58 downloads so far.
How do I install Determinability Checker?
Run "/install determinability-checker" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Determinability Checker free?
Yes, Determinability Checker is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Determinability Checker support?
Determinability Checker is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Determinability Checker?
It is built and maintained by JEP (Judgment Event Protocol) (@schchit); the current version is v1.0.2.