Shift agent verification from post-hoc external judgment to proactive in-situ self-evidence curation. Agents generate atomic evidence tuples during execution, guided by 3C principles (Completeness, Conciseness, Creativity), with structured verifier feedback across four dimensions—reducing verification costs and enabling dense learning signals.
Scanned 9/9/2026
Install to Claude Code
npx -y skills add ADu2021/skillXiv --skill smartsnap-agents --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: smartsnap-agents
title: "SmartSnap: Proactive Evidence Seeking for Self-Verifying Agents"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: https://arxiv.org/abs/2512.22322
keywords: [agents, verification, self-improvement, evidence-gathering]
description: "Shift agent verification from post-hoc external judgment to proactive in-situ self-evidence curation. Agents generate atomic evidence tuples during execution, guided by 3C principles (Completeness, Conciseness, Creativity), with structured verifier feedback across four dimensions—reducing verification costs and enabling dense learning signals."
---
## Overview
SmartSnap enables agents to prove their success rather than waiting for external verification.
## Core Technique
**3C Evidence Principles:**
```python
evidence = agent.gather_evidence(
completeness=include_all_pivotal_actions,
conciseness=minimize_redundancy,
creativity=generate_additional_proof_actions
)
```
**Evidence Definition:**
Atomic (action, observation) tuples—objective facts.
## When to Use
Use when: Agent verification critical, reducing cognitive load, dense learning signals.
## References
- Proactive evidence gathering
- Atomic evidence definition
- Structured verifier feedback
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