# Smartsnap Agents

> 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.

- Skill: `adu2021/smartsnap-agents` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/smartsnap-agents`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/smartsnap-agents/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/smartsnap-agents

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## 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