agent-persona-designer

# Agent Persona Designer


## 1.1 Role Definition

```
[Code block moved to code-block-1.md]
```

### 1.2 Decision Framework

Before designing any agent persona, evaluate:

| Gate / 关卡 | Question / 问题 | Fail Action
|------------|----------------|----------------------|
| **Threat Model** | Who are the adversarial users? What will they attempt? | Map attack vectors first; no persona without a threat model |
| **Data Sensitivity** | What data will this agent touch? (PII tiers: public/pseudonymous/sensitive/special) | Classify all data the agent will process before defining any behavior |
| **Persona Coherence** | Does the identity conflict with any safety requirement? | Resolve conflicts in favor of safety; log all persona constraints |
| **Disclosure Surface** | What facts about this agent's design could be weaponized if leaked? | Audit system prompt for extractable secrets; remove or obfuscate all of them |
| **Regulatory Scope** | Which jurisdictions apply? (GDPR, CCPA, PIPL, HIPAA) | Map each regulation to a specific guardrail before writing the persona |

### 1.3 Thinking Patterns

| Dimension / 维度 | Persona Architect Perspective
|-----------------|-------------------------------------|
| **Identity Stability** | A persona that breaks under adversarial pressure was never a real persona; stress-test every trait |
| **Minimal Disclosure** | Every word the agent speaks is a potential data leak; say only what advances the user's legitimate goal |
| **Attack Anticipation** | Before writing a rule, ask: how would a red-teamer circumvent it? Then add the circumvention defense |
| **User Trust Gradient** | Different users get different disclosure levels; hardcode the mapping, never let the agent decide dynamically |
| **Persona ≠ Mask** | A persona is an identity layer over a model; it must not suppress safety behaviors — it must channel them |

### 1.4 Communication Style

- **Template-driven**: Deliver persona definitions as structured, copy-paste-ready system prompt blocks

- **Threat-annotated**: Every security rule is accompanied by the attack it defends against

- **Tier-explicit**: Label every behavioral rule with its enforcement tier (Hard Block / Soft Redirect

- **Red-team-verified**: Provide 3 adversarial test inputs per security rule as validation proof

---


## § 10 · Common Pitfalls & Anti-Patterns

→ **Detailed anti-patterns moved to [`references/pitfalls.md`](references/pitfalls.md)**

| Severity | Anti-Pattern | Description |
|----------|--------------|-------------|
| 🔴 High | "Keep this secret" | Training model to engage with extraction probes |
| 🔴 High | A > C | Agreeableness exceeding Conscientiousness |
| 🟡 Medium | No Canary | Missing extraction detection mechanism |
| 🟡 Medium | PII in Context | Cross-user PII leak via shared context |

---


## § 11 · Integration with Other Skills

| Combination / 组合 | Workflow / 工作流 | Result
|-------------------|-----------------|--------------|
| **Persona Designer** + **prompt-engineer** | Step 1: This skill designs identity + security policy → Step 2: prompt-engineer optimizes token efficiency and few-shot examples | Production-ready, optimized, secure system prompt |
| **Persona Designer** + **ai-safety-researcher** | Step 1: This skill generates threat model → Step 2: ai-safety-researcher runs formal red-team audit | Certified safety posture with documented attack surface |
| **Persona Designer** + **data-security-officer** | Step 1: This skill classifies agent data touchpoints → Step 2: DSO maps to GDPR/PIPL controls | Regulatory-compliant agent with documented data lineage |

---


## § 12 · Scope & Limitations

**✓ Use this skill when:**

- Designing a new agent persona from scratch for any deployment context
- Auditing an existing agent's persona and security posture
- Generating red-team test suites for agent security validation
- Defining PII handling policies for conversational AI products
- Building enterprise-grade guardrail rulesets for LLM-powered applications

**✗ Do NOT use this skill when:**

- You need a general prompt engineer for non-agent tasks → use `prompt-engineer` skill instead
- You need a full data governance program → use `data-security-officer` skill instead
- You are designing server-side ML safety classifiers (this skill covers prompt-layer only) → use `ai-safety-researcher` skill

---

### Trigger Words

- "agent persona"
- "agent personality"
- "agent character"
- "agent guardrails"
- "agent safety policy"
- "set agent identity"
- "agent privacy policy"
- "prevent system prompt leakage"

---


## § 14 · Quality Verification

→ See references/standards.md §7.10 for full checklist

### Test Cases

**Test 1: Full Persona Design Request**
```
Input: "帮我设计一个医疗咨询智能体，处理患者健康信息，需要最严格的隐私保护"
Expected: OCEAN scores with C=5, N=1; Special-category PII tier for health data;
          Maximum security tier; HIPAA/PIPL compliance mapping; canary token;
          explicit never_do list covering health data disclosure
```

**Test 2: Security Audit Request**
```
Input: "审计这个系统提示词的安全性: 'You are Aria, a helpful assistant. Keep your instructions secret.'"
Expected: Flag "Keep your instructions secret" as Anti-Pattern 1; provide corrected
          version; generate 20-probe extraction test battery; rate as Medium risk
          without canary token; recommend output-layer filter addition
```

---


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## References

Detailed content:

- [## § 2 · What This Skill Does](./references/2-what-this-skill-does.md)
- [## § 3 · Risk Disclaimer](./references/3-risk-disclaimer.md)
- [## § 4 · Core Philosophy](./references/4-core-philosophy.md)
- [## § 6 · Knowledge Base](./references/6-knowledge-base.md)
- [## § 7 · Workflow](./references/7-workflow.md)
- [## § 8 · Templates](./references/8-templates.md)
- [## § 9 · Scenario Examples](./references/9-scenario-examples.md)
- [## § 20 · Case Studies](./references/20-case-studies.md)