Praesidia

v2.0.1

Verify AI agents, check trust scores (0-100), fetch A2A agent cards, discover marketplace agents, apply guardrails for security and compliance. Use when user mentions agent verification, trust scores, agent discovery, A2A protocol, agent identity, agent marketplace, guardrails, security policies, content moderation, or asks "is this agent safe?" or "find agents that can [task]" or "apply guardrails to protect my agent".

⭐ 4· 1.9k·0 current·0 all-time

by@msoica

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OpenClaw

Benign

medium confidence

✓

Purpose & Capability

Name, description, and runtime instructions all describe calling a Praesidia REST API to fetch agent cards, trust scores, discovery results, and manage guardrails — the single required credential (PRAESIDIA_API_KEY) is appropriate for that purpose.

ℹ

Instruction Scope

SKILL.md instructs only API calls (web_fetch) to Praesidia endpoints and presenting results. It does tell the agent to obtain orgId from the user's profile/context and to read OpenClaw config (~/.openclaw/openclaw.json) for the API key/URL; both are reasonable for this functionality but are assumptions the agent will need to access user/context data to populate orgId. No instructions request unrelated files or credentials.

✓

Install Mechanism

Instruction-only skill (no install spec, no code files). Lowest install risk — nothing is written to disk by an installer.

ℹ

Credentials

The skill requires a single credential (PRAESIDIA_API_KEY) which matches the API usage. SKILL.md however uses PRAESIDIA_API_URL in examples (and asks users to place it in openclaw.json) but PRAESIDIA_API_URL is not declared in the top-level required env list/metadata — minor inconsistency that could cause runtime confusion but not an escalated permission request.

✓

Persistence & Privilege

always is false and there is no install-time agent persistence or cross-skill config modification described. The skill can be invoked autonomously (platform default), which is expected for this type of service.

Assessment

This skill appears to do what it says: make authenticated calls to a Praesidia API to verify agents and manage guardrails. Before installing: 1) Confirm the Praesidia service and domain (api.praesidia.ai) are legitimate and you trust the provider. 2) Limit the API key scope and rotate keys if possible; do not reuse high-privilege keys. 3) Note SKILL.md expects PRAESIDIA_API_URL in config even though only PRAESIDIA_API_KEY is declared — set the URL explicitly in ~/.openclaw/openclaw.json or confirm with the publisher. 4) Understand the agent will need access to your orgId/context to call organization-scoped endpoints; verify you are comfortable with the skill reading that context. 5) Because this is instruction-only with no source code in the package and the skill's source is listed as unknown, consider obtaining the publisher/homepage or additional provenance before trusting it in sensitive environments.

Like a lobster shell, security has layers — review code before you run it.

Runtime requirements

🛡️ Clawdis

EnvPRAESIDIA_API_KEY

Primary envPRAESIDIA_API_KEY

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1.9kdownloads

4stars

5versions

Updated 1mo ago

v2.0.1

MIT-0

Praesidia Agent Identity, Verification & Guardrails

Verify AI agents, check trust scores (0-100), discover marketplace agents, and apply guardrails for security and compliance.

Core Capabilities

Verify agents - Check if an agent is registered, verified, and trustworthy
Trust scores - View 0-100 trust ratings and verification status
Agent discovery - Search marketplace for public agents by capability
Guardrails - Apply security policies and content moderation to agents
A2A protocol - Fetch standard Agent-to-Agent protocol cards

Prerequisites

Praesidia account: https://praesidia.ai
API key from Settings → API Keys
Configure in ~/.openclaw/openclaw.json:

{
  "skills": {
    "entries": {
      "praesidia": {
        "apiKey": "pk_live_your_key_here",
        "env": {
          "PRAESIDIA_API_URL": "https://api.praesidia.ai"
        }
      }
    }
  }
}

For local development, use http://localhost:3000 as the URL.

Quick Reference

1. Verify an Agent

User says: "Is agent chatbot-v2 safe?" / "Verify agent chatbot-v2"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/agents/chatbot-v2/agent-card",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Accept": "application/json"
  }
})

Present to user:

✅ Agent name & description
🛡️ Trust score (0-100) and trust level
✓ Verification status (verified date)
🔧 Capabilities (what the agent can do)
📜 Compliance (SOC2, GDPR, etc.)
🔗 Agent card URL

Example output:

✅ ChatBot V2 is verified and safe to use!

Trust Score: 92.5/100 (VERIFIED)
Status: ACTIVE
Capabilities: message:send, task:create, data:analyze
Compliance: SOC2, GDPR
Last verified: 2 days ago

Agent card: https://api.praesidia.ai/agents/chatbot-v2/agent-card

2. List Guardrails for an Agent

User says: "What guardrails are configured for my agent?" / "Show me security policies for chatbot-v2"

Your action:

// First, get the user's organization ID from their profile or context
// Then fetch guardrails
web_fetch({
  url: "${PRAESIDIA_API_URL}/organizations/${orgId}/guardrails?agentId=${agentId}",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Accept": "application/json"
  }
})

Present to user:

List of guardrails with:
- Name and description
- Type (RULE, ML, LLM)
- Category (CONTENT, SECURITY, COMPLIANCE, etc.)
- Action (BLOCK, WARN, REDACT, REPLACE)
- Scope (INPUT, OUTPUT, BOTH)
- Enabled status
- Trigger count

Example output:

Found 3 guardrails for ChatBot V2:

1. PII Detection (ENABLED)
   - Type: ML | Category: SECURITY
   - Scope: BOTH (input & output)
   - Action: REDACT sensitive data
   - Triggered: 45 times

2. Toxic Language Filter (ENABLED)
   - Type: RULE | Category: CONTENT
   - Scope: BOTH
   - Action: BLOCK toxic content
   - Triggered: 12 times

3. Financial Advice Warning (ENABLED)
   - Type: LLM | Category: COMPLIANCE
   - Scope: OUTPUT only
   - Action: WARN if detected
   - Triggered: 3 times

3. Get Available Guardrail Templates

User says: "What guardrail templates are available?" / "Show me security templates"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/organizations/${orgId}/guardrails/templates",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Accept": "application/json"
  }
})

Available Templates:

Content Moderation:

TOXIC_LANGUAGE - Detect toxic/harmful language
PROFANITY_FILTER - Filter profanity
HATE_SPEECH - Detect hate speech
VIOLENCE_DETECTION - Detect violent content
ADULT_CONTENT - Filter adult content

Security:

PII_DETECTION - Detect personally identifiable information
CREDIT_CARD_DETECTION - Detect credit card numbers
SSN_DETECTION - Detect social security numbers
API_KEY_DETECTION - Detect leaked API keys
PROMPT_INJECTION - Detect prompt injection attacks
JAILBREAK_DETECTION - Detect jailbreak attempts

Compliance:

FINANCIAL_ADVICE - Flag financial advice
MEDICAL_ADVICE - Flag medical advice
LEGAL_ADVICE - Flag legal advice
GDPR_COMPLIANCE - Enforce GDPR rules
HIPAA_COMPLIANCE - Enforce HIPAA rules

Brand Safety:

COMPETITOR_MENTIONS - Detect competitor mentions
POSITIVE_TONE - Ensure positive tone
BRAND_VOICE - Maintain brand voice
OFF_TOPIC_DETECTION - Detect off-topic responses

Accuracy:

HALLUCINATION_DETECTION - Detect hallucinations
FACT_CHECKING - Verify facts
SOURCE_VALIDATION - Validate sources
CONSISTENCY_CHECK - Check consistency

4. Apply a Guardrail to an Agent

User says: "Add PII detection to my chatbot" / "Apply toxic language filter to agent xyz"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/organizations/${orgId}/guardrails",
  method: "POST",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    name: "PII Detection",
    description: "Automatically detect and redact PII",
    agentId: "${agentId}",
    template: "PII_DETECTION",
    type: "ML",
    category: "SECURITY",
    scope: "BOTH",
    action: "REDACT",
    severity: "HIGH",
    isEnabled: true,
    priority: 0
  })
})

Guardrail Options:

Type:

RULE - Simple regex/keyword matching (fast)
ML - Machine learning model (balanced)
LLM - LLM-powered validation (most accurate)

Category:

CONTENT - Content moderation
SECURITY - Security checks
COMPLIANCE - Regulatory compliance
BRAND - Brand safety
ACCURACY - Accuracy checks
CUSTOM - Custom rules

Scope:

INPUT - Validate user input only
OUTPUT - Validate agent output only
BOTH - Validate both directions

Action:

BLOCK - Block the request/response entirely
WARN - Log warning but allow through
REDACT - Mask the offending content
REPLACE - Replace with alternative content
RETRY - Retry with modified prompt
ESCALATE - Escalate to human review

Severity:

LOW, MEDIUM, HIGH, CRITICAL

5. Validate Content Against Guardrails

User says: "Check if this message passes guardrails: [content]"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/organizations/${orgId}/guardrails/validate",
  method: "POST",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    content: "User's message here",
    agentId: "${agentId}",
    scope: "INPUT"
  })
})

Response shows:

Whether content passed or failed
Which guardrails were triggered
Suggested actions (block, redact, warn)
Modified content (if redaction applied)

6. Discover Public Agents

User says: "Find public data analysis agents" / "Show me chatbot agents"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/agents/discovery?visibility=PUBLIC&search=data",
  headers: { "Accept": "application/json" }
  // Authorization optional for public agents (includes it for more results)
})

Filters available:

?visibility=PUBLIC - public marketplace agents
?role=SERVER - agents that provide services
?role=CLIENT - agents that consume services
?status=ACTIVE - only active agents
?search=keyword - search by name/description

Present to user:

List of matching agents with:
- Name, description, agent ID
- Trust score and level
- Role (SERVER/CLIENT)
- Key capabilities
- Link to full card

Example output:

Found 2 public data analysis agents:

1. OpenData Analyzer (VERIFIED - 88.0/100)
   - Capabilities: data:analyze, chart:generate, report:create
   - Role: SERVER | Status: ACTIVE
   - Card: https://api.praesidia.ai/agents/opendata-1/agent-card

2. CSV Processor (STANDARD - 70.0/100)
   - Capabilities: file:parse, data:transform, export:json
   - Role: SERVER | Status: ACTIVE
   - Card: https://api.praesidia.ai/agents/csv-proc/agent-card

7. List User's Agents

User says: "Show my agents" / "List all my server agents"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/agents/discovery?role=SERVER",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Accept": "application/json"
  }
})

This returns all agents the user has access to (their own + team/org agents).

Trust Levels Guide

Present trust information clearly to help users make decisions:

Trust Score	Level	Meaning	Recommendation
90-100	VERIFIED	Fully vetted, compliant, verified identity	✅ Safe to use
70-89	STANDARD	Good reputation, basic verification	✅ Generally safe
50-69	LIMITED	Minimal verification	⚠️ Use with caution
0-49	UNTRUSTED	Not verified or poor reputation	❌ Not recommended

Always show the trust score numerically (e.g., 92.5/100) and the level (e.g., VERIFIED).

Error Handling

Error	Meaning	What to tell user
401 Unauthorized	API key missing/invalid	"Check PRAESIDIA_API_KEY in ~/.openclaw/openclaw.json"
403 Forbidden	No permission	"You don't have access to this agent"
404 Not Found	Agent doesn't exist	"Agent not found. Check the agent ID"
500 Server Error	Praesidia API issue	"Praesidia API temporarily unavailable. Try again"

API Endpoints

GET /agents/:id/agent-card

Fetch detailed agent card with trust data.

Auth: Required for private/team/org agents, optional for public Returns: A2A agent card + Praesidia extensions (trust, compliance)

GET /agents/discovery

List/search agents with filters.

Auth: Optional (more results with auth) Query params: role, status, visibility, search Returns: Array of agent summaries with card URLs

Guardrails Best Practices

When helping users with guardrails:

Start with templates - Use predefined templates before custom rules
Layer security - Combine multiple guardrails (PII + Toxic + Compliance)
Test before enabling - Use validate endpoint to test content first
Monitor triggers - Check stats regularly to tune thresholds
Scope appropriately - Use INPUT for user content, OUTPUT for agent responses
Choose right action:
- BLOCK for critical security issues (PII, prompt injection)
- REDACT for sensitive data that can be masked
- WARN for compliance/brand issues that need logging
- ESCALATE for edge cases requiring human review

Best Practices

Always verify before recommending - Check trust score before suggesting an agent
Explain trust levels - Users may not know what "VERIFIED" means
Filter by SERVER role - When users want agents to use/call
Show compliance - Important for enterprise users (SOC2, GDPR)
Present trust score numerically - 92.5/100 is clearer than just "VERIFIED"
Layer guardrails - Combine security, content, and compliance guardrails

Common User Patterns

Pattern 1: Safety Check

User: "Is agent xyz safe to use?"
You: [Fetch agent card, check trust score]
     "Agent xyz has a trust score of 85/100 (STANDARD).
      It's verified for basic operations. What would you like to use it for?"

Pattern 2: Capability Discovery

User: "I need an agent that can analyze spreadsheets"
You: [Search discovery with visibility=PUBLIC&search=spreadsheet]
     "I found 3 spreadsheet analysis agents. The highest rated is..."

Pattern 3: Fleet Management

User: "Show me all my agents that are inactive"
You: [Fetch discovery with status=INACTIVE]
     "You have 2 inactive agents: [list with trust scores]"

Pattern 4: Apply Security

User: "I need to secure my chatbot against PII leaks"
You: [List available templates, recommend PII_DETECTION]
     [Apply guardrail with REDACT action on BOTH scope]
     "I've added PII Detection (ML-powered) to your chatbot.
      It will automatically redact sensitive information in both
      user inputs and bot responses."

Pattern 5: Compliance Check

User: "My agent handles healthcare data. What guardrails should I add?"
You: [Check if HIPAA compliance is required]
     [Recommend HIPAA_COMPLIANCE + PII_DETECTION + AUDIT_LOGGING]
     "For healthcare data, I recommend these guardrails:
      1. HIPAA Compliance (BLOCK on violations)
      2. PII Detection (REDACT)
      3. Medical Advice Warning (WARN)
      Would you like me to apply these?"

Environment Variables

PRAESIDIA_API_KEY (required) - Your API key from https://app.praesidia.ai
PRAESIDIA_API_URL (optional) - Defaults to https://api.praesidia.ai
- Production: https://api.praesidia.ai
- Local dev: http://localhost:3000
- Custom: Your deployment URL

Additional Resources

Full setup guide: See README.md in this skill folder
API documentation: https://app.praesidia.ai/docs/api
A2A protocol: https://a2a-protocol.org
Support: hello@praesidia.ai or https://discord.gg/e9EwZfHS

Security & Privacy

All production requests use HTTPS
API keys stored in OpenClaw config (never exposed to users)
Private/team/org agents require authentication
Public agents accessible without auth
Trust verification protects against malicious agents

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