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Learn from user answers and build persistent context. Use when agents need to remember user preferences, detect patterns in responses, and reduce repeated questions. Handles preference learning, pattern detection, and context management.
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基于 SOC 职业分类
| name | Context Enhancement |
| slug | context-enhancement |
| description | Learn from user answers and build persistent context. Use when agents need to remember user preferences, detect patterns in responses, and reduce repeated questions. Handles preference learning, pattern detection, and context management. |
| category | AI Tools |
| tags | ["context","learning","preferences","patterns","memory","agent-intelligence"] |
| price_per_call | 0 |
| input_schema | {"type":"object","properties":{"action":{"type":"string","enum":"[Truncated]","description":"Action to perform"},"question":{"type":"string","description":"Question that was asked (for analyze-answer)"},"context":{"type":"string","description":"Context of the question (for analyze-answer)"},"answer":{"type":"string","description":"User's answer (for analyze-answer)"},"user_id":{"type":"string","description":"User identifier (optional, defaults to 'default')"},"scope":{"type":"string","enum":"[Truncated]","description":"Scope of context to retrieve (for get-context)"},"updates":{"type":"object","description":"Context updates to apply (for update-context)"},"history_days":{"type":"number","description":"Number of days of history to analyze (for detect-patterns)"}},"required":["action"]} |
| output_schema | {"type":"object","properties":{"pattern_detected":{"type":"string","description":"Type of pattern detected"},"confidence":{"type":"number","description":"Confidence score (0-1)"},"suggested_rule":{"type":"string","description":"Suggested rule based on pattern"},"context_update":{"type":"object","description":"Updates to apply to user context"},"context":{"type":"object","description":"User context (for get-context)"},"patterns":{"type":"array","description":"Detected patterns (for detect-patterns)"},"_meta":{"type":"object","properties":"[Truncated]"}}} |
Learn from user answers to detect patterns and build persistent context that reduces repeated questions.
FREE. No charge per call. Requires Claw0x API key for authentication.
Context Enhancement analyzes user answers to detect patterns and build a persistent context layer that:
IMPORTANT: This is the server-side version that runs through Claw0x Gateway. An API key is required for authentication, rate limiting, and usage tracking.
export CLAW0X_API_KEY="ck_live_..."
Note: For local use without API key, install via ClawHub: openclaw skills install context-enhancement
FREE. No charge per call.
Problem: Agent asks "Use single or double quotes?" every time
Solution: After first answer, Context Enhancement detects pattern and remembers preference
Result: Agent automatically uses preferred style without asking
Problem: Agent doesn't remember user's deployment workflow
Solution: Learns from answers like "Always deploy to staging first"
Result: Agent follows learned deployment strategy automatically
Problem: Agent asks "TypeScript or JavaScript?" for every new file
Solution: Detects language preference pattern from past answers
Result: Agent defaults to preferred language
Problem: Each team member has to teach the agent their preferences
Solution: Context can be shared across team members
Result: Consistent behavior across team
| When This Happens | Do This | What You Get |
|---|---|---|
| User answers preference question | analyze-answer | Pattern detection + rule generation |
| Need to check user preferences | get-context | Current user context |
| Want to update context manually | update-context | Updated context |
| Want to see all detected patterns | detect-patterns | List of patterns with confidence scores |
Sign up at claw0x.com → Dashboard → Create API Key
export CLAW0X_API_KEY="ck_live_..."
curl -X POST https://api.claw0x.com/v1/call \
-H "Authorization: Bearer $CLAW0X_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"skill": "context-enhancement",
"input": {
"action": "analyze-answer",
"question": "Use TypeScript or JavaScript?",
"context": "Creating auth endpoint",
"answer": "TypeScript",
"user_id": "user_123"
}
}'
{
"pattern_detected": "language_preference",
"confidence": 0.95,
"suggested_rule": "User prefers TypeScript for new code",
"context_update": {
"preferences.language": "TypeScript",
"preferences.use_cases": ["Creating auth endpoint"]
},
"_meta": {
"skill": "context-enhancement",
"latency_ms": 15
}
}
curl -X POST https://api.claw0x.com/v1/call \
-H "Authorization: Bearer $CLAW0X_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"skill": "context-enhancement",
"input": {
"action": "get-context",
"user_id": "user_123"
}
}'
Problem: New agent doesn't know user preferences
Solution: Use Context Enhancement to learn from first interactions
Example:
// First interaction
await claw0x.call('context-enhancement', {
action: 'analyze-answer',
question: 'Preferred code style?',
answer: 'Single quotes, no semicolons',
user_id: 'user_123'
});
// Later interactions - agent checks context
const context = await claw0x.call('context-enhancement', {
action: 'get-context',
user_id: 'user_123',
scope: 'preferences'
});
// context.preferences.code_style.quotes === 'single'
Problem: Agent asks same questions repeatedly
Solution: Detect patterns and generate rules
Example:
// After 3 similar answers, pattern is detected
await claw0x.call('context-enhancement', {
action: 'detect-patterns',
user_id: 'user_123'
});
// Returns: [
// {
// pattern: 'deployment_strategy',
// frequency: 3,
// confidence: 0.98,
// suggested_rule: 'Always deploy to staging first'
// }
// ]
Problem: Each team member has different preferences
Solution: Use team-level context
Example:
// Team lead sets preferences
await claw0x.call('context-enhancement', {
action: 'update-context',
user_id: 'team_acme',
updates: {
'preferences.language': 'TypeScript',
'preferences.deployment': 'staging-first',
'preferences.code_style.quotes': 'single'
}
});
// All team members inherit team context
const teamContext = await claw0x.call('context-enhancement', {
action: 'get-context',
user_id: 'team_acme'
});
Problem: Agent needs to learn complex workflows
Solution: Analyze multiple interactions to detect patterns
Example:
// Interaction 1
await claw0x.call('context-enhancement', {
action: 'analyze-answer',
question: 'Run tests before deploy?',
answer: 'Yes, always',
user_id: 'user_123'
});
// Interaction 2
await claw0x.call('context-enhancement', {
action: 'analyze-answer',
question: 'Deploy to staging first?',
answer: 'Yes',
user_id: 'user_123'
});
// Pattern detected: deployment_strategy
// Rule generated: "Run tests, then deploy to staging, then production"
import { Claw0xClient } from '@claw0x/sdk';
const claw0x = new Claw0xClient(process.env.CLAW0X_API_KEY);
agent.onQuestion(async (question, context, answer) => {
// Analyze answer to detect patterns
const result = await claw0x.call('context-enhancement', {
action: 'analyze-answer',
question,
context,
answer,
user_id: agent.userId
});
if (result.pattern_detected) {
console.log(`Pattern detected: ${result.pattern_detected}`);
console.log(`Rule: ${result.suggested_rule}`);
}
});
agent.beforeAction(async (action) => {
// Check context before taking action
const context = await claw0x.call('context-enhancement', {
action: 'get-context',
user_id: agent.userId,
scope: 'preferences'
});
// Apply preferences to action
if (context.preferences.language) {
action.language = context.preferences.language;
}
});
from claw0x import Claw0xClient
claw0x = Claw0xClient(api_key=os.environ['CLAW0X_API_KEY'])
def analyze_answer(question, context, answer, user_id):
result = claw0x.call('context-enhancement', {
'action': 'analyze-answer',
'question': question,
'context': context,
'answer': answer,
'user_id': user_id
})
if result['pattern_detected']:
print(f"Pattern: {result['pattern_detected']}")
print(f"Rule: {result['suggested_rule']}")
return result
def get_user_context(user_id):
return claw0x.call('context-enhancement', {
'action': 'get-context',
'user_id': user_id,
'scope': 'all'
})
const axios = require('axios');
async function enhanceContext(question, answer, userId) {
const response = await axios.post('https://api.claw0x.com/v1/call', {
skill: 'context-enhancement',
input: {
action: 'analyze-answer',
question,
answer,
user_id: userId
}
}, {
headers: {
'Authorization': `Bearer ${process.env.CLAW0X_API_KEY}`,
'Content-Type': 'application/json'
}
});
return response.data;
}
async function getUserPreferences(userId) {
const response = await axios.post('https://api.claw0x.com/v1/call', {
skill: 'context-enhancement',
input: {
action: 'get-context',
user_id: userId,
scope: 'preferences'
}
}, {
headers: {
'Authorization': `Bearer ${process.env.CLAW0X_API_KEY}`,
'Content-Type': 'application/json'
}
});
return response.data.context.preferences;
}
User Answer
↓
Context Enhancement (analyze-answer)
↓
Pattern Detection
├─ Language Preference (confidence: 0.95)
├─ Deployment Strategy (confidence: 0.98)
├─ Code Style (confidence: 0.92)
└─ Generic Preference (confidence: 0.7)
↓
Rule Generation
├─ "User prefers TypeScript"
├─ "Deploy to staging first"
└─ "Use single quotes"
↓
Context Update
├─ preferences.language = "TypeScript"
├─ preferences.deployment = "staging-first"
└─ preferences.code_style.quotes = "single"
↓
Agent Uses Context
├─ Checks preferences before asking
├─ Applies learned rules automatically
└─ Reduces repeated questions
action: "analyze-answer" (required)question: Question that was asked (required)context: Context of the question (optional)answer: User's answer (required)user_id: User identifier (optional, defaults to "default")action: "get-context" (required)user_id: User identifier (optional)scope: "preferences" | "patterns" | "rules" | "all" (optional, defaults to "all")action: "update-context" (required)user_id: User identifier (optional)updates: Object with context updates (required)action: "detect-patterns" (required)user_id: User identifier (optional)history_days: Number of days to analyze (optional, defaults to 30){
"pattern_detected": "language_preference",
"confidence": 0.95,
"suggested_rule": "User prefers TypeScript for new code",
"context_update": {
"preferences.language": "TypeScript"
},
"_meta": {
"skill": "context-enhancement",
"latency_ms": 15
}
}
{
"context": {
"preferences": {
"language": "TypeScript",
"deployment": "staging-first"
},
"patterns": [
{
"pattern": "language_preference",
"confidence": 0.95,
"examples": ["Creating auth endpoint"],
"rule": "User prefers TypeScript"
}
],
"rules": [
"User prefers TypeScript for new code",
"Deploy to staging first"
]
},
"_meta": {
"skill": "context-enhancement",
"latency_ms": 10
}
}
{
"patterns": [
{
"pattern": "language_preference",
"frequency": 5,
"confidence": 0.95,
"suggested_rule": "User prefers TypeScript"
},
{
"pattern": "deployment_strategy",
"frequency": 3,
"confidence": 0.98,
"suggested_rule": "Deploy to staging first"
}
],
"_meta": {
"skill": "context-enhancement",
"latency_ms": 12
}
}
| Error Code | Meaning | Solution |
|---|---|---|
| 400 | Invalid input or missing required fields | Check input schema |
| 401 | Missing or invalid API key | Set CLAW0X_API_KEY |
| 500 | Internal error (not billed) | Retry or contact support |
| Pattern | Trigger | Confidence | Example Rule |
|---|---|---|---|
language_preference | TypeScript/JavaScript questions | 0.95 | "User prefers TypeScript" |
deployment_strategy | Deployment workflow questions | 0.98 | "Deploy to staging first" |
code_style | Code style questions | 0.92 | "Use single quotes, no semicolons" |
generic_preference | Any other question | 0.7 | "User answered X for Y" |
detect-patterns to review learned patternsThis skill is provided by Claw0x, the native skills layer for AI agents.
Explore more skills: claw0x.com/skills