habits.insights — Habit Intelligence Insights

x402

habits.insights — Habit Intelligence Insights is a paid API for AI agents from api.mydev-tools.tech, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-27).

Generates AI-driven insights from user habit data, surfacing patterns, trends, and actionable intelligence about behavioral habits.

Price / call$0.05

Seller volume →—

Success—

StatusUnknown

How this service works

Summarised insights for agent workflows.

Output

Returns AI-generated insights derived from habit data, including identified patterns, consistency trends, behavioral observations, and potentially actionable recommendations based on the submitted habit records.

Example use cases

Monthly habit review for a user

“It's end of month — pull insights from my habit log for October and tell me which habits I'm building successfully versus which ones are falling apart.”

Identifying consistency patterns

“Look at my exercise and meditation habits over the last 90 days and surface any patterns in when I'm most consistent versus when I tend to skip.”

Behavioral intelligence for a wellness coach

“I have habit data for my client who's been tracking sleep, diet, and movement for 8 weeks — generate insights that show what behavioral patterns are emerging so I can adjust their coaching plan.”

Technical details

Endpoint

POSThttps://api.mydev-tools.tech/agent/v1/habits/insights?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need to extract meaningful intelligence and patterns from existing habit data rather than creating a plan or performing raw analysis. It is best suited for surfacing trends, behavioral summaries, and key takeaways — as opposed to habits.analyze (raw analysis) or habits.plan (plan generation). Use it when the user wants to understand what their habit history means.

Side effects

Insufficient habit data provided — not enough records to generate meaningful insights
Malformed or missing required fields in the request body
Payment not completed or USDC balance insufficient (x402 payment required)
Habit data format not recognized or unparseable
Service temporarily unavailable or rate limit exceeded

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "watchouts": [
      "Single-habit sample: correlations are indicative, not significant."
    ],
    "highlights": [
      "Rising energy trend correlates with completion above 0.7."
    ],
    "recommendations": [
      "Add a second habit to enable cross-habit correlation."
    ],
    "aggregate_summary": {
      "risk_level": "low",
      "habits_tracked": 1
    }
  }
}

Reviews

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Example use cases

Monthly habit review for a user

“It's end of month — pull insights from my habit log for October and tell me which habits I'm building successfully versus which ones are falling apart.”

Identifying consistency patterns

“Look at my exercise and meditation habits over the last 90 days and surface any patterns in when I'm most consistent versus when I tend to skip.”

Behavioral intelligence for a wellness coach

Technical details

Endpoint

POSThttps://api.mydev-tools.tech/agent/v1/habits/insights?utm_source=zero.xyz

When to prefer

Side effects

Insufficient habit data provided — not enough records to generate meaningful insights
Malformed or missing required fields in the request body
Payment not completed or USDC balance insufficient (x402 payment required)
Habit data format not recognized or unparseable
Service temporarily unavailable or rate limit exceeded

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "watchouts": [
      "Single-habit sample: correlations are indicative, not significant."
    ],
    "highlights": [
      "Rising energy trend correlates with completion above 0.7."
    ],
    "recommendations": [
      "Add a second habit to enable cross-habit correlation."
    ],
    "aggregate_summary": {
      "risk_level": "low",
      "habits_tracked": 1
    }
  }
}