How do I install the Predict skill?

Run npx skillmds@latest add thinkfleetai/predict in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Predict safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Predict?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Predict free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Predict?

thinkfleetai (@thinkfleetai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Predict by thinkfleetai — Agent Skill

Name: Predict
Author: thinkfleetai

Predict

Forecast what a subject will do next from their mined behavior patterns — with a calibrated, horizon-decayed confidence and provenance. Use when the user asks "what is this user/account likely to do next", "will X churn/convert/ reorder", or wants a forward prediction rather than a recall of known facts. This is a MemMesh capability a plain memory layer does not have.

thinkfleetai 50cc8c3 2.4 KB Updated Sep 17, 2026

File contents

predict

⚙️ Requires MemMesh hosted mode. Calibrated prediction and behavior discovery run on the hosted engine — set your mm- API key. On a local / open-source install these tools (memory_predict, memory_build_context) are not registered; if a call returns "unknown tool", tell the user this is a hosted capability and fall back to search / recall for what's already known.

Turn accumulated memory into a forward forecast. Unlike search ("what do we know"), predict answers "what happens next" — and it tells you how confident it honestly is, or abstains.

Forward behavior prediction (local MCP)

{ "name": "memory_predict",
  "arguments": { "subjectKind": "user", "subjectId": "<id>",
                 "horizonDays": 30, "minConfidence": 0.5, "limit": 20 } }

Returns ranked predictions, each with a confidence decayed over the horizon and the provenance behind it. Confidence is calibrated — 0.8 means it's right ~80% of the time — not a raw model logit.

Read the result honestly

Present the top predictions with their confidence and horizon.
If a prediction abstains (not enough evidence), say so plainly — "not enough signal yet" is a valid, correct answer, and the point of MemMesh.
Cite the evidence ids so the user can trace why. Use the why skill to dig into calibration/provenance.

Predict ANY target (hosted / SDK)

The declarative "predict anything" surface (lattice.predictTarget with target.kind ∈ event_occurrence | numeric | event_time | anomaly) lets you add a new prediction with no code change — just name the target. It runs on the hosted gRPC/SDK path:

await memory.lattice.predictTarget({
  subject: { kind: "account", externalId: "acme" },
  target:  { kind: "event_occurrence", name: "churn" },
});

Prereq

Predictions come from mined behavior_pattern memories. If memory_predict returns nothing, the subject may not have enough observed history yet — feed more via observe, or check what patterns exist with the behaviors skill.

---name: predictdescription: Forecast what a subject will do next from their mined behavior patterns — with a calibrated, horizon-decayed confidence and provenance. Use when the user asks "what is this user/account likely to do next", "will X churn/convert/ reorder", or wants a forward prediction rather than a recall of known facts. This is a MemMesh capability a plain memory layer does not have.---# predict> **⚙️ Requires MemMesh hosted mode.** Calibrated prediction and behavior discovery run on the hosted engine — set your `mm-` API key. On a local / open-source install these tools (`memory_predict`, `memory_build_context`) are not registered; if a call returns "unknown tool", tell the user this is a hosted capability and fall back to `search` / `recall` for what's already known.Turn accumulated memory into a forward forecast. Unlike `search` ("what do weknow"), `predict` answers "what happens next" — and it tells you how confident ithonestly is, or abstains.## Forward behavior prediction (local MCP)```jsonc{ "name": "memory_predict", "arguments": { "subjectKind": "user", "subjectId": "<id>", "horizonDays": 30, "minConfidence": 0.5, "limit": 20 } }```Returns ranked predictions, each with a confidence decayed over the horizon andthe provenance behind it. Confidence is **calibrated** — 0.8 means it's right~80% of the time — not a raw model logit.## Read the result honestly- Present the top predictions with their confidence and horizon.- If a prediction **abstains** (not enough evidence), say so plainly — "not enough signal yet" is a valid, correct answer, and the point of MemMesh.- Cite the evidence ids so the user can trace *why*. Use the `why` skill to dig into calibration/provenance.## Predict ANY target (hosted / SDK)The declarative "predict anything" surface (`lattice.predictTarget` with`target.kind ∈ event_occurrence | numeric | event_time | anomaly`) lets you adda new prediction with **no code change** — just name the target. It runs on thehosted gRPC/SDK path:```tsawait memory.lattice.predictTarget({ subject: { kind: "account", externalId: "acme" }, target: { kind: "event_occurrence", name: "churn" },});```## PrereqPredictions come from mined `behavior_pattern` memories. If `memory_predict`returns nothing, the subject may not have enough observed history yet — feed morevia `observe`, or check what patterns exist with the `behaviors` skill.

Frequently asked questions