AI coding agent skills for Hindsight — long-term memory for AI agents.
Works with Claude Code, Codex, Gemini CLI, Cursor, Kiro, and Factory Droid.
Most agents forget everything between sessions. Hindsight fixes that. It extracts facts, builds entity graphs, and synthesizes mental models from conversations — so your agent remembers users, learns patterns, and gets better over time.
These skills give your coding agent deep expertise in Hindsight's architecture, so you can go from "I want my agent to remember things" to working code in a single session.
You: /hindsight-architect
Claude: [reads README, package.json, scans directory structure, checks for LLM usage]
I've looked at your project. It's a Next.js + Python customer support
platform with OpenAI calls in the ticket handler and no existing
Hindsight config.
How do you want to approach adding memory?
> A) Find opportunities for me
B) I already know what I want
C) Chat about it
You: A
Claude: [examines handlers, routes, LLM calls, data flows]
Here's how memory would work in this application:
Memory flow: Retain full ticket conversations per-user, recall past
context before each agent response, and synthesize patterns over time.
Integration points:
- api/chat.py:42 — add retain after each conversation turn
- api/chat.py:18 — add recall before the LLM call
- lib/agent.ts:31 — inject mental model content into system prompt
What this enables: The agent remembers every user's history, learns
each customer's product setup, and detects recurring issues across
your entire base.
Is this the direction you want, or would you adjust the scope?
You: This is right. Let's do all of it.
Claude: Are there any of these considerations for your solution?
> [ ] Enterprise security
[ ] Data privacy / PII
[ ] Regulatory compliance
[x] None of these
Claude: [presents the three architecture decisions with reasoning]
1. What to retain — full conversations, upsert on session_id
2. Tag schema — userId:{id}, customerId:{id}
3. Mental models — per-user preferences, per-customer usage, cross-customer issues
Does this design work?
You: Yes.
Claude: [asks about deployment: Cloud, Local, or Self-hosted]
[asks about recall optimization: Balanced, Completeness, Cost, or Custom]
Claude: # Memory Implementation Plan: support-agent
## Bank Configuration — single bank, per-user tag isolation
## Tag Schema — userId:{id}, customerId:{id}
## Retain Strategy — full conversation upsert with document_id
## Recall Strategy — 10K token budget, any_strict tag matching
## Mental Models — 3 models with auto-refresh
## Client Setup — Python SDK with environment variables
## Implementation Checklist — 9 steps, executable in order
Ready to implement this plan?
> A) Yes, implement it
B) Not now
You: A
Claude: [installs SDK, adds env vars, wires up retain/recall, creates mental
models, runs test scenario — committing as it goes]
A guided conversation. One implementation plan. Then it builds it for you.
| Skill | What it does |
|---|---|
/hindsight-architect |
Memory architecture design. Understands your application, identifies where memory adds value, produces a complete implementation plan, and optionally implements it — bank config, tag schema, retain/recall patterns, mental models, and working code. |
/hindsight-docs |
Full Hindsight reference. API operations, SDK guides, configuration, deployment, cookbook recipes. Your agent searches these docs to answer specific questions or debug your integration. |
/hindsight-upgrade |
Version check and upgrade. Detects when a newer version of hindsight-skills is available and offers to install it. Runs automatically in the background; can also be invoked directly. |
git clone --depth 1 https://github.com/vectorize-io/hindsight-skills.git ~/hindsight-skills
cd ~/hindsight-skills && ./setupOr add to your repo so teammates get it:
git clone --depth 1 https://github.com/vectorize-io/hindsight-skills.git .claude/skills/hindsight-skills
cd .claude/skills/hindsight-skills && ./setupThese agents all follow the SKILL.md standard and discover skills from .agents/skills/ or ~/.codex/skills/.
Install to one repo:
git clone --depth 1 https://github.com/vectorize-io/hindsight-skills.git .agents/skills/hindsight-skills
cd .agents/skills/hindsight-skills && ./setup --host codexInstall globally:
git clone --depth 1 https://github.com/vectorize-io/hindsight-skills.git ~/hindsight-skills
cd ~/hindsight-skills && ./setup --host codexgit clone --depth 1 https://github.com/vectorize-io/hindsight-skills.git ~/hindsight-skills
cd ~/hindsight-skills && ./setup --host kirogit clone --depth 1 https://github.com/vectorize-io/hindsight-skills.git ~/hindsight-skills
cd ~/hindsight-skills && ./setup --host factoryIf you have multiple agents installed, setup will find and register with all of them:
git clone --depth 1 https://github.com/vectorize-io/hindsight-skills.git ~/hindsight-skills
cd ~/hindsight-skills && ./setup --host autonpx skills add vectorize-io/hindsight-skills --skill hindsight-architect
npx skills add vectorize-io/hindsight-skills --skill hindsight-docsThe architect skill isn't a generic template generator. It has deep knowledge of Hindsight internals and makes real architecture decisions:
Retain — Knows that document_id enables conversation upsert (same ID = replace + re-extract), that content over 3K chars is auto-chunked, that context guides extraction quality, and that you send full conversations, not deltas.
Recall — Understands the 4 parallel retrieval strategies (semantic, BM25, graph, temporal), how tags_match modes work (any includes untagged, any_strict excludes), and how to size token budgets for your use case.
Tags — Knows tags are for identity scoping (userId, customerId), not content classification. Designs tag schemas that enforce memory isolation and prevent cross-user data leakage.
Mental models — Understands that source_query determines what to synthesize, tags filter whose memories to analyze, and trigger: { refresh_after_consolidation: true } enables auto-refresh. Designs retrieval strategies so your application can find the right model at runtime.
Reflect — Knows this is an expensive agentic loop (up to 10 iterations), not a routine pre-response call. Recommends recall + direct mental model fetch for the pre-response pattern, and reflect only for complex disposition-influenced reasoning.
Deployment — Detects your stack (Python, Node.js, framework) and generates code for your specific setup: Hindsight Cloud, self-hosted, or embedded.
Skills not showing up? Re-run setup and restart your agent:
cd ~/hindsight-skills && ./setup # or --host codex, --host auto, etc.Slash commands don't autocomplete? Skills must be at ~/.claude/skills/{name}/SKILL.md (Claude Code), ~/.codex/skills/{name}/SKILL.md (Codex/Gemini/Cursor), ~/.kiro/skills/{name}/SKILL.md (Kiro), or ~/.factory/skills/{name}/SKILL.md (Factory Droid). The setup script handles this — run it again if something got out of sync.
Want to update? The /hindsight-upgrade skill checks automatically. To force-check or upgrade manually:
cd ~/hindsight-skills && git pull && ./setup --host auto- An AI coding agent: Claude Code, Codex, Gemini CLI, Cursor, Kiro, or Factory Droid
- Either:
- A Hindsight Cloud account — sign up at ui.hindsight.vectorize.io
- A Hindsight self-hosted instance.
MIT. Free and open source.