LaunchKit · 2026
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hyperstack

The Agent Provenance Graph for AI agents — the only memory layer where agents can prove what they knew, trace why.

1
1.4k downloads
by @deeqyaqub1-cmd

Setup & Installation

openclaw skills install @deeqyaqub1-cmd/hyperstack

Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:

npx clawhub install hyperstack

Version History

v1.0.26Feb 22, 2026 - Version note by skill developer:

v1.0.26 - Security & Trust Hardening Input Trust Boundaries — Added explicit prompt injection guardrails. Stored card content is now declared as DATA not instructions. Rules added for slug validation, raw content forwarding, and injection pattern detection. Permissions Manifest — Added formal permissions declaration (exec: false, filesystem: none, network scope, env vars) so the skill passes OpenClaw's security classification without ambiguity. Supply Chain Fix — Replaced unpinned npx --yes hyperstack-mcp with version-pinned hyperstack-mcp@1.10.1 across all install configs. Added local install guidance and integrity verification command. Data Safety Promoted — Data safety warning elevated from a footer note to a named security section in both SKILL.md and README.md, making it visible to scanners and reviewers before tool documentation. Version bump — _meta.json updated to 1.0.25.

What This Skill Does

HyperStack is a typed graph memory layer for AI agents that stores facts, decisions, and their relationships with full provenance. Agents can query what breaks if a card changes, replay the exact context that existed when a decision was made, branch memory like a Git repo, and coordinate across agents via typed signals. Works via MCP, JavaScript/Python SDKs, or REST.

Stores decisions as a traversable graph rather than flat markdown logs, so agents can query impact, blockers, and provenance without parsing text or calling an LLM.

When to use it

  • Tracing which services break when an auth dependency changes
  • Checking blockers before deploying to production
  • Replaying what an agent knew at the moment it made a specific decision
  • Branching agent memory to test a new approach without affecting live state
  • Coordinating multiple agents with typed signals and trust scoring

Example Workflow

Here's how your AI assistant might use this skill in practice.

INPUT

User asks: What breaks if we change the auth system?

AGENT
  1. 1Call hs_identify to register agent identity for provenance tracking
  2. 2Call hs_smart_search with query 'what depends on auth?' to auto-route to impact mode
  3. 3Call hs_impact with slug 'use-clerk' to find all dependent cards
  4. 4Return the typed dependency list with relation labels
OUTPUT

[auth-api] API Service via triggers, [billing-v2] Billing v2 via depends-on, [deploy-prod] Production Deploy via blocks

Requirements

Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.

HYPERSTACK_API_KEY environment variableHYPERSTACK_WORKSPACE environment variable