Context Engineering Memory Design
Use when designing or reviewing agent instructions, context surfaces, prompt/cache budget, project memory, compaction behavior, durable memory promotion, or stale fact handling.
Install
$ agentstack add skill-jukrap-ai-agent-playbook-context-engineering-memory-design ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-jukrap-ai-agent-playbook-context-engineering-memory-design) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Context Engineering Memory Design
Use this as the primary AI harness skill for context surfaces and durable project memory.
Workflow
- Classify each item as always-on instruction, project-local context, selected skill reference, generated runtime evidence, durable memory, handoff, or archive.
- Keep default context small, stable, and free of raw source lists, secrets, personal paths, branch names, PR numbers, and large excerpts.
- Treat runtime reports, indexes, dry-run output, and generated graphs as evidence candidates until reviewed and promoted.
- Verify context freshness with project docs, canon checks, worklogs, and explicit owner decisions when facts conflict.
Reference
Read references/context-surface-and-cache-budget.md for prompt/cache budget and context surface placement.
Read references/memory-promotion-and-staleness.md for generated evidence, durable memory, stale fact, and promotion checks.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: jukrap
- Source: jukrap/ai-agent-playbook
- License: MIT
- Homepage: https://www.npmjs.com/package/ai-agent-playbook
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.