Skills context-manager
📦

context-manager

Content revision r2 Safe ⚙️ External commands

Design Reliable AI Context Systems

AI applications lose relevance when context, memory, and retrieval systems are poorly designed. This skill guides context architecture, RAG, knowledge graphs, and multi-agent state management.

Supports: Claude Codex Code(CC)
🥉 78 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "context-manager" from https://skillstore.io/skills/sickn33-context-manager.md and its manifest at https://skillstore.io/api/skills/sickn33-context-manager/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/sickn33-context-manager.md Signed manifest GET /api/skills/sickn33-context-manager/manifest Signed lockfile GET /api/skills/sickn33-context-manager/lockfile

Test it

Using "context-manager". Design context management for a customer support assistant with long-running cases.

Expected outcome:

Use a compact working summary, retrieve case evidence on demand, store approved durable facts, and log every agent handoff.

Using "context-manager". Improve retrieval quality for a large technical document collection.

Expected outcome:

Test structure-aware chunks, combine semantic and keyword retrieval, rerank candidates, filter stale versions, and measure grounded answer quality.

Using "context-manager". Reduce token use across a multi-agent workflow.

Expected outcome:

Assign context ownership, pass task-specific summaries, retrieve details only when needed, and track token cost per successful workflow.

Security Audit

Safe
v5 • 7/23/2026 Open versioned report

Both static findings are false positives. The cited lines contain a Markdown file reference and a hybrid-search capability statement, without shell execution or system reconnaissance. No semantic security issue was found.

1
Files scanned
188
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

⚙️ External commands (1)
SKILL.md:24
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
Share & cite this report

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Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). context-manager security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-context-manager/audits/5

BibTeX citation

@techreport{sickn33-sickn33-context-manager-2026, author = {sickn33}, title = {context-manager security audit report (audit version 5)}, institution = {Skillstore}, year = {2026}, number = {5}, url = {https://skillstore.io/skills/sickn33-context-manager/audits/5}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "context-manager security audit report (audit version 5)" version: "unspecified" type: report authors: - name: "sickn33" date-released: "2026-07-23" url: "https://skillstore.io/skills/sickn33-context-manager/audits/5" identifiers: - type: other value: "skillstore:sickn33-context-manager:audit:5" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
85
Maintainability
87
Content
69
Community
83
Spec Compliance

What You Can Build

Plan a Support Context Architecture

Design retrieval, memory, and agent handoffs for consistent responses across long-running customer cases.

Improve Enterprise Document Retrieval

Select chunking, embedding, hybrid search, ranking, and freshness strategies for a large internal document collection.

Control Multi-Agent Workflow State

Define context ownership, routing, handoff, and recovery rules for a coordinated AI workflow.

Try These Prompts

Assess Basic Context Needs 
Review this AI workflow: [workflow]. Identify required context, missing inputs, token constraints, and a simple assembly strategy.
Design a Retrieval Pipeline 
Design a RAG pipeline for [corpus]. Recommend chunking, embeddings, retrieval, ranking, freshness checks, and evaluation metrics.
Plan Long-Term Memory 
Create a memory strategy for [application]. Separate working, episodic, semantic, and persistent memory with retention and retrieval rules.
Architect Multi-Agent Context 
Architect context management for [agents and workflow]. Define ownership, handoffs, isolation, token budgets, failure recovery, governance, and measurable service targets.

Best Practices

  • Define measurable relevance, latency, cost, freshness, and privacy requirements before selecting components.
  • Separate durable facts, conversation history, retrieved evidence, and temporary working context.
  • Evaluate retrieval and final responses with representative tasks, failure cases, and stale data.

Avoid

  • Do not place every available document into each prompt.
  • Do not choose an embedding model or vector database without measured requirements.
  • Do not store sensitive conversation history without retention, access, and deletion controls.

Frequently Asked Questions

Does this skill deploy infrastructure?
No. It provides design guidance and validation steps but does not deploy databases, models, APIs, or monitoring systems.
Which vector databases does it cover?
It discusses Pinecone, Weaviate, and Qdrant, with selection based on scale, latency, governance, and operational requirements.
Can it improve an existing RAG system?
Yes. It can assess chunking, retrieval, ranking, freshness, quality metrics, latency, and cost.
Does it address privacy and governance?
Yes. It covers isolation, retention, audit trails, and privacy-aware architecture, but regulated deployments still require qualified review.
What information should I provide?
Provide users, workflows, data sources, scale, latency, token limits, privacy constraints, current architecture, and success metrics.
Are implementation files included?
No. The skill references an implementation playbook, but that file is not included in the reviewed package.

Developer Details

Author

sickn33

License

MIT

Skillstore revision

r2

Version notice

The author did not declare a version.

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/context-manager

Ref

f9e2c34b4f19c7f3e6b0a1e93227b5f77cc12526

Maintenance freshness

7/26/2026

Usage

17 downloads · 143 views

File structure

📄 SKILL.md

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