Is context-engineering safe to install?

SaferSkills independently audited context-engineering (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

dot_cursor/skills/context-engineering/SKILL.md · 1 file

dot_cursor/skills/context-engineering/SKILL.md3.9 KB · Markdown

name: context-engineering

description: >-

Master context engineering for AI agent systems. Use when designing agent architectures,

debugging context failures, optimizing token usage, implementing memory systems,

building multi-agent coordination, evaluating agent performance, or developing

LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization

techniques (compaction, masking, caching), compression strategies, memory architectures,

multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.

version: 1.0.0

Context Engineering

Context engineering curates the smallest high-signal token set for LLM tasks. The goal: maximize reasoning quality while minimizing token usage.

When to Activate

Designing/debugging agent systems
Context limits constrain performance
Optimizing cost/latency
Building multi-agent coordination
Implementing memory systems
Evaluating agent performance
Developing LLM-powered pipelines

Core Principles

Context quality > quantity - High-signal tokens beat exhaustive content
Attention is finite - U-shaped curve favors beginning/end positions
Progressive disclosure - Load information just-in-time
Isolation prevents degradation - Partition work across sub-agents
Measure before optimizing - Know your baseline

Quick Reference

Topic	When to Use	Reference
Fundamentals	Understanding context anatomy, attention mechanics	context-fundamentals.md
Degradation	Debugging failures, lost-in-middle, poisoning	context-degradation.md
Optimization	Compaction, masking, caching, partitioning	context-optimization.md
Compression	Long sessions, summarization strategies	context-compression.md
Memory	Cross-session persistence, knowledge graphs	memory-systems.md
Multi-Agent	Coordination patterns, context isolation	multi-agent-patterns.md
Evaluation	Testing agents, LLM-as-Judge, metrics	evaluation.md
Tool Design	Tool consolidation, description engineering	tool-design.md
Pipelines	Project development, batch processing	project-development.md

Key Metrics

Token utilization: Warning at 70%, trigger optimization at 80%
Token variance: Explains 80% of agent performance variance
Multi-agent cost: ~15x single agent baseline
Compaction target: 50-70% reduction, <5% quality loss
Cache hit target: 70%+ for stable workloads

Four-Bucket Strategy

Write: Save context externally (scratchpads, files)
Select: Pull only relevant context (retrieval, filtering)
Compress: Reduce tokens while preserving info (summarization)
Isolate: Split across sub-agents (partitioning)

Anti-Patterns

Exhaustive context over curated context
Critical info in middle positions
No compaction triggers before limits
Single agent for parallelizable tasks
Tools without clear descriptions

Guidelines

Place critical info at beginning/end of context
Implement compaction at 70-80% utilization
Use sub-agents for context isolation, not role-play
Design tools with 4-question framework (what, when, inputs, returns)
Optimize for tokens-per-task, not tokens-per-request
Validate with probe-based evaluation
Monitor KV-cache hit rates in production
Start minimal, add complexity only when proven necessary

Scripts

context_analyzer.py - Context health analysis, degradation detection
compression_evaluator.py - Compression quality evaluation

context-engineering— agent skill

Is context-engineering safe to install?

Context Engineering

When to Activate

Core Principles

Quick Reference

Key Metrics

Four-Bucket Strategy

Anti-Patterns

Guidelines

Scripts

Audit the pieces. Scan the whole. Decide.