Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/manhvann/codexkit/context-engineeringnpx skills add manhvann/codexkit --skill context-engineeringgit clone --depth 1 https://github.com/manhvann/codexkitWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00045 | $0.01032 |
| Opus 5 | $0.00023 | $0.00516 |
| Sonnet 5 | $0.00009 | $0.00206 |
| Haiku 4.5 | $0.00005 | $0.00103 |
Grade A, and why
ck:context-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- context-engineering — 92% identical, 36 lines differ
- context-engineering — 92% identical, 36 lines differ
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
IMPORTANT:
- Sacrifice grammar for the sake of concision.
- Ensure token efficiency while maintaining high quality.
- Pass these rules to subagents.
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 |
| Runtime Awareness | Usage limits, context window monitoring | runtime-awareness.md |
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/context-compression.md 2.3 KB
- references/context-degradation.md 3.0 KB
- references/context-fundamentals.md 2.7 KB
- references/context-optimization.md 2.3 KB
- references/evaluation.md 2.2 KB
- references/memory-systems.md 2.5 KB
- references/multi-agent-patterns.md 2.3 KB
- references/project-development.md 2.1 KB
- references/runtime-awareness.md 4.8 KB
- references/tool-design.md 2.0 KB
- scripts/compression_evaluator.py 11 KB runs code
- scripts/context_analyzer.py 11 KB runs code
- scripts/tests/test_edge_cases.py 9.4 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 109 lines · 45 tokens per session scan A 868d862888ad
ck:context-engineering is a skill published in the GitHub repository manhvann/codexkit (88 stars, last pushed 3d ago), licensed MIT. It adds 45 tokens to every session and 1,032 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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