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/apexiq/skillsmith/context_engineeringnpx skills add ApexIQ/skillsmith --skill context_engineeringgit clone --depth 1 https://github.com/ApexIQ/skillsmithWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/apexiq/skillsmith/context_engineering)<a href="https://agentmods.dev/skills/apexiq/skillsmith/context_engineering"><img src="https://agentmods.dev/badge/skills/apexiq/skillsmith/context_engineering.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00025 | $0.00811 |
| Opus 5 | $0.00013 | $0.00405 |
| Sonnet 5 | $0.00005 | $0.00162 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🎯 Context Engineering
Philosophy: The right context at the right time produces the best results.
The Problem
AI models have limited context windows:
- Too little context → Hallucinations, wrong assumptions
- Too much context → Quality degradation, high costs
- Wrong context → Irrelevant or incorrect output
Context Window Management
Know Your Limits
| Model | Approx Context | Practical Limit |
|---|---|---|
| GPT-4 | 128k tokens | ~80k for best quality |
| Claude 3.5 | 200k tokens | ~150k for best quality |
| Gemini 2.0 | 1M+ tokens | ~500k for best quality |
Rule of thumb: Stay at 50-70% of max for optimal performance.
Context Priority Stack
Order context by importance:
1. CRITICAL: Current task instructions
2. HIGH: Directly relevant code/files
3. MEDIUM: Related patterns/examples
4. LOW: General project context
5. OPTIONAL: Historical context
Strategies
1. Progressive Disclosure
Start minimal, add context as needed:
Step 1: Give task + key file
Step 2: Agent asks for more context
Step 3: Provide specific additional files
Step 4: Agent completes task
2. Context Windowing
Show only relevant sections:
# ❌ Don't: Load entire 5000-line file
file_content = read_file("huge_file.py")
# ✅ Do: Load only relevant section
file_content = read_file("huge_file.py", start=100, end=200)
3. Semantic Chunking
Include related code, not arbitrary ranges:
Good chunk: Entire function + its imports
Bad chunk: Lines 100-200 (arbitrary)
4. Context Prefetching
Anticipate needs based on task type:
| Task Type | Prefetch |
|---|---|
| Bug fix | Error logs, related tests, recent changes |
| New feature | Similar features, API patterns, tests |
| Refactor | Callers, tests, type definitions |
Context Organization
Structure context for clarity:
<current_file>
[The file being edited]
</current_file>
<related_files>
[Files that import/use current_file]
</related_files>
<tests>
[Test files for current functionality]
</tests>
<documentation>
[API docs, READMEs relevant to task]
</documentation>
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.
- 4d ago First seen · 142 lines · 25 tokens per session scan A 8a36bc2a6498
context-engineering is a skill published in the GitHub repository ApexIQ/skillsmith (5 stars, last pushed 5mo ago), licensed MIT. It adds 25 tokens to every session and 811 once invoked, about $0.0001 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 2026-08-31.
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semantic-search
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init
Initialize structured context for this project. Scaffolds .scs/ with all 11 concern bundles, scans the project, and recommends where to start.
auto-context
智能上下文检测 - 自动识别AI所需的文件、函数和上下文，优化token使用.
context-engineering
Use this skill to systematically manage what information goes into an LLM context window — selecting, compressing, and prioritizing content to maximize response quality within token budget constraints. Activates when building or optimizing LLM applications, RAG systems, or agent workflows that face context window…
Context Manager
Manage conversation context and memory for optimal AI performance.