Borrowing it
Nothing to install: this file belongs to opencue/cuecards. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/opencue/cuecards/main/.agents/skills/vc-context-engineering/SKILL.mdgit clone --depth 1 https://github.com/opencue/cuecardsWrote 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/opencue/cuecards/vc-context-engineering)<a href="https://agentmods.dev/skills/opencue/cuecards/vc-context-engineering"><img src="https://agentmods.dev/badge/skills/opencue/cuecards/vc-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 5.1 | $0.00038 | $0.01037 |
| Opus 5 | $0.00019 | $0.00518 |
| Sonnet 5 | $0.00008 | $0.00207 |
| Haiku 4.5 | $0.00004 | $0.00104 |
Grade A, and why
vc: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 3d 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 — 111 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.
- 3d ago First seen · 111 lines · 38 tokens per session scan A 952c4e456d0c
vc:context-engineering is a skill published in the GitHub repository opencue/cuecards (5 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 1,037 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 2026-09-03.
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atomic-wiki
Conversational wiki and capture-bucket routing. Fires when the user wants a place, space, or folder for notes, research, tickets, raw dumps, or knowledge capture — checks the block in /.claude/CLAUDE.md; if the cwd is under a registered realm, creates the folder as a bucket via atomic wiki bucket add rather than a…
next
Produce a clean handoff to a fresh Claude Code window. Use when the current conversation is too long (>400K tokens, hallucinations, lost context, or the user wants to close the window). "/next" produces a handoff, "/next list" shows all pending, "/next remove X" deletes one.
moai-foundation-context
Manages context window optimization, session state persistence, and token budget allocation for multi-agent workflows. Use for token budget management, context limits, or session handoff across agents.
durable-session-state
Persist plans, scope decisions, evidence, and reviewer/critic verdicts to durable files during long or multi-phase tasks so work survives context compaction, session resumes, and handoffs. Use for swarm-mode tasks, before context grows large, when recording approval gates, and when resuming after compaction or a…
token-optimization
Use when the user says 'token optimization', 'save tokens', 'context window', 'reduce tokens', 'token stack', or 'TokenStack', or asks about extending context window capacity. Covers TokenStack, the built-in compression proxy that shrinks Claude Code tool output before it reaches the Anthropic API. Do NOT use for…
state
Use when the user says 'update state', 'project state', 'where was I', or at session start to load current context.