ai-context-engineer
Skill8 starsChanged 6 months agoMITView on GitHub
Guides context engineering for LLM systems—assembling prompts, budgeting tokens, prioritizing sources, compressing history, caching, structured context blocks, and debugging context-related failures (lost instructions, overflow, distraction). Use when designing what enters the model context each turn, optimizing cost/latency via context strategy, building context pipelines for agents, implementing summarization or compaction, or fixing "model ignored X" issues—not for persistent memory store design (ai-memory-developer), full RAG index pipelines (ai-engineer), or AI org operations (ai-lead-ops). For a structured token/cost improvement roadmap with phased initiatives and KPIs, use ai-token-improvement-plan-engineer. For commercial/enterprise AI solution architecture (RAG, copilots, platform selection), use applied-ai-architect-commercial-enterprise. Token research and compression ablations: research-engineer-scientist-tokens.
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- rade-nugroho/Agentic-Enteprises-Skillword for word, first seen May 20, 2026
Other versions1 project uses a version of this.
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- rade-nugroho/Agentic-Enteprises-Skill★ 9100% same
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The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
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Distinctive · estimate
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1
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Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 62
Use explicit XML/markdown sections: <policy>, <tools>, <context>, <history>, <user>.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
AI Context Engineer
When to Use
- Designing what enters the model context each turn
- Optimizing cost/latency via context strategy and token budgeting
- Building context pipelines for agents (prefix, retrieval, history, user input)
- Implementing summarization, compaction, or rolling history
- Debugging context-related failures (lost instructions, overflow, distraction, ignored constraints)
- Choosing delimiters, XML blocks, or structured context formats
When NOT to Use
- Persistent memory store design or long-term recall architecture →
ai-memory-developer - Full RAG ingest/chunk/embed/index pipelines →
ai-engineer - AI org operations, release governance, or SLOs →
ai-lead-ops - Structured token/cost improvement roadmaps with phased KPIs →
ai-token-improvement-plan-engineer - Commercial/enterprise AI solution architecture →
applied-ai-architect-commercial-enterprise
Related skills
| Need | Skill |
| Memory stores and long-term recall | ai-memory-developer |
| RAG ingest/chunk/embed | ai-engineer |
| System and tool prompts | prompt-engineer |
| Red-team injection via context | ai-redteam |
| Cost and production SLAs | ai-lead-ops |
| Token reduction program and roadmap | ai-token-improvement-plan-engineer |
| Commercial/enterprise AI architecture | applied-ai-architect-commercial-enterprise |
| Token efficiency research and ablations | research-engineer-scientist-tokens |
Core Workflows
1. Context budget and layout
Allocate tokens (example 128k window):
| Block | Budget % | Priority |
| System policy + tools | 15–25% | Fixed, never truncated |
| Retrieved docs / memory | 30–45% | High, reranked |
| Conversation history | 25–40% | Compress oldest first |
| User current message | 5–10% | Never drop |
Use explicit XML/markdown sections: <policy>, <tools>, <context>, <history>, <user>.
See references/context_layout.md for templates and delimiter rules.
2. History management
| Strategy | When |
| Full recent window | Short chats, high-stakes instructions in last N turns |
| Rolling summary | Long sessions; summarize every K turns |
| Anchor messages | Pin system + key user constraints; summarize middle |
| Structured state | Replace chat with JSON task state for agents |
Preserve: user goals, constraints, unresolved tool errors, pending confirmations.
See references/history_compression.md for summarization prompts and pitfalls.
3. Retrieval into context
- Query from user message + state summary
- Retrieve candidates (RAG chunks, memories, tool outputs)
- Deduplicate overlapping passages
- Order by relevance; add source labels
- Truncate with sentence boundaries; show "[truncated]" when cut
See references/retrieval_packing.md for packing algorithms and citation format.
4. Caching and prefetch
- Cache stable prefix (system + tools) where provider supports prompt caching
- Prefetch retrieval while user types (optional)
- Invalidate cache on prompt version change
See references/caching_prefetch.md for provider notes and invalidation.
5. Debug context failures
| Symptom | Likely cause | Fix |
| Ignored instruction | Buried in middle / summarized away | Move to system or last user turn |
| Hallucinated doc | Weak retrieval | Raise threshold; require citation |
| Overflow error | No budget enforcement | Pre-flight token count; compress |
| Tool confusion | Ambiguous schemas in context | Separate tool block; shorten descriptions |
Log token counts per block in dev/staging.
See references/debugging_context.md for instrumentation checklist.
When to load references
- Layout and budgets →
references/context_layout.md - Summarization →
references/history_compression.md - RAG/memory packing →
references/retrieval_packing.md - Caching →
references/caching_prefetch.md - Debugging →
references/debugging_context.md
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