context-engineering
Skill1 starsChanged 17 days agoMITView on GitHub
Design context selection, packing, isolation, and compaction for LLM applications and agents when token limits, stale history, or noisy retrieval degrade results.
We split this skill into the rules it gives an agent
7 rules. 0 are common, 7 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 113 edited versions we read keep these.
7
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 15
Choose a context strategy: Use retrieval for large knowledge collections, summaries for long histories, structured state for workflow …
- Distinctive: line 16
Preserve provenance: Keep source identifiers, timestamps, access scope, and enough surrounding evidence to verify important claims.
- Distinctive: line 19
Evaluate the assembled context: Test whether needed facts survive packing and whether irrelevant or malicious content can alter behavior.
- Distinctive: line 23
Normalize: remove boilerplate, preserve structure, and record metadata.
- Distinctive: line 26
Use: ask the model to cite or reference evidence where appropriate.
- Distinctive: line 28
Expire: remove stale, sensitive, or no-longer-authorized context.
- Distinctive: line 42
Document context sources, trust boundaries, ranking/packing rules, token budgets, retention policy, and tests for omissions, stale facts, …
“Common” is measured: kept by at least 6 in 10 of the 113 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Context Engineering
Goal
Supply the model with the smallest, highest-value, correctly scoped context needed for the current decision. Context engineering covers selection and lifecycle, not only prompt wording.
Workflow
- Map context sources. Separate system/developer policy, current user request, conversation state, retrieved evidence, tool results, persistent memory, and generated intermediate artifacts.
- Define trust labels. Mark untrusted user content, web pages, retrieved files, and tool outputs as data. Never promote their embedded instructions into trusted policy.
- Prioritize by utility. Include task requirements, relevant facts, constraints, and evidence first. Remove unrelated history, duplicate passages, verbose tool output, and obsolete state.
- Choose a context strategy. Use retrieval for large knowledge collections, summaries for long histories, structured state for workflow facts, and references/files for details that need not be in every model call.
- Preserve provenance. Keep source identifiers, timestamps, access scope, and enough surrounding evidence to verify important claims.
- Control context growth. Set budgets for history, retrieved chunks, tool output, and intermediate notes. Truncate or summarize deliberately, not silently.
- Prevent cross-task leakage. Scope context by user, tenant, project, task, and authorization. Do not reuse another user's private state.
- Evaluate the assembled context. Test whether needed facts survive packing and whether irrelevant or malicious content can alter behavior.
Context lifecycle
- Collect: fetch only sources authorized for this request.
- Normalize: remove boilerplate, preserve structure, and record metadata.
- Rank: order by relevance, freshness, trust, and task-specific utility.
- Pack: fit a token budget with room for the response and tool calls.
- Use: ask the model to cite or reference evidence where appropriate.
- Update: persist only validated state that will be useful later.
- Expire: remove stale, sensitive, or no-longer-authorized context.
Common failure modes
- Lost constraints after summarization.
- Too much low-quality retrieval diluting relevant evidence.
- Stale memory treated as current truth.
- Prompt injection hidden in retrieved text or tool output.
- Unbounded tool output consuming the context window.
- Context assembled across tenants or permission boundaries.
Evaluation
Track task success against context budget, evidence coverage, retrieval noise, truncation rate, and latency/cost. Create adversarial tests where malicious instructions appear in otherwise relevant documents. Compare full-history, summarized-history, and structured-state approaches on the same cases.
Deliverable
Document context sources, trust boundaries, ranking/packing rules, token budgets, retention policy, and tests for omissions, stale facts, injection, and isolation.
References
- Microsoft AI Agents for Beginners, context engineering: https://github.com/microsoft/ai-agents-for-beginners
- LangGraph persistence and memory: https://docs.langchain.com/oss/python/langgraph/add-memory
- Anthropic context engineering overview: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
Other versions126 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 126
- addyosmani/agent-skills★ 112ksame name
- bojieli/ai-agent-book-projects★ 53ksame name
- bojieli/ai-agent-book★ 53ksame name
- rohitg00/pro-workflow★ 2.9ksame name
- mrgoonie/claudekit-skills★ 2.2ksame name
- kdlbs/kandev★ 1ksame name
- liza-mas/liza★ 414same name
- typv/nest-turbo-starter★ 224same name
- VAMFI/claude-user-memory★ 206same name
- shashankswe2020-ux/whoop-mcp★ 174same name
116 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
Used alongsideProjects with it also use using-agent-skills, idea-refine and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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idea-refine
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MrCipherSmithSkillconstraint-driven-development
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HK-hubSkilldeprecation-and-migration
Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when deciding whether to maintain or sunset existing code.
Also in 39 projects that use this
- HK-hubSkill
source-driven-development
Grounds every implementation decision in official documentation. Use when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters.
Also in 43 projects that use this
- +7 more from LLl0k0laD
In this repository28 other files it gives its agents.
- agent-architecture-designSkill
- agent-evaluationSkill
- agent-harness-engineeringSkill
- agentic-workflow-testingSkill
- agent-memory-designSkill
- browser-computer-useSkill
- durable-agent-workflowsSkill
- human-in-the-loopSkill
- mcp-developmentSkill
- mcp-server-developmentSkill
- multi-agent-orchestrationSkill
- tool-calling-designSkill
- advanced-retrieval-rerankingSkill
- embedding-model-engineeringSkill
- llm-evaluationSkill
- llm-observabilitySkill
- model-serving-inferenceSkill
- multimodal-ai-pipelinesSkill
- prompt-engineeringSkill
- rag-engineeringSkill
- rag-pipeline-engineeringSkill
- structured-outputsSkill
- agentic-securitySkill
- security-reviewSkill
- code-reviewSkill
- debuggingSkill
- test-engineeringSkill
- example-skillSkill
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