Context Engineering Pack (Summit)

Purpose

This pack integrates the upstream “Agent Skills for Context Engineering” library as a vendored dependency and provides routing rules so agents load only the minimum necessary instructions.

Upstream location (vendored): skills/vendor/agent-skills-context-engineering/skills/

Operating Rules (Summit-specific)

1. •

   Progressive disclosure only

   • •Do NOT load every skill into context.
   • •Start with a directory scan + short descriptions, then load 1–3 relevant skills’ SKILL.md only when needed.
2. •

   Atomic PR surfaces

   • •Prefer changes that touch a single coherent file surface.
   • •If multiple surfaces are needed, split into separate PR prompts.
3. •

   Evidence-first for GA

   • •When a change affects release/CI/security: include tests, checks, and “how to verify” steps.

Routing Heuristics (when to consult upstream)

Use the upstream library when you see:

• •long-running tasks / “agent gets worse over time”
• •too much context / tool output spam / “lost in the middle”
• •multi-agent coordination issues
• •RAG/citations quality problems
• •evaluation needs (LLM-as-judge, pairwise evals, bias mitigation)

How to Load Upstream Skills (agent procedure)

1. •

   List candidate skills:

   • •Look in: skills/vendor/agent-skills-context-engineering/skills/
   • •Identify 3–7 likely skill folders by name.
2. •

   Read only metadata first:

   • •For each candidate folder, open the first ~60 lines of SKILL.md (YAML + intro) to get name/description.
3. •

   Select + load:

   • •Choose the best 1–3 skills.
   • •Load their SKILL.md fully.
   • •Apply them to the current task.
4. •

   If still failing:

   • •Add at most 1 additional skill.
   • •If context is bloated, summarize tool outputs and compress history.

Common “first picks”

• •context-fundamentals
• •context-degradation
• •context-optimization
• •multi-agent-architecture
• •evaluation / llm-as-judge (when judging outputs)