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context-engineering-pack

@BrianCLong/summit
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SKILL.md

name context-engineering-pack
version 1.0.0
description Summit/IntelGraph context-engineering skill pack. Routes to the vendored Agent Skills for Context Engineering library via progressive disclosure, and applies repo-specific guardrails (atomic PRs, evidence, governance).
tags context-engineering, skills-routing, long-horizon, ga-readiness
license MIT
dependencies skills/vendor/agent-skills-context-engineering

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)