| 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)
Progressive disclosure only
- Do NOT load every skill into context.
- Start with a directory scan + short descriptions, then load 1–3 relevant
skills’
SKILL.mdonly when needed.
Atomic PR surfaces
- Prefer changes that touch a single coherent file surface.
- If multiple surfaces are needed, split into separate PR prompts.
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)
List candidate skills:
- Look in:
skills/vendor/agent-skills-context-engineering/skills/ - Identify 3–7 likely skill folders by name.
- Look in:
Read only metadata first:
- For each candidate folder, open the first ~60 lines of
SKILL.md(YAML + intro) to get name/description.
- For each candidate folder, open the first ~60 lines of
Select + load:
- Choose the best 1–3 skills.
- Load their
SKILL.mdfully. - Apply them to the current task.
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)