Agent Skills for Context Engineering (muratcankoylan) — Prompts
Prompt File 1: context-fundamentals SKILL.md (verbatim excerpt)
Technique: Principle enumeration with explicit anti-examples and routing directives. The skill establishes conceptual ownership and then routes to other skills for operational work — creating a skill-to-skill handoff graph.
---
name: context-fundamentals
description: This skill should be used to explain or reason about the foundational concepts
of context engineering: what context is, the anatomy of a context window, how attention
mechanics work, the U-shaped attention curve, why context quality matters more than
quantity, and the mental models needed to interpret every other context-engineering
decision.
---
# Context Engineering Fundamentals
Context is the complete state available to a language model at inference time: system
instructions, tool definitions, retrieved documents, message history, and tool outputs.
Context engineering is the discipline of curating the smallest high-signal token set
that maximizes the likelihood of desired outcomes.
Apply four principles when assembling context:
1. **Informativity over exhaustiveness** — include only what matters for the current
decision; design systems that can retrieve additional information on demand.
2. **Position-aware placement** — place critical constraints at the beginning and end of
context because long-context evaluations show middle-position information is less
reliably recovered.
3. **Progressive disclosure** — load skill names and summaries at startup; load full
content only when a skill activates for a specific task.
4. **Iterative curation** — context engineering is not a one-time prompt-writing
exercise but an ongoing discipline applied every time content is passed to the model.
Prompting technique: Foundational-concept anchoring — the SKILL.md establishes definitions and first-principles before any operational guidance. This grounds all downstream skills in a shared vocabulary.
Prompt File 2: harness-engineering SKILL.md (verbatim excerpt)
Technique: Surface classification table — a structured four-class taxonomy for agent permissions with explicit rules.
### Harness Boundary
Separate the agent from the environment it operates inside. The agent proposes actions;
the harness defines allowed surfaces, feedback, persistence, and promotion rules.
Use four surface classes:
| Surface | Examples | Rule |
| --- | --- | --- |
| Locked | Eval metric, rubric, validation script, merge policy | Agent may read and propose changes, but cannot score itself with modified rules |
| Editable | Skill draft, experiment file, prompt, config under test | Agent may mutate during the loop |
| Append-only | Results log, research thread, rejected ideas | Agent may append, not rewrite |
| Human-controlled | Merge, production deploy, credentials, destructive operations | Requires explicit human approval |
### Tight Feedback Loops
Autonomy works when feedback is fast, unambiguous, and hard to game. Karpathy's
`autoresearch` is the minimal pattern: one editable file, one locked evaluation file,
fixed wall-clock budget, one scalar metric, git rollback, and a durable results log.
Prompting technique: Permission taxonomy with rule table — uses structured tabular format to make the permission model machine-readable and unambiguous. References Karpathy's autoresearch as a concrete anchor example.