Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/d-padmanabhan/agent-engineering-handbook/015-context-engineeringgit clone --depth 1 https://github.com/d-padmanabhan/agent-engineering-handbookWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/d-padmanabhan/agent-engineering-handbook/015-context-engineering)<a href="https://agentmods.dev/rules/d-padmanabhan/agent-engineering-handbook/015-context-engineering"><img src="https://agentmods.dev/badge/rules/d-padmanabhan/agent-engineering-handbook/015-context-engineering.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00815 | $0.00815 |
| Opus 5 | $0.00407 | $0.00407 |
| Sonnet 5 | $0.00163 | $0.00163 |
| Haiku 4.5 | $0.00081 | $0.00081 |
Grade A, and why
015-context-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering for Coding Agents
This rule strengthens agent performance by making context intentional, minimal, and reproducible.
[!NOTE] This rule complements
010-workflow.mdc(phases + context files) and100-core.mdc(anti-slop guardrails).
Goals
- Keep the agent’s working context small and high-signal
- Make work resumable across sessions without “reloading the world”
- Prefer retrieval over memory: fetch exactly what you need, when you need it
Mental Model: Three Layers of Context
- Working context (volatile): what the model sees right now
- Repo context (retrievable): files, diffs, logs, docs that can be re-read on demand
- Durable context (portable): short summaries + decisions captured in
tmp/context files
[!IMPORTANT] If it’s important and will matter later, it must be captured in durable context.
Prompt Packing (What to Carry vs. What to Fetch)
Always carry (paste/recap)
- Goal: one sentence
- Non-goals: 1-3 bullets
- Constraints: security, tooling, performance, “don’t refactor”, etc.
- Current plan: 3-7 steps max
- Current state: what’s done / what’s blocked
- Critical artifacts: exact file paths, key identifiers, and error messages (verbatim)
Never carry (fetch instead)
- Entire files or long logs “just in case”
- Repeated restatements of requirements
- Unbounded command output without a reason
Retrieval Strategy (Search → Read Narrow → Cite)
Use a tight loop:
- Search for the smallest set of candidate files (prefer repo-local sources first)
- Read only the minimal sections needed to decide or implement
- Cite exact file paths / relevant snippets in your reasoning and updates
[!TIP] Prefer “retrieve just-in-time” over “load everything up-front”. It is faster and more accurate.
Compaction Strategy (Frequent Intentional Compaction)
After any meaningful milestone (plan approved, a module completed, a bug fixed), compact:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 116 lines · 815 tokens per session scan A a91e6d1f4384
015-context-engineering is a cursor rule published in the GitHub repository d-padmanabhan/agent-engineering-handbook (16 stars, last pushed 4d ago), licensed MIT. It adds 815 tokens to every session, about $0.0041 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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