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 skills add selvarajmurugesan90/ops-engineering-skills --skill prompt-and-context-engineeringgit clone --depth 1 https://github.com/selvarajmurugesan90/ops-engineering-skillsWrote 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/skills/selvarajmurugesan90/ops-engineering-skills/prompt-and-context-engineering)<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/prompt-and-context-engineering"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/prompt-and-context-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/prompt-and-context-engineering"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/prompt-and-context-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00093 | $0.02671 |
| Opus 5 | $0.00046 | $0.01336 |
| Sonnet 5 | $0.00019 | $0.00534 |
| Haiku 4.5 | $0.00009 | $0.00267 |
Grade A, and why
prompt-and-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 11d 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt and Context Engineering
Purpose
Everything an LLM "knows" during a single call is whatever text is in its context window at that moment — there is no other channel. Prompt and context engineering is the discipline of deciding what goes into that window, in what order, in what format, and how it's kept from growing without bound as an agent runs. Done poorly, this produces agents that ignore instructions, contradict themselves across turns, burn tokens (and money) on irrelevant history, or become unpredictable as conversations grow long. Done well, it is what makes an agent's behavior consistent, debuggable, and affordable to run at scale. This is distinct from model selection or fine-tuning: it's about structuring information for a fixed model, which is usually the highest-leverage, lowest-cost lever available.
When to use
- Writing or revising a system prompt for an agent, especially one with multiple instructions, tools, or output-format requirements.
- The agent's behavior is inconsistent, ignores stated rules, or drifts as a conversation gets longer.
- Deciding what belongs in the system prompt vs. a per-turn user message vs. a tool result vs. retrieved (RAG) content.
- Reducing token usage / latency / cost on a working agent (also see llm-cost-and-latency-optimization).
- Designing how conversation history is truncated, summarized, or windowed for a long-running session.
- Debugging why the model's output format doesn't match what was requested.
Prerequisites & environment
- Know your target model's context window size and, ideally, its documented behavior around very long contexts (many models show degraded attention to middle-of-context content, sometimes called "lost in the middle" — verify current behavior for your specific model rather than assuming a fixed rule).
- Access to token-counting tooling for your model/SDK so budgets are measured, not guessed.
- A test harness or even a handful of representative transcripts you can re-run after each prompt change — prompt engineering without a way to check for regressions is guesswork (see agent-evaluation-and-guardrails).
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.
- 11d ago First seen · 260 lines · 93 tokens per session scan A f51ebd22af27
prompt-and-context-engineering is a skill published in the GitHub repository selvarajmurugesan90/ops-engineering-skills (38 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 93 tokens to every session and 2,671 once invoked, about $0.0005 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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