Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/wyattowalsh/agentsnpx agentmods add skills/wyattowalsh/agents/prompt-engineerWrote 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/wyattowalsh/agents/prompt-engineer)<a href="https://agentmods.dev/skills/wyattowalsh/agents/prompt-engineer"><img src="https://agentmods.dev/badge/skills/wyattowalsh/agents/prompt-engineer/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/wyattowalsh/agents/prompt-engineer"><img src="https://agentmods.dev/badge/skills/wyattowalsh/agents/prompt-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 9 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00048 | $0.05537 |
| Opus 5.5 | $0.00019 | $0.02215 |
| Sonnet 5.5 | $0.00010 | $0.01107 |
| Haiku 4.5 | $0.00005 | $0.00554 |
Grade A, and why
prompt-engineer 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 1mo 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 — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer
Comprehensive prompt and context engineering. Every non-obvious recommendation must be evidence-scoped.
Canonical Vocabulary
Use these terms exactly throughout all modes:
| Term | Definition |
|---|---|
| system prompt | The top-level instruction block sent before user messages; sets model behavior |
| context window | The full token budget: system prompt + conversation history + tool results + retrieved docs |
| context engineering | Designing the entire context window, not just the prompt text — write, select, compress, isolate |
| template | A reusable prompt structure with variable slots ({{input}}, named placeholders, or runtime arguments) |
| rubric | A scoring framework with dimensions, levels (1-5), and concrete examples per level |
| few-shot example | An input/output pair included in the prompt to demonstrate desired behavior |
| chain-of-thought (CoT) | Explicit step-by-step reasoning scaffolding; beneficial for instruction-following models, harmful for reasoning models |
| model class | Either "instruction-following" or "reasoning" — determines which techniques apply |
| injection | Untrusted input that manipulates model behavior outside intended boundaries |
| anti-pattern | A prompt construction that reliably degrades output quality |
| over-specification | Adding constraints past the point where they help; too much specificity can degrade output quality and flexibility |
| scorecard | The 5-dimension diagnostic (Clarity, Completeness, Efficiency, Robustness, Model Fit) scored 1-5 |
| playbook | Model-family-specific guidance document in references/model-playbooks.md |
| prefix caching | Cost optimization by placing static content early so API providers cache the prefix |
| trust boundary | The separation between trusted instructions and untrusted user, tool, retrieved, or external content |
| evidence class | Source type behind a recommendation: official docs, peer-reviewed/preprint, community heuristic, local practice, or single-study |
| PromptOps | Versioning, evals, rollout checks, observability, and rollback discipline for prompts in production |
What ships with it
18 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/evals.json 16 KB
- references/anti-patterns.md 12 KB
- references/architecture-patterns.md 14 KB
- references/context-management.md 10 KB
- references/evaluation-frameworks.md 7.6 KB
- references/hardening-checklist.md 10 KB
- references/model-playbooks.md 15 KB
- references/output-formats.md 9.9 KB
- references/technique-catalog.md 14 KB
- scripts/asset_toolkit/__init__.py 303 B runs code
- scripts/asset_toolkit/_shared.py 5.6 KB runs code
- scripts/asset_toolkit/common.py 3.0 KB runs code
- scripts/asset_toolkit/package.py 43 KB runs code
- scripts/asset_toolkit/validate_evals.py 11 KB runs code
- scripts/asset_toolkit/validate_hooks.py 13 KB runs code
- scripts/asset_toolkit/validate_skill.py 3.6 KB runs code
- scripts/check.py 2.2 KB runs code
- scripts/validate-references.py 8.4 KB runs code
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.
- 1mo ago First seen · 387 lines · 48 tokens per session scan A e65d3b10979d
prompt-engineer is a skill published in the GitHub repository wyattowalsh/agents (5 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 5,537 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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