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 xobotyi/cc-foundry --skill prompt-engineeringgit clone --depth 1 https://github.com/xobotyi/cc-foundryWrote 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/xobotyi/cc-foundry/prompt-engineering)<a href="https://agentmods.dev/skills/xobotyi/cc-foundry/prompt-engineering"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/prompt-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/xobotyi/cc-foundry/prompt-engineering"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/prompt-engineering.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 Memory Poisoning · line 225 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
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 | $0.00037 | $0.05658 |
| Opus 5.5 | $0.00015 | $0.02263 |
| Sonnet 5.5 | $0.00007 | $0.01132 |
| Haiku 4.5 | $0.00004 | $0.00566 |
Grade A, and why
prompt-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 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 — 356 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A model that reasons natively does not need to be taught how to think. It needs to be told what done looks like, and left alone. Instruction text that explains, encourages, or supervises costs more than it buys — the model already does those things, and your text competes with its own judgment.
Prompt text lives on four surfaces, and the same words behave differently on each. A rule that helps in a request can tax every turn for months as persistent context. Identify the surface before writing.
On encountering a defective prompt
Do not read past it. Prompt text that violates these rules is found far more often than it is written — in a skill, a tool description, a standing instruction file, a subagent brief.
- Already editing that file — fix it, in the same change.
- Not editing that file — name the defect and the fix, once, in a sentence. Then continue the task you were asked to do.
- Flag what changes behavior, not what offends taste. A stale fact, a contradiction between two rules, a filter that suppresses recall, a rule the model already follows — these cost output. Wording preferences do not.
- One flag, then proceed. A task does not become a prompt audit because a prompt was involved. Raising the same class of defect repeatedly in one session costs more attention than the defect does.
Not a prompt problem
Write nothing for these. Each has a fix that prompt text cannot reach, and reaching for words instead is the most common wasted motion in this skill's domain.
- Reasoning is too shallow or too deep → the effort setting, not the wording
- Context degrades across a multi-turn run → compaction and reasoning persistence. This is not the same as a single prompt carrying large documents, which is prompt text — see Long context
- A prohibition must actually hold → permissions, schemas, sandboxes. Prose steers; it does not enforce
- The rule is deterministic and machine-checkable → compile it into code and have the model call it
- Deterministic filtering or aggregation over tool output → run it as code outside the context window
What ships with it
7 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.
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 · 356 lines · 37 tokens per session scan A a39f24eb675f
prompt-engineering is a skill published in the GitHub repository xobotyi/cc-foundry (22 stars, last pushed 7d ago), licensed MIT. It adds 37 tokens to every session and 5,658 once invoked, about $0.0001 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 2026-08-30.
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