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 cosmicstack-labs/mercury-agent-skills --skill prompt-engineeringgit clone --depth 1 https://github.com/cosmicstack-labs/mercury-agent-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/cosmicstack-labs/mercury-agent-skills/prompt-engineering)<a href="https://agentmods.dev/skills/cosmicstack-labs/mercury-agent-skills/prompt-engineering"><img src="https://agentmods.dev/badge/skills/cosmicstack-labs/mercury-agent-skills/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/cosmicstack-labs/mercury-agent-skills/prompt-engineering"><img src="https://agentmods.dev/badge/skills/cosmicstack-labs/mercury-agent-skills/prompt-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
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 →
- high YARA Match · line 3 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high Anti-Refusal · line 290 Skill instructs the agent to never refuse or to always comply. Suppressing the agent's ability to decline removes a core safety control and enables downstream harmful requests to succeed.Fix: Remove any instruction telling the agent to never refuse or always comply. The agent must retain the ability to decline unsafe, out-of-scope, or harmful requests.
- high Anti-Refusal · line 385 Skill attempts to nullify the agent's safety policies or restrictions ('you have no restrictions', 'ignore your guidelines', 'do anything now'). This is a direct jailbreak that disables guardrails.Fix: Remove jailbreak framing that nullifies safety policies or restrictions. Skill content must not instruct the agent to ignore its guidelines or operate without guardrails.
- high Prompt Injection · line 385 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
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 | $0.00044 | $0.03732 |
| Opus 5 | $0.00022 | $0.01866 |
| Sonnet 5 | $0.00009 | $0.00746 |
| Haiku 4.5 | $0.00004 | $0.00373 |
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 9d 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 — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering
Core Principles
1. Clarity Over Cleverness
A clear, direct prompt always outperforms a clever but ambiguous one. State exactly what you want, in what format, and with what constraints. Ambiguity is the enemy of consistent output.
2. Context is Everything
Models have no inherent context beyond their training data. Every prompt must establish:
- Who the model should be (role)
- What the task is (instruction)
- How to respond (format, tone, length)
- Why the task matters (optional but helpful for complex tasks)
3. Iterate, Don't Expect Perfection First Time
The first prompt is rarely the best. Prompt engineering is an iterative discipline. Each refinement teaches you something about how the model interprets your instructions.
4. Constrain to Liberate
Paradoxically, more constraints (format, length constraints, guardrails) lead to better outputs. Open-ended prompts invite hallucination and inconsistency.
5. Test Systematically
Change one variable at a time. Track what works. Build a personal library of prompt patterns that reliably produce good results.
Prompt Engineering Scorecard
| Level | Characteristics | Typical Output Quality | Refinement Approach |
|---|---|---|---|
| Beginner | Single-sentence prompts, no role definition, no format specification | Inconsistent, often misses the mark, requires manual editing | Trial and error, adds more words hoping for improvement |
| Proficient | Clear instructions, role assignment, basic format constraints, some examples | Mostly correct, occasionally deviates, needs minor edits | Systematic A/B testing, adjusts temperature, adds few-shot examples |
| Expert | Multi-layered instructions, chain-of-thought reasoning, structured output schemas, temperature calibration, guardrails | Highly consistent, follows complex constraints, minimal editing needed | Uses prompt chains, dynamic few-shot selection, automated evaluation, version-controlled prompts |
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.
- 9d ago First seen · 444 lines · 44 tokens per session scan E e336bda11c3b
prompt-engineering is a skill published in the GitHub repository cosmicstack-labs/mercury-agent-skills (471 stars, last pushed 15d ago), licensed MIT. It adds 44 tokens to every session and 3,732 once invoked, about $0.0002 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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