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 Jamie-BitFlight/claude_skills --skill cove-prompt-designgit clone --depth 1 https://github.com/Jamie-BitFlight/claude_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/jamie-bitflight/claude_skills/cove-prompt-design)<a href="https://agentmods.dev/skills/jamie-bitflight/claude_skills/cove-prompt-design"><img src="https://agentmods.dev/badge/skills/jamie-bitflight/claude_skills/cove-prompt-design/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/jamie-bitflight/claude_skills/cove-prompt-design"><img src="https://agentmods.dev/badge/skills/jamie-bitflight/claude_skills/cove-prompt-design.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 | $0.00061 | $0.01056 |
| Opus 5 | $0.00030 | $0.00528 |
| Sonnet 5 | $0.00012 | $0.00211 |
| Haiku 4.5 | $0.00006 | $0.00106 |
Grade A, and why
cove-prompt-design 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 12d 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chain of Verification (CoVe) for Prompt Design
This skill documents what CoVe is, when to use it, and how to apply it correctly in prompt design.
CoVe is a prompt design pattern that improves factual reliability by separating generation from verification. The model first produces an answer, then independently verifies that answer through structured checks, and finally produces a corrected or confirmed result.
This is not chain of thought exposure. The verification steps are explicit instructions, not hidden reasoning.
What CoVe Is
Chain of Verification (CoVe) is a structured prompting approach with three phases:
- Initial answer generation
- Independent verification questions
- Final validated answer
The key idea is that the model should not assume its first answer is correct. It must actively test it.
When to Use CoVe
Use CoVe when any of the following are true:
- The task requires factual accuracy (dates, specs, standards, APIs).
- Hallucinations would be costly or misleading.
- The answer depends on multiple independent facts.
- The model may rely on weak priors or pattern completion.
- You want the model to challenge its own output.
Concrete examples:
- Explaining technical standards or protocols.
- Summarizing regulations or compliance requirements.
- Producing step by step procedures with real world consequences.
- Comparing versions, limits, or constraints.
- Answering questions that look simple but are easy to get subtly wrong.
Do not use CoVe for:
- Creative writing.
- Brainstorming.
- Open ended ideation.
- Pure opinion or preference questions.
Core CoVe Structure
A minimal CoVe prompt has this structure:
Step 1: Produce an initial answer.
Step 2: Generate verification questions that would test the answer.
Step 3: Answer each verification question independently.
Step 4: Revise or confirm the original answer based on verification.
The verification questions must be phrased so they can falsify the answer.
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.
- 12d ago First seen · 189 lines · 61 tokens per session scan A e2a8fecb0a57
cove-prompt-design is a skill published in the GitHub repository Jamie-BitFlight/claude_skills (66 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 1,056 once invoked, about $0.0003 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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