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 OpenCoven/coven --skill prompt-vaultgit clone --depth 1 https://github.com/OpenCoven/covenWrote 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/opencoven/coven/prompt-vault)<a href="https://agentmods.dev/skills/opencoven/coven/prompt-vault"><img src="https://agentmods.dev/badge/skills/opencoven/coven/prompt-vault/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/opencoven/coven/prompt-vault"><img src="https://agentmods.dev/badge/skills/opencoven/coven/prompt-vault.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 | $0.00039 | $0.00454 |
| Opus 5 | $0.00019 | $0.00227 |
| Sonnet 5 | $0.00008 | $0.00091 |
| Haiku 4.5 | $0.00004 | $0.00045 |
Grade A, and why
prompt-vault 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 10d 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.
What it actually says
Prompt Vault
Local-first prompt manager. Use when asked to save a prompt, find a saved prompt, list prompts by tag, or export/import the prompt library.
Prerequisites
pvCLI installed:npm install -g BunsDev/prompt-vault- Data stored at
~/.prompt-vault.db(SQLite)
Commands
Save a prompt
pv add "prompt text here" --tags tag1,tag2
- Tags are comma-separated, no spaces
- Returns the prompt ID
Search prompts
pv search "keyword"
- Substring match on prompt text
List prompts
pv list # all prompts, newest first
pv list --tag code # filter by tag
Export library
pv export # JSON to stdout
pv export > prompts.json
Workflow Patterns
Save a prompt the user liked
When the user says "save that prompt" or "remember this prompt":
- Identify the prompt text from conversation context
- Ask for tags if not obvious (or infer from context)
pv add "<text>" --tags <tags>
Find a prompt for reuse
When the user says "find my prompt about X" or "use that refactor prompt":
pv search "X"to find matches- Present the best match
- Use it in the current task
Suggest tags
Common useful tags: system-prompt, code-review, refactor, summary, debug, explain, test, docs, creative, agent, tool-use
Notes
- Prompt text with quotes: escape inner quotes or use single quotes around the CLI arg
- The DB is local-only — no sync. Use
pv exportto back up. - Search is basic substring matching (LIKE %query%)
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.
- 10d ago First seen · 64 lines · 39 tokens per session scan A 2f3dd4c443d5
prompt-vault is a skill published in the GitHub repository OpenCoven/coven (47 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 454 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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chain-of-thought-prompts
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few-shot-example-gen
Few-shot example generation and optimization for improved LLM performance.
llm-classifier
LLM-based zero-shot and few-shot classification for flexible intent detection.
oracle
Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.
prompt-tuning
Tune a prompt, or anything whose quality is measured by non-deterministic model output, without chasing noise - a noise baseline before the first edit, medians over repeated runs, enforcement AFTER generation rather than in the wording. Use when iterating on prompts or model-judged output.