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 Drizzy07x/Skillquiver --skill engineer-promptsgit clone --depth 1 https://github.com/Drizzy07x/SkillquiverWrote 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/drizzy07x/skillquiver/engineer-prompts)<a href="https://agentmods.dev/skills/drizzy07x/skillquiver/engineer-prompts"><img src="https://agentmods.dev/badge/skills/drizzy07x/skillquiver/engineer-prompts.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00711 |
| Opus 5 | $0.00022 | $0.00356 |
| Sonnet 5 | $0.00009 | $0.00142 |
| Haiku 4.5 | $0.00004 | $0.00071 |
Grade A, and why
engineer-prompts 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 7d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engineer Prompts
Turn an informal request into a prompt whose result can be verified. Keep the core contract provider- and version-neutral; add target-specific advice only when a target model is explicitly supplied and current documentation supports it.
Workflow
- Extract the requested outcome. Describe the finished state, not the activity.
- Write observable success criteria. Avoid criteria such as "high quality" unless a measurable definition follows.
- Separate boundaries from permissions:
- boundaries state what is in and out of scope;
- permissions state which reads, writes, network calls, installations, or external side effects are authorized.
- Name the tools that may be used and the evidence required before claiming completion.
- Define stop conditions for completion, blockers, exhausted retries, or required user decisions.
- Include
target_modelonly when the user requests model-specific optimization. Check version-matched current documentation first (see research-systematically) before adding model-specific guidance. - Check the contract structurally by hand: every required field present, every list non-empty with distinct strings, no unknown fields, fields kept in the canonical order below, no model names outside
target_model. Then perform the semantic audit below — structural checks cannot determine whether prose is genuinely observable or authorized. - When a textual prompt is needed, render the contract into a stable prompt: one section per field, in the canonical order, wording taken verbatim from the contract, nothing added.
Contract shape
Use a JSON object with these required fields, in this order:
{
"outcome": "A concrete finished state",
"success_criteria": ["An observable condition"],
"boundaries": ["A scope limit"],
"permissions": ["An explicitly allowed action"],
"tools": ["A tool or capability"],
"evidence": ["Proof required for a claim"],
"stop_conditions": ["A condition that ends or pauses work"]
}
What ships with it
1 file 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.
- 7d ago First seen · 64 lines · 44 tokens per session scan A 2528ecb422eb
engineer-prompts is a skill published in the GitHub repository Drizzy07x/Skillquiver (2 stars, last pushed 15d ago), licensed MIT. It adds 44 tokens to every session and 711 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 2026-08-31.
Other skills, from other repositories
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.
context-engineering
Optimize Claude Code context-window usage for accuracy and cost. TRIGGER when: hitting context limits, structuring prompts for an agent, or trimming what gets injected into a task. SKIP: persisting knowledge across sessions (use agent-memory); semantic recall tuning (use semantic-memory-mcp).
grok-prompting
Internal guidance for composing Grok prompts for coding, review, diagnosis, and research tasks inside the Grok Claude Code plugin.
prompt-pilot
Rewrite a rough, vague prompt into a precise, context-enriched prompt. Explores the codebase read-only and weaves real file paths and symbols into the rewritten prompt — never implements the task itself. Use when the user asks to optimize, enrich, or improve a prompt before running it.
analyze-prompt
Use when the user wants to review, score, or improve how they write prompts; when coaching prompt quality; or when tracking prompting mistakes over a session. Keywords: prompt analysis, prompt coaching, how should I ask, rate my prompt, am I prompting well, prompt statistics, prompt score.
prompt-report
Use when the user wants an end-of-session summary of their prompting: overall stats, best and worst prompts, biggest recurring mistake, improvement curve, and personalized recommendations. Keywords: prompt report, session summary, how did I do, my prompting stats, prompt scorecard, prompting progress.