engineer-prompts

engineer-prompts is a skill for Claude Code, Codex from Drizzy07x/Skillquiver. It costs 44 tokens per session (711 once invoked), scanned A, original, MIT.

A method for designing or checking prompts that tell an agent exactly what outcome to produce. It defines permissions, tools, evidence, and conditions for stopping.

In plain words
What is it for?
Use it to write reusable agent instructions, review existing prompts, and specify tests or evidence for successful work.
Why use it?
It removes ambiguity about what the agent may do and what counts as a completed, verifiable result.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present.

Part of the skillquiver plugin — 24 skills shipped together

Good fit Use it to write reusable agent instructions, review existing prompts, and specify tests or evidence for successful work.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/drizzy07x/skillquiver/engineer-prompts
Install

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.

Any agent
npx skills add Drizzy07x/Skillquiver --skill engineer-prompts
Clone the repo
git clone --depth 1 https://github.com/Drizzy07x/Skillquiver

Made for: Claude Code, Codex.

Or install skillquiver, the plugin that ships this one along with the rest of its 24 skills.

Wrote 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.

agentmods badge for engineer-prompts

README.md
[![agentmods](https://agentmods.dev/badge/skills/drizzy07x/skillquiver/engineer-prompts.svg)](https://agentmods.dev/skills/drizzy07x/skillquiver/engineer-prompts)
Your own site
<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>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 711 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 7d ago against content hash 2528ecb422eb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

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.

skills/engineer-prompts/SKILL.md · 64 lines

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

  1. Extract the requested outcome. Describe the finished state, not the activity.
  2. Write observable success criteria. Avoid criteria such as "high quality" unless a measurable definition follows.
  3. 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.
  4. Name the tools that may be used and the evidence required before claiming completion.
  5. Define stop conditions for completion, blockers, exhausted retries, or required user decisions.
  6. Include target_model only when the user requests model-specific optimization. Check version-matched current documentation first (see research-systematically) before adding model-specific guidance.
  7. 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.
  8. 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"]
}

Read the full file on GitHub · 64 lines

Files

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.

Changes

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.

  1. 7d ago First seen · 64 lines · 44 tokens per session scan A 2528ecb422eb

Subscribe to this mod's changes

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.

Related

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.

jjanczur/tyran · 58 tokens

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).

komluk/scaffolding · 62 tokens

grok-prompting

Internal guidance for composing Grok prompts for coding, review, diagnosis, and research tasks inside the Grok Claude Code plugin.

LovelaceLoom/grok-plugin-cc · 30 tokens

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.

savasturkoglu1/promptpilot-plugin · 63 tokens

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.

rayapatiranjithai/prompt-analyzer · 64 tokens

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.

rayapatiranjithai/prompt-analyzer · 60 tokens