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 bhaumikmaan/claude-code-master-skills --skill prompt-engineeringgit clone --depth 1 https://github.com/bhaumikmaan/claude-code-master-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/bhaumikmaan/claude-code-master-skills/prompt-engineering)<a href="https://agentmods.dev/skills/bhaumikmaan/claude-code-master-skills/prompt-engineering"><img src="https://agentmods.dev/badge/skills/bhaumikmaan/claude-code-master-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/bhaumikmaan/claude-code-master-skills/prompt-engineering"><img src="https://agentmods.dev/badge/skills/bhaumikmaan/claude-code-master-skills/prompt-engineering.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00061 | $0.01537 |
| Opus 5 | $0.00030 | $0.00768 |
| Sonnet 5 | $0.00012 | $0.00307 |
| Haiku 4.5 | $0.00006 | $0.00154 |
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 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering
CRITICAL: Check existing project conventions before inventing new ones. Read CLAUDE.md, .claude/rules/, and existing skills to align with what's already established.
Skill Creation Workflow
Step 1: Analyze the Session/Request
Before asking questions, analyze what you already know:
- What repeatable process was performed or requested
- What the inputs/parameters are
- The distinct steps in order
- Success criteria for each step
- Where the user steered or corrected during the session
- What tools and permissions are needed
Step 2: Interview (4 Rounds)
Use structured questions. Don't over-ask for simple processes.
Round 1 — High-Level Confirmation
- Suggest a name and description. Ask to confirm or rename.
- Suggest goal(s) and specific success criteria.
Round 2 — Details
- Present the steps as a numbered list.
- Suggest arguments based on observed inputs.
- Ask: inline (current conversation) or forked (sub-agent with own context)?
- Fork for self-contained tasks without mid-process user input
- Inline when the user wants to steer mid-process
- Ask where to save:
- This repo (
.claude/skills/<name>/SKILL.md) — project-specific workflows - Personal (
~/.claude/skills/<name>/SKILL.md) — follows you across repos
- This repo (
Round 3 — Per-Step Breakdown For each major step, if not obvious:
- What does this step produce that later steps need?
- What proves this step succeeded?
- Should the user confirm before proceeding? (especially irreversible actions)
- Are any steps independent and could run in parallel?
- What are the hard constraints?
Round 4 — Final
- Confirm when the skill should be invoked. Suggest trigger phrases.
- Ask for gotchas or edge cases.
Step 3: Write the SKILL.md
Use this template:
---
name: {{skill-name}}
description: {{one-line description}}
allowed-tools:
{{list of tool permission patterns}}
when_to_use: {{detailed invocation triggers with example phrases}}
argument-hint: "{{hint showing argument placeholders}}"
arguments:
{{list of argument names}}
context: {{inline or fork — omit for inline}}
---
# {{Skill Title}}
Description of skill
## Inputs
- `$arg_name`: Description of this input
## Goal
Clearly stated goal with defined artifacts or completion criteria.
## Steps
### 1. Step Name
What to do. Be specific and actionable. Include commands when appropriate.
**Success criteria**: What proves this step is done.
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.
- 12d ago First seen · 176 lines · 61 tokens per session scan A ca136fb11c67
prompt-engineering is a skill published in the GitHub repository bhaumikmaan/claude-code-master-skills (3 stars, last pushed 5mo ago), licensed MIT. It adds 61 tokens to every session and 1,537 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 2026-08-31.
Other skills, from other repositories
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guidance
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outlines
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prompt-master
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…
create-simple-prompt
This skill should be used when the user asks to "create a new prompt sample", "add a new prompt sample", "scaffold a new prompt sample", "create a prompt contribution", "add a prompt", or needs to create a new prompt sample with proper folder structure, README, and sample.json metadata. Do NOT use this skill for agent…
prompt-context-engineer
Transform any rough prompt, request, or idea into a well-structured, context-engineered prompt using Andrej Karpathy's context engineering principles. Use this skill whenever the user asks to improve, rewrite, restructure, optimize, or "engineer" a prompt; mentions prompt engineering or context engineering; pastes a…