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 aroyburman-codes/compound-product-management --skill prompt-coachgit clone --depth 1 https://github.com/aroyburman-codes/compound-product-managementWrote 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/aroyburman-codes/compound-product-management/prompt-coach)<a href="https://agentmods.dev/skills/aroyburman-codes/compound-product-management/prompt-coach"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/compound-product-management/prompt-coach/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/aroyburman-codes/compound-product-management/prompt-coach"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/compound-product-management/prompt-coach.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 | $0.00045 | $0.01357 |
| Opus 5 | $0.00023 | $0.00678 |
| Sonnet 5 | $0.00009 | $0.00271 |
| Haiku 4.5 | $0.00005 | $0.00136 |
Grade B, and why
prompt-coach scanned grade B with 1 finding 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 11d 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
Coach the user toward better prompts. Intervene only when it matters. Show, don't lecture. How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Coach
Coach the user toward better prompts. Intervene only when it matters. Show, don't lecture.
When to Nudge
Fire coaching only when one of these conditions is true:
- Missing goal — the prompt says what but not why or what success looks like
- No constraints — no mention of language, framework, style, or boundaries
- Overloaded request — multiple unrelated tasks bundled into one prompt
- Dump-and-hope — code or content pasted with no explicit question
- Repeated failure — the same type of request has failed or needed correction 2+ times this session
- Undefined success — "make this better" or "fix this" with no measurable target
When to Stay Silent
- Conversation history already fills in the ambiguity
- The user gave a clear, specific instruction
- Slash commands and memorization prompts (clear intent)
- The user has bypassed coaching before on similar prompts
How to Coach
In-Session (Real-Time)
When a prompt triggers coaching:
-
Research first, ask second. Check the codebase, conversation history, and memory files before formulating questions. Questions grounded in context ("Are you targeting the UserProfile component or the API layer?") beat generic ones ("Can you be more specific?").
-
Ask 1-3 targeted questions. Never more than 3. Use the AskUserQuestion tool when available. Frame as quick clarifications, not interrogations.
-
Show the rewrite. After getting answers, show the improved prompt alongside the original. Concrete demonstration transfers faster than abstract guidance.
Example:
Your prompt: "Fix the login bug"
Improved: "Fix the login bug where users get a 401 after OAuth redirect.
The issue is in auth/callback.ts — the session token isn't persisted
before the redirect completes. Expected: user lands on /dashboard
after Google OAuth. Actual: user sees 'Unauthorized' and loops back
to /login."
Retrospective (Session Review)
At session breakpoints, scan the conversation for prompting patterns:
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.
- 11d ago First seen · 118 lines · 45 tokens per session scan B ae0b76d0e97a
prompt-coach is a skill published in the GitHub repository aroyburman-codes/compound-product-management (1 stars, last pushed 6mo ago), licensed MIT. It adds 45 tokens to every session and 1,357 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). 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-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…
prompt-optimization
Improves LLM-facing context while preserving intent, execution boundaries, and proportional work. Use when creating or reviewing prompts, agent definitions, skill definitions, or other instructions for an LLM.
recipe-eval-prompt
Compares original and optimized prompts through repeated blind paired execution in git worktrees. Use when evaluating prompt improvement effects or learning prompt engineering through concrete examples.
dspy-optimize-anything
Use for GEPA optimizeanything on text artifacts such as code, prompts, agent architectures, configs, and non-DSPy optimization targets.
dspy-haystack-integration
Use for integrating DSPy with Haystack, optimizing Haystack prompts, improving retrieval pipelines, and extracting DSPy prompts.
dspy-miprov2-optimizer
Use for MIPROv2, Bayesian optimization, instruction and demo tuning, and high-performance DSPy program optimization.