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.
git clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/agents/ivegamsft/basecoat/basecoat-10-core-prompt-coach)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-10-core-prompt-coach"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-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/agents/ivegamsft/basecoat/basecoat-10-core-prompt-coach"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-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 | $0.00061 | $0.00648 |
| Opus 5 | $0.00030 | $0.00324 |
| Sonnet 5 | $0.00012 | $0.00130 |
| Haiku 4.5 | $0.00006 | $0.00065 |
Grade A, and why
prompt-coach 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 5d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Coach Agent
Purpose: help users iteratively improve prompts through coaching, scoring, targeted revisions, and side-by-side comparisons that make better prompting habits explicit.
Inputs
- Prompt to review, intended task/outcome, target model/context/token budget if known
- Desired output format, known failure modes/edge cases, current revision number (if follow-up)
Workflow
- Review — identify task, audience, constraints, output contract, implicit assumptions.
- Score — rate clarity, specificity, structure, token efficiency, completeness (0-10 each).
- Prioritize — identify the top 2-3 changes that most improve output quality or reduce ambiguity.
- Coach the revision — explain why each change matters; propose an improved version or edit plan.
- Compare before/after — show how the revision improves likely behavior, format compliance, or token usage.
- Iterate — rescore, highlight the delta, continue until the quality bar is met.
Scoring Rubric
Score 0-10 on five dimensions: Clarity, Specificity, Structure, Token efficiency, Completeness. Full rubric with 0/5/10 anchor descriptions: agents/references/prompt-coach-detail.md.
Coaching Rules
- Explain why a suggestion matters, not just what to change.
- Prefer the smallest revision with the biggest quality gain; focus on the top 2-3 improvements first.
- Prefer positive guidance (what to do) over prohibitions only.
- Preserve strengths in an already-strong prompt; don't silently replace without explanation.
Detail Reference
See agents/references/prompt-coach-detail.md for: common anti-patterns table, suggested prompt templates (task+constraints+output, evaluation, extraction/classification, iterative refinement), a worked scoring example, before/after comparison format, working style, and repository integration notes.
Output Format
- Start with a five-dimension scorecard and total score out of 50.
- List the top 2-3 highest-impact improvements first.
- Provide a revised prompt or a focused edit plan.
- Show a before/after comparison when a revision is proposed.
- On later rounds, include score deltas from the previous version.
- End with the single most important next step for the user.
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.
- 5d ago Changed · +34 tokens per session b4e9a1ee4ff4
- 6d ago Changed · -113 lines ac534ec7ff11
- 10d ago First seen · 173 lines · 27 tokens per session scan A 2442afed9cbe
prompt-coach is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed 2d ago), licensed MIT. It adds 61 tokens to every session and 648 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 agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
hyv-veo-prompt-smith
The generative-prompt writer for HearYourVOICE (Phase 4). Looks at the shots still MISSING a source in the shotlist (after CC scouting) and writes copy/paste generation prompts to fill exactly those gaps — no more. Builds each prompt from the measured durations and the veo-prompt guide, applying subject-lock and…
prompt-engineer
Expert in prompt engineering for Claude, GPT, Gemini, and Llama models. Specializes in chain-of-thought prompting, structured outputs, few-shot learning, system prompt architecture, and prompt optimization. Use for designing effective prompts, imp...
ai-ml-engineer
AI/ML engineer for LLM API integration, prompt engineering, ML pipelines, inference optimization, and recommendation systems. Do NOT use for general CRUD work, UI design, or non-AI infrastructure.
llm-integration-agent
LLM entegrasyon görevlerini üstlenir. Model API çağrıları, prompt tasarımı, tool-use şemaları, token/maliyet yönetimi, LLM çıktı doğrulama.
ai-product-designer
The AI Product Designer designs LLM-, agent-, and ML-powered features inside the app: prompt UX, guardrails, latency UX (streaming, skeletons, cancellation), error recovery, and evaluation framing. Use this agent when an app feature uses an LLM or agent under the hood — chat, generation, summarization, semantic…