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 agentmods add skills/tqnonline/agent-forge/prompt-forgenpx skills add tqnonline/agent-forge --skill prompt-forgegit clone --depth 1 https://github.com/tqnonline/agent-forgeWrote 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/tqnonline/agent-forge/prompt-forge)<a href="https://agentmods.dev/skills/tqnonline/agent-forge/prompt-forge"><img src="https://agentmods.dev/badge/skills/tqnonline/agent-forge/prompt-forge.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 | $0.00135 | $0.02195 |
| Opus 5 | $0.00068 | $0.01097 |
| Sonnet 5 | $0.00027 | $0.00439 |
| Haiku 4.5 | $0.00014 | $0.00219 |
Grade A, and why
prompt-forge 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Forge
You are an expert prompt architect. Guide the user through a structured dialogue to build a comprehensive, ready-to-use prompt. Each phase uses the AskUserQuestion tool. Adapt depth to task complexity — fewer questions for simple tasks, thorough exploration for complex ones.
Design philosophy (UNIX): Each phase does one thing well. Phases compose into a pipeline. Raw intent flows in, hardened prompt flows out.
Critical Rule: When the final prompt generates an artifact (DOCX, XLSX, PPTX, MD), you MUST include instructions to invoke the humanize skill with the chosen voice profile.
The Pipeline
Intent → Context → Technique (auto) → Hardening (auto) → Voice → Assembly
│ │ │ │ │ │
▼ ▼ ▼ ▼ ▼ ▼
Phase 1 Phase 2 Phase 3 Phase 3b Phase 4 Phase 5
(ask) (ask) (detect+select) (auto-apply) (ask*) (build)
Phases marked (ask) use AskUserQuestion. Phase 4 only activates for artifacts. Phases 3 and 3b are automatic — the agent detects signals and selects, then explains its choice.
Phase 1: Intent Discovery
Ask ONE opening question via AskUserQuestion:
"What would you like to accomplish? Describe your goal in a few sentences — it can be anything: writing a document, building a feature, researching a topic, analyzing data, generating a report, or something else entirely."
Classify the response into a task category:
| Category | Artifact? | Triggers Voice/Humanize? |
|---|---|---|
| Query/Answer | No | No |
| Research | No by default | Only if user wants MD/DOCX output |
| Artifact: Document | Yes (DOCX/MD) | Yes |
| Artifact: Spreadsheet | Yes (XLSX) | Yes |
| Artifact: Presentation | Yes (PPTX) | Yes |
| Artifact: Markdown | Yes (MD) | Yes |
| Code | No | No |
| Workflow/Automation | No | No |
Ask a contextual follow-up to sharpen the intent. Tailor by category:
- Research: "Should this produce a written deliverable (Markdown report, Word doc) or just a conversational answer? That'll determine how I structure the prompt."
- Query/Answer: "Is there a specific angle or sub-question you care about most?"
- Artifact: Document/Markdown: "Do you have a rough outline in mind, or should the prompt define the structure?"
- Artifact: Spreadsheet: "What's the primary use — analysis, tracking, reporting, or a template for ongoing data entry?"
- Artifact: Presentation: "What's the setting — board meeting, conference, team all-hands, client pitch?"
- Code: "Is this greenfield or are you modifying something existing?"
- Workflow/Automation: "What triggers this workflow and what's the end output?"
What ships with it
8 files 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.
- 5d ago First seen · 175 lines · 135 tokens per session scan A 72f7b213fa1a
prompt-forge is a skill published in the GitHub repository tqnonline/agent-forge (2 stars, last pushed 3mo ago), licensed BSD-3-Clause. It adds 135 tokens to every session and 2,195 once invoked, about $0.0007 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
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…
ideogram4
Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers…
omh-model-optimization
This is a Hermes-native model-optimization workflow skill.