Borrowing it
Nothing to install: this file belongs to JNZader/repoforge. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/JNZader/repoforge/main/.claude/skills/main/prompts/SKILL.mdgit clone --depth 1 https://github.com/JNZader/repoforgeWrote 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/jnzader/repoforge/prompts)<a href="https://agentmods.dev/skills/jnzader/repoforge/prompts"><img src="https://agentmods.dev/badge/skills/jnzader/repoforge/prompts/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/jnzader/repoforge/prompts"><img src="https://agentmods.dev/badge/skills/jnzader/repoforge/prompts.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 | $0.00029 | $0.00349 |
| Opus 5 | $0.00015 | $0.00175 |
| Sonnet 5 | $0.00006 | $0.00070 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
add-prompts-endpoint 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 9d 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.
What it actually says
Critical Patterns
Using skill_prompt
Utilize skill_prompt to define a new skill prompt for the system.
from repoforge.prompts import skill_prompt
new_skill = skill_prompt("New skill description")
Building skill registry
Leverage build_skill_registry to create a registry of all defined skills.
from repoforge.prompts import build_skill_registry
registry = build_skill_registry([new_skill])
When to Use
- When defining new skills for the application using prompts.
- When creating a registry of skills for orchestration.
- To debug prompt-related issues in the skill definitions.
Commands
python repoforge/cli.py add-prompts-endpoint
Anti-Patterns
Don't: Overuse skill_prompt
Overusing skill_prompt can lead to a cluttered and unmanageable skill set.
# BAD
from repoforge.prompts import skill_prompt
skill1 = skill_prompt("Skill 1")
skill2 = skill_prompt("Skill 2")
skill3 = skill_prompt("Skill 3") # Too many skills without organization
Quick Reference
| Task | Pattern |
|---|---|
| Define a new skill prompt | skill_prompt("description") |
| Create a skill registry | build_skill_registry([skills]) |
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.
- 9d ago First seen · 66 lines · 29 tokens per session scan A 5bd5ff6b4d55
add-prompts-endpoint is a skill published in the GitHub repository JNZader/repoforge (5 stars, last pushed 15d ago), licensed MIT. It adds 29 tokens to every session and 349 once invoked, about $0.0001 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
groq-inference
Ultra-fast LLM inference on custom LPU hardware. OpenAI-compatible API at api.groq.com. Lowest latency in the industry (500-1000+ tok/s). Supports chat completions, vision, audio (Whisper STT + TTS), tool calling, JSON mode, and streaming. Free tier available. Inference only — no training.
arkcli-infer-endpoint
A manager for inference endpoints, the online addresses used to send requests to deployed AI models. It can work with endpoints created by the current SSO sub-user, which is a separately identified account user.
prompt-engineering
Use when one prompt must give the same right answer across reruns, models, and pasted-in hostile input: forcing a fixed schema, picking the few-shot set, ordering the prompt blocks, or the inline cases you run while tuning. NOT the agent loop, tools, or retrieval (that is building-agents), NOT a standing CI eval…
LLM
Implement large language model (LLM) chat completions using the z-ai-web-dev-sdk. Use this skill when the user needs to build conversational AI applications, chatbots, AI assistants, or any text generation features. Supports multi-turn conversations, system prompts, and context management.
c-ai
Query LLMs from the CLI — pipe text for summarization, chat interactively, use local or cloud models with llm or aichat.
prompt-improver
Transform vague prompts into precise, verifiable structured XML prompts that coding agents execute reliably. Modes: execute (default — generate then run) and plan (generate XML for review first). Use when the user says improve prompt, make this work better, prompt engineer, structure a request, plan a complex change…