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 LovelaceLoom/grok-plugin-cc --skill grok-promptinggit clone --depth 1 https://github.com/LovelaceLoom/grok-plugin-ccWrote 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/lovelaceloom/grok-plugin-cc/grok-prompting)<a href="https://agentmods.dev/skills/lovelaceloom/grok-plugin-cc/grok-prompting"><img src="https://agentmods.dev/badge/skills/lovelaceloom/grok-plugin-cc/grok-prompting/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/lovelaceloom/grok-plugin-cc/grok-prompting"><img src="https://agentmods.dev/badge/skills/lovelaceloom/grok-plugin-cc/grok-prompting.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 | $0.00030 | $0.01065 |
| Opus 5 | $0.00015 | $0.00532 |
| Sonnet 5 | $0.00006 | $0.00213 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
grok-prompting 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Composing Grok prompts
Use this skill from inside grok:grok-rescue (and only there) to tighten a user's free-form rescue request into a more effective Grok prompt before calling task.
This skill draws on Grok's distinct strengths:
- Live web/X search awareness — Grok can search current information on the web and on X (Twitter) without being limited to a training cutoff. Prefer asking Grok for help on questions that depend on recent events, recent docs, or live discourse.
- Code-focused model defaults — the default model (
grok-build) is tuned for advanced coding work with a 512K-token context window, so it can absorb a substantial portion of a codebase at once. - Distinct reasoning style — Grok tends to be direct and willing to take strong positions. Don't pad the prompt with hedging.
Prompt structure
A good rescue prompt for Grok is short, direct, and grounded:
- Goal — one sentence stating what the user wants resolved or answered.
- Context — two to four sentences of relevant repo context (what file/feature is involved, what's known to be broken, what's already been tried).
- Constraints — anything that's off-limits (don't change public API, don't add dependencies, don't touch tests).
- Output expectation — what Grok should return: an explanation, a diagnosis, a patch plan, or actual code edits.
Patterns
| User intent | Prompt pattern |
|---|---|
| "investigate why X" | Goal: diagnose root cause. Context: behavior + reproduction. Output: ranked list of suspect causes with evidence. |
| "fix the failing test" | Goal: make <test name> pass. Context: test file + recent changes. Output: minimal patch + explanation. |
| "explain how X works" | Goal: explain the data/control flow of <feature>. Output: a walkthrough citing files and line numbers. |
| "what should the design be" | Goal: propose a design for <feature>. Output: 2–3 options with tradeoffs, then a recommendation. |
| "what changed about X recently" | Goal: identify recent ecosystem changes (web search). Context: the framework/library. Output: dated summary + sources. |
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 · 60 lines · 30 tokens per session scan A 125fd19da284
grok-prompting is a skill published in the GitHub repository LovelaceLoom/grok-plugin-cc (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 30 tokens to every session and 1,065 once invoked, about $0.0002 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
opik-optimizer
Optimize LLM prompts, tools, and agents in Opik using standardized optimizer workflows (prompt optimization, tool optimization, and parameter tuning), dataset/metric wiring, and result interpretation.
meta-prompting
Enhanced reasoning patterns via slash commands (/think, /verify, /adversarial, /edge, /compare, /confidence, /budget, /constrain, /json, /flip, /assumptions, /tensions, /analyze, /trade) or natural language ("argue against", "what could break", "show reasoning", "deep review", "meta-prompts", "thinking modes"…
prompt-tuning
Tune a prompt, or anything whose quality is measured by non-deterministic model output, without chasing noise - a noise baseline before the first edit, medians over repeated runs, enforcement AFTER generation rather than in the wording. Use when iterating on prompts or model-judged output.
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.
refine-prompt
Transforms vague or rough prompts into precise, structured AI instructions. Use when asked to "refine prompt", "improve prompt", "make this prompt better", "promptify", "optimize prompt", "rewrite prompt", "enhance prompt", or "sharpen instructions".