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 codingmydna/grokers --skill imaginegit clone --depth 1 https://github.com/codingmydna/grokersWrote 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/codingmydna/grokers/imagine)<a href="https://agentmods.dev/skills/codingmydna/grokers/imagine"><img src="https://agentmods.dev/badge/skills/codingmydna/grokers/imagine/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/codingmydna/grokers/imagine"><img src="https://agentmods.dev/badge/skills/codingmydna/grokers/imagine.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 | $0.00097 | $0.02283 |
| Opus 5.5 | $0.00039 | $0.00913 |
| Sonnet 5.5 | $0.00019 | $0.00457 |
| Haiku 4.5 | $0.00010 | $0.00228 |
Grade A, and why
imagine 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 1mo 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.
This is a copy
100% identical to imagine — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Imagine
Guidance for the two image tool calls in Grok Build:
image_gen- generate a new image from a text prompt.image_edit- modify an existing image using a text prompt and source image.
Apply this whenever you're considering or about to call either tool.
Build accurate visuals with code, not the image tools
- Image models are unreliable at exact text, numbers, and structure. They can handle short text or a simple layout, but they often garble words, invent numbers, draw chart bars that match no data, or point diagram arrows nowhere, and the more that has to be exact, the worse they do. A detailed prompt doesn't make it dependable, and an
image_editpass usually won't fix it. So when a result needs specific text, data, or structure to be correct (charts from real numbers, labeled or technical diagrams, math explainers, tables, screens with real copy), construct the asset with code, where you control the exact content. Prefer HTML and CSS, which give much better layout, typography, and polish than Python plotting. When only the look matters (photos, illustrations, characters, scenes, decorative art), the image tools are the right choice. Which one fits depends on what the output needs to get right, not on how the request is worded.
Verifying discrete accuracy (loop)
When the output must get specific text, numbers, data, or structure right, don't trust the first result - verify it in a loop:
- Produce the result (generate, or per Build accurate visuals with code, construct it in code).
- Inspect the actual output - use image understanding to read a generated image back (or check the rendered code) - and confirm every word, number, label, and structural detail matches the requirement, and that nothing overlaps, clips, or runs off-canvas.
- If anything is wrong, fix and re-verify:
- Garbled text, invented numbers, or broken layout from an image model? Don't just re-prompt - it will likely garble it again. Rebuild it with code.
- Overlapping or clipped elements in code-built output? Re-lay-out with auto-layout (HTML/CSS) rather than nudging coordinates by hand.
- Otherwise make one targeted edit.
- Only finish when the discrete content is exactly correct. If it can't be made accurate, tell the user instead of shipping something wrong.
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.
- 1mo ago First seen · 131 lines · 97 tokens per session scan A ced79ca46b18
imagine is a skill published in the GitHub repository codingmydna/grokers (4 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 97 tokens to every session and 2,283 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to imagine, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
byok-custom-model
Register a custom LLM endpoint with your own API key for chat in Starchild. Use when adding a personal Anthropic, OpenAI, Grok, Qwen, DeepSeek, Meta (Muse Spark), NEAR AI, or Venice key as a chat model (e.g. add my Claude key, register DeepSeek, use Muse Spark 1.1).
loongsuite-pilot-insight
A reporting workflow for turning LoongSuite Pilot and AI coding-agent logs into structured reports about events, teams, data quality, development efficiency, and AI use. It defines the meaning of the log fields and the measurements used in dashboards.
byok-relay
OpenAI-compatible LLM gateway for any client-side application (browser, mobile, React Native, Flutter, VS Code extensions, browser extensions, Electron, smart TV, and more). Routes requests to OpenAI, Anthropic, Gemini, Groq, Mistral, and 200+ models, handling CORS, key encryption, and streaming without a dedicated…
pi-triage
Decide whether an upstream pi change needs porting to the Go port. Use when assessing upstream commits/PRs ("should we port X?"), or as the triage stage of /pi-sync. Outputs a WHY/WHAT/SCOPE verdict per change.
pi-parity-review
Adversarially verify that a ported change is faithful to the original pi implementation (TS source + published npm build). Use after porting upstream pi changes, or standalone on any area of this repo ("is X faithful to pi?").
Review a GitHub PR (via gh)
Review a specific GitHub pull request with gh — fetch the diff, fan out reviewers, consolidate, and optionally post the review. Requires the gh CLI or the GitHub MCP server.