Borrowing it
Nothing to install: this file belongs to JustinPerea/midjourney-cc-skill. 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/JustinPerea/midjourney-cc-skill/main/.claude/commands/apply-knowledge.mdgit clone --depth 1 https://github.com/JustinPerea/midjourney-cc-skillWrote 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/commands/justinperea/midjourney-cc-skill/apply-knowledge)<a href="https://agentmods.dev/commands/justinperea/midjourney-cc-skill/apply-knowledge"><img src="https://agentmods.dev/badge/commands/justinperea/midjourney-cc-skill/apply-knowledge/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/commands/justinperea/midjourney-cc-skill/apply-knowledge"><img src="https://agentmods.dev/badge/commands/justinperea/midjourney-cc-skill/apply-knowledge.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 | $0.00000 | $0.01175 |
| Opus 5 | $0.00000 | $0.00588 |
| Sonnet 5 | $0.00000 | $0.00235 |
| Haiku 4.5 | $0.00000 | $0.00118 |
Grade A, and why
apply-knowledge 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 8d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply Knowledge
Given a reference description or intent, show which learned patterns would apply and construct a prompt using accumulated knowledge.
Instructions
-
Verify database access. Run
SELECT COUNT(*) FROM sessionsvia sqlite-simple MCP. If the query fails, tell the user: "Database not available. Runclaude mcp add sqlite-simple -- npx @anthropic-ai/sqlite-simple-mcp mydatabase.dbthen restart Claude Code." Do not proceed without database access. -
Analyze the user's input to identify relevant categories. The input could be:
- A description of what they want ("glowing organic form with dual-tone lighting")
- A reference to a visual style ("like that glass sculpture look")
- A set of requirements ("commercial product shot, clean, minimal")
-
Extract category tags from the input. Map descriptions to categories:
- Mentions of light, glow, shadow, bright, dark →
lighting - Mentions of glass, metal, wood, fabric, ceramic →
materials - Mentions of shape, form, organic, geometric →
forms - Mentions of color names, warm, cool, vibrant →
color - Mentions of mood, feeling, vibe →
mood - Mentions of render, 3D, photo, illustration →
style - Mentions of framing, layout, position →
composition
- Mentions of light, glow, shadow, bright, dark →
-
Query candidate patterns:
SELECT * FROM patterns WHERE is_active = 1 AND category IN (...extracted categories...) AND confidence IN ('medium', 'high') ORDER BY confidence DESC, success_rate DESC -
Score each pattern for relevance to this specific task. Not every pattern in a matching category is equally useful. For each candidate pattern, assess:
a. Context match — Does the pattern's problem description match the current situation? A lighting pattern for "soft ambient" is irrelevant when the user wants "harsh directional." Read the pattern's
problem,example_bad,example_good, andnotesfields to judge fit.b. Specificity alignment — Prefer patterns whose specificity level matches the task:
universalpatterns always apply (prompt structure rules, parameter basics)generalpatterns apply when the category matchesspecificpatterns apply only when the exact context matches (checknotesandtagsfor conditional context like "works when subject is organic" or "only for V7")user-preferencepatterns apply when the same user's style preferences are relevant
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.
- 8d ago First seen · 98 lines · 0 tokens per session scan A f3c479fc8f5c
apply-knowledge is a command published in the GitHub repository JustinPerea/midjourney-cc-skill (11 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,175 tokens. 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 commands, from other repositories
gen-image
Generate a brand-aligned AI image — tool selection, full prompt (subject, style, lighting, camera, mood, negatives), variation prompts, and seed strategy. Works across Midjourney, DALL-E, Ideogram, Firefly, and Stable Diffusion.
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
start-3-1-3
Module 3.1.3: Consistency & Style - Golden Rules of prompting, reference images, and variants.
develop-image-prompt.eval
Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.
stardust-use
A command for retrieving an encrypted prompt template from a service, decrypting it in memory, filling in parameters, and generating content. It requires an authenticated session.
pickle-prompt
Build a model-ready image/video prompt with the macro-pickle Prompt Engine and (on export) save it to your prompt vault.