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/research.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/research)<a href="https://agentmods.dev/commands/justinperea/midjourney-cc-skill/research"><img src="https://agentmods.dev/badge/commands/justinperea/midjourney-cc-skill/research/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/research"><img src="https://agentmods.dev/badge/commands/justinperea/midjourney-cc-skill/research.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 | $0.01020 |
| Opus 5 | $0.00000 | $0.00510 |
| Sonnet 5 | $0.00000 | $0.00204 |
| Haiku 4.5 | $0.00000 | $0.00102 |
Grade A, and why
research 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 10d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research
Research community techniques for a specific Midjourney generation challenge. Usable standalone or mid-session.
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. -
Determine context — check for an active session:
SELECT id, intent, total_iterations, approach_rationale FROM sessions WHERE status = 'active' LIMIT 1- Mid-session: The user hit a wall during iteration. Research focus comes from the gap analysis of recent failures.
- Standalone: No active session. Research is exploratory — produce a brief for future sessions.
-
Parse research focus from user input (
$ARGUMENTS). If empty and mid-session, derive focus from the last 3 iterations:SELECT iteration_number, what_failed, gap_analysis, prompt FROM iterations WHERE session_id = ? ORDER BY iteration_number DESC LIMIT 3Extract the recurring failure themes and use them as the research focus.
-
Check internal knowledge first — what do we already know about this topic?
SELECT id, category, problem, solution, confidence, success_rate FROM patterns WHERE is_active = 1 AND (problem LIKE '%' || ? || '%' OR solution LIKE '%' || ? || '%') ORDER BY confidence DESCSELECT keyword, intended_effect, effectiveness, notes FROM keyword_effectiveness WHERE intended_effect LIKE '%' || ? || '%' ORDER BY effectiveness DESCNote what's available internally so research can fill the gaps rather than duplicate.
-
Construct targeted search queries (pick 3-5 most relevant):
- Core technique:
"midjourney v7" "{primary_concept}" prompt - Reddit community:
site:reddit.com/r/midjourney "{concept}" tips - Failure-specific:
"midjourney" "{hard_aspect}" how to - Parameter-specific:
"midjourney v7" --style raw "{concept}" - Prompt examples:
"midjourney prompt" "{concept}" example
- Core technique:
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.
- 10d ago First seen · 97 lines · 0 tokens per session scan A bf3eae89cf30
research is a command published in the GitHub repository JustinPerea/midjourney-cc-skill (12 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,020 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.
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