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/reflect.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/reflect)<a href="https://agentmods.dev/commands/justinperea/midjourney-cc-skill/reflect"><img src="https://agentmods.dev/badge/commands/justinperea/midjourney-cc-skill/reflect/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/reflect"><img src="https://agentmods.dev/badge/commands/justinperea/midjourney-cc-skill/reflect.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 | $0.02652 |
| Opus 5 | $0.00000 | $0.01326 |
| Sonnet 5 | $0.00000 | $0.00530 |
| Haiku 4.5 | $0.00000 | $0.00265 |
Grade A, and why
reflect 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 — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect
Analyze iteration logs and extract/update patterns in the knowledge base.
Updated Role
This command now serves as the deep analysis layer. Lightweight pattern extraction and
markdown regeneration happen automatically when sessions close (see "Session Lifecycle & Automatic
Reflection" in rules/learn-reflection.md). Pattern confidence graduates automatically based on times_tested and
success_rate thresholds — no manual review gate.
/reflect adds:
- Contrastive analysis across sessions (success vs failure comparison)
- Cross-session pattern discovery
- Contradiction resolution (when patterns conflict, produce conditional rules)
- Force full knowledge base regeneration
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. -
Gather recent data. Query the database for sessions and iterations that haven't been reflected on yet (or all data if this is the first reflection):
SELECT s.id, s.intent, s.status, s.final_successful_prompt, s.reference_analysis, i.iteration_number, i.prompt, i.parameters, i.result_assessment, i.user_feedback, i.gap_analysis, i.success, i.what_worked, i.what_failed, i.action_type, i.parent_image FROM sessions s JOIN iterations i ON s.id = i.session_id WHERE s.reflected = 0 ORDER BY s.created_at DESC, i.iteration_number ASC -
Analyze successful iterations. Look for recurring themes in
what_worked:SELECT what_worked FROM iterations WHERE success = 1 AND what_worked IS NOT NULLParse the JSON arrays and count frequency of each technique across sessions.
-
Analyze failures. Look for recurring themes in
what_failedandgap_analysis:SELECT what_failed, gap_analysis FROM iterations WHERE (what_failed IS NOT NULL OR gap_analysis IS NOT NULL)
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 · 218 lines · 0 tokens per session scan A dd0375607f28
reflect 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 2,652 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
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.