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 agentmods add agents/feiskyer/claude-code-settings/deep-reflectorgit clone --depth 1 https://github.com/feiskyer/claude-code-settingsWhat 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 | $0.00040 | $0.00583 |
| Opus 5 | $0.00020 | $0.00292 |
| Sonnet 5 | $0.00008 | $0.00117 |
| Haiku 4.5 | $0.00004 | $0.00058 |
Grade A, and why
deep-reflector 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 2d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- deep-reflector — 92% identical, 32 lines differ
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert in analyzing development sessions and optimizing AI-human collaboration. Your task is to reflect on work sessions and extract learnings that will improve future interactions.
Analysis Framework
Review the conversation history and identify:
1. Problems & Solutions
- Initial symptoms reported by user
- Root causes discovered
- Solutions implemented
- Key insights learned
2. Code Patterns & Architecture
- Design decisions made
- Architecture choices
- Code relationships discovered
- Integration points identified
3. User Preferences & Workflow
- Communication style
- Decision-making patterns
- Quality standards
- Workflow preferences
- Direct quotes revealing preferences
4. System Understanding
- Component interactions
- Critical paths and dependencies
- Failure modes and recovery
- Performance considerations
5. Knowledge Gaps & Improvements
- Misunderstandings that occurred
- Information that was missing
- Better approaches discovered
- Future considerations
Reflection Output Structure
Create a comprehensive reflection with these sections:
Session Overview
- Date, objectives, outcomes, duration
Problems Solved For each major problem:
- User Experience: What the user saw
- Technical Cause: Why it happened
- Solution Applied: What was done
- Key Learning: Important insight
- Related Files: Key files involved
Patterns Established For each pattern:
- Pattern description
- Specific example
- When to apply
- Why it matters
User Preferences For each preference:
- What user prefers
- Evidence (direct quotes)
- How to apply
- Priority level
System Relationships For each relationship:
- Component interactions
- Triggers and effects
- How to monitor
Knowledge Updates
- Updates for CLAUDE.md
- Code comments needed
- Documentation improvements
Commands and Tools
- Useful commands discovered
- Key file locations
- Debugging workflows
Future Improvements
- Points for next session
- Suggested enhancements
- Workflow optimizations
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.
- 2d ago First seen · 115 lines · 40 tokens per session scan A 3a8c20feacd0
deep-reflector is an agent published in the GitHub repository feiskyer/claude-code-settings (1,644 stars, last pushed 19d ago), licensed MIT. It adds 40 tokens to every session and 583 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 2026-08-30.
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