Claude Code Skills & Agents is a collection of reusable skills, sub-agents, and configuration templates that extend Claude Code with workflows such as research, image generation, translation, and GitHub automation. It is for Claude Code users who want to install these capabilities individually or as a marketplace plugin. The catalogue entries are components from this collection.
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.
git clone --depth 1 https://github.com/feiskyer/claude-code-settingsWrote 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/agents/feiskyer/claude-code-settings/deep-reflector)<a href="https://agentmods.dev/agents/feiskyer/claude-code-settings/deep-reflector"><img src="https://agentmods.dev/badge/agents/feiskyer/claude-code-settings/deep-reflector/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/agents/feiskyer/claude-code-settings/deep-reflector"><img src="https://agentmods.dev/badge/agents/feiskyer/claude-code-settings/deep-reflector.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 | $0.00040 | $0.00523 |
| Opus 5.5 | $0.00016 | $0.00209 |
| Sonnet 5.5 | $0.00008 | $0.00105 |
| Haiku 4.5 | $0.00004 | $0.00052 |
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 22d 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, 40 lines differ
How it starts
The opening of the file, as written. The whole thing — 107 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.
- 22d ago Changed · -8 lines 11a3ba42e524
- 1mo 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 (1,663 stars, last pushed 23d ago), licensed MIT. It adds 40 tokens to every session and 523 once invoked, about $0.0002 per session on Opus 5.5. 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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intuition-oracle
Use this agent as the single queryable entry point to the Shadow Mind — the team's parallel non-invasive cognitive layer. When any agent emits [NEXUS:INTUIT ], the oracle reads the Shadow Mind's Pattern Library, observations, speculations, and dreams (all READ-ONLY) and returns a structured, probabilistic…
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