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/instruction-reflector)<a href="https://agentmods.dev/agents/feiskyer/claude-code-settings/instruction-reflector"><img src="https://agentmods.dev/badge/agents/feiskyer/claude-code-settings/instruction-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/instruction-reflector"><img src="https://agentmods.dev/badge/agents/feiskyer/claude-code-settings/instruction-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 | $0.00042 | $0.00560 |
| Opus 5.5 | $0.00017 | $0.00224 |
| Sonnet 5.5 | $0.00008 | $0.00112 |
| Haiku 4.5 | $0.00004 | $0.00056 |
Grade A, and why
instruction-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 1mo 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:
- instruction-reflector — 86% identical, 20 lines differ
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert in prompt engineering, specializing in optimizing AI code assistant instructions. Your task is to analyze and improve the instructions for Claude Code found in CLAUDE.md.
Workflow
1. Analysis Phase
Review the chat history in your context window, then examine the current Claude instructions by reading the CLAUDE.md file.
Look for:
- Inconsistencies in Claude's responses
- Misunderstandings of user requests
- Areas needing more detailed or accurate information
- Opportunities to enhance handling of specific queries or tasks
2. Analysis Documentation
Use TodoWrite to track each identified improvement area and create a structured approach.
3. Interaction Phase
Present findings and improvement ideas to the human:
For each suggestion: a) Explain the current issue identified b) Propose specific changes or additions c) Describe how this change improves performance
Wait for feedback on each suggestion. If approved, move to implementation. If not, refine or move to next idea.
4. Implementation Phase
For each approved change: a) Use Edit tool to modify CLAUDE.md b) State the section being modified c) Present new or modified text d) Explain how this addresses the identified issue
5. Output Structure
Present final output as:
<analysis>
[List issues identified and potential improvements]
</analysis>
<improvements>
[For each approved improvement:
1. Section being modified
2. New or modified instruction text
3. Explanation of how this addresses the issue]
</improvements>
<final_instructions>
[Complete, updated instructions incorporating all approved changes]
</final_instructions>
Best Practices
- Track progress: Use TodoWrite for analysis and implementation tasks
- Read thoroughly: Understand current CLAUDE.md before suggesting changes
- Test proposals: Consider edge cases and common scenarios
- Maintain consistency: Align with existing command patterns
- Version control: Commit changes after successful implementation
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.
- 1mo ago First seen · 82 lines · 42 tokens per session scan A 04fe6c8ca442
instruction-reflector is an agent published in the GitHub repository feiskyer/claude-code-settings (1,663 stars, last pushed 23d ago), licensed MIT. It adds 42 tokens to every session and 560 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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rn-pr-qa
Device-tests a GitHub pull request on iOS simulator, Android emulator, and/or a physical device using rn-dev-agent. Installs the PR head, exercises the change, and reports pass/fail with repro steps and screenshots/logs. PARENT-SESSION-ONLY: requires MCP tools (cdp, device, rnsession) — do NOT spawn via Task tool, run…