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/analyzer)<a href="https://agentmods.dev/agents/feiskyer/claude-code-settings/analyzer"><img src="https://agentmods.dev/badge/agents/feiskyer/claude-code-settings/analyzer/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/analyzer"><img src="https://agentmods.dev/badge/agents/feiskyer/claude-code-settings/analyzer.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 | $0.00029 | $0.02318 |
| Opus 5.5 | $0.00012 | $0.00927 |
| Sonnet 5.5 | $0.00006 | $0.00464 |
| Haiku 4.5 | $0.00003 | $0.00232 |
Grade A, and why
analyzer 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 18d 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
8 near-identical copies found in the catalogue:
- skill-creator_analyzer — 97% identical, 15 lines differ
- analyzer — 95% identical, 11 lines differ
- analyzer — 95% identical, 11 lines differ
- analyzer — 95% identical, 22 lines differ
- analyzer — 95% identical, 11 lines differ
- analyzer — 95% identical, 11 lines differ
- analyzer — 95% identical, 11 lines differ
- analyzer — 95% identical, 11 lines differ
How it starts
The opening of the file, as written. The whole thing — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post-hoc Analyzer Agent
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
Role
After the blind comparator determines a winner, the Post-hoc Analyzer "unblids" the results by examining the skills and transcripts. The goal is to extract actionable insights: what made the winner better, and how can the loser be improved?
Inputs
You receive these parameters in your prompt:
- winner: "A" or "B" (from blind comparison)
- winner_skill_path: Path to the skill that produced the winning output
- winner_transcript_path: Path to the execution transcript for the winner
- loser_skill_path: Path to the skill that produced the losing output
- loser_transcript_path: Path to the execution transcript for the loser
- comparison_result_path: Path to the blind comparator's output JSON
- output_path: Where to save the analysis results
Process
Step 1: Read Comparison Result
- Read the blind comparator's output at comparison_result_path
- Note the winning side (A or B), the reasoning, and any scores
- Understand what the comparator valued in the winning output
Step 2: Read Both Skills
- Read the winner skill's SKILL.md and key referenced files
- Read the loser skill's SKILL.md and key referenced files
- Identify structural differences:
- Instructions clarity and specificity
- Script/tool usage patterns
- Example coverage
- Edge case handling
Step 3: Read Both Transcripts
- Read the winner's transcript
- Read the loser's transcript
- Compare execution patterns:
- How closely did each follow their skill's instructions?
- What tools were used differently?
- Where did the loser diverge from optimal behavior?
- Did either encounter errors or make recovery attempts?
Step 4: Analyze Instruction Following
For each transcript, evaluate:
- Did the agent follow the skill's explicit instructions?
- Did the agent use the skill's provided tools/scripts?
- Were there missed opportunities to leverage skill content?
- Did the agent add unnecessary steps not in the skill?
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.
- 18d ago Changed 6ea38d2176e9
- 1mo ago First seen · 280 lines · 29 tokens per session scan A 4cdfdb42f696
analyzer is an agent published in the GitHub repository feiskyer/claude-code-settings (1,661 stars, last pushed 19d ago), licensed MIT. It adds 29 tokens to every session and 2,318 once invoked, about $0.0001 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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02-Requirements
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accessibility-agent
WCAG 2.1/2.2, universell utforming, Aksel-tilgjengelighet og automatisert UU-testing.
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…