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 skills add Bilal140202/the-lord-of-the-skills --skill glittercowboy__taches-cc-resourcesgit clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-skillsWrote 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/skills/bilal140202/the-lord-of-the-skills/glittercowboy__taches-cc-resources)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/glittercowboy__taches-cc-resources"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/glittercowboy__taches-cc-resources/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/skills/bilal140202/the-lord-of-the-skills/glittercowboy__taches-cc-resources"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/glittercowboy__taches-cc-resources.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 | $0.00044 | $0.02488 |
| Opus 5 | $0.00022 | $0.01244 |
| Sonnet 5 | $0.00009 | $0.00498 |
| Haiku 4.5 | $0.00004 | $0.00249 |
Grade B, and why
debug-like-expert scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
ls ~/.claude/skills/expertise/ 2>/dev/null | head -5 This is a copy
100% identical to debug-like-expert — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The skill emphasizes treating code you wrote with MORE skepticism than unfamiliar code, as cognitive biases about "how it should work" can blind you to actual implementation errors. Use scientific method to systematically identify root causes rather than applying quick fixes.
<context_scan> Run on every invocation to detect domain-specific debugging expertise:
# What files are we debugging?
echo "FILE_TYPES:"
find . -maxdepth 2 -type f 2>/dev/null | grep -E '\.(py|js|jsx|ts|tsx|rs|swift|c|cpp|go|java)$' | head -10
# Check for domain indicators
[ -f "package.json" ] && echo "DETECTED: JavaScript/Node project"
[ -f "Cargo.toml" ] && echo "DETECTED: Rust project"
[ -f "setup.py" ] || [ -f "pyproject.toml" ] && echo "DETECTED: Python project"
[ -f "*.xcodeproj" ] || [ -f "Package.swift" ] && echo "DETECTED: Swift/macOS project"
[ -f "go.mod" ] && echo "DETECTED: Go project"
# Scan for available domain expertise
echo "EXPERTISE_SKILLS:"
ls ~/.claude/skills/expertise/ 2>/dev/null | head -5
Present findings before starting investigation. </context_scan>
<domain_expertise>
Domain-specific expertise lives in ~/.claude/skills/expertise/
Domain skills contain comprehensive knowledge including debugging, testing, performance, and common pitfalls. Before investigation, determine if domain expertise should be loaded.
<scan_domains>
ls ~/.claude/skills/expertise/ 2>/dev/null
This reveals available domain expertise (e.g., macos-apps, iphone-apps, python-games, unity-games).
If no expertise skills found: Proceed without domain expertise (graceful degradation). The skill works fine with general debugging methodology. </scan_domains>
<inference_rules> If user's description or codebase contains domain keywords, INFER the domain:
| Keywords/Files | Domain Skill |
|---|---|
| "Python", "game", "pygame", ".py" + game loop | expertise/python-games |
| "React", "Next.js", ".jsx/.tsx" | expertise/nextjs-ecommerce |
| "Rust", "cargo", ".rs" files | expertise/rust-systems |
| "Swift", "macOS", ".swift" + AppKit/SwiftUI | expertise/macos-apps |
| "iOS", "iPhone", ".swift" + UIKit | expertise/iphone-apps |
| "Unity", ".cs" + Unity imports | expertise/unity-games |
| "SuperCollider", ".sc", ".scd" | expertise/supercollider |
| "Agent SDK", "claude-agent" | expertise/with-agent-sdk |
If domain inferred, confirm:
Detected: [domain] issue → expertise/[skill-name]
Load this debugging expertise? (Y / see other options / none)
</inference_rules>
<no_inference> If no domain obvious, present options:
What type of project are you debugging?
Available domain expertise:
1. macos-apps - macOS Swift (SwiftUI, AppKit, debugging, testing)
2. iphone-apps - iOS Swift (UIKit, debugging, performance)
3. python-games - Python games (Pygame, physics, performance)
4. unity-games - Unity (C#, debugging, optimization)
[... any others found in build/]
N. None - proceed with general debugging methodology
C. Create domain expertise for this domain
Select:
</no_inference>
<load_domain> When domain selected, READ all references from that skill:
cat ~/.claude/skills/expertise/[domain]/references/*.md 2>/dev/null
This loads comprehensive domain knowledge BEFORE investigation:
- Common issues and error patterns
- Domain-specific debugging tools and techniques
- Testing and verification approaches
- Performance profiling and optimization
- Known pitfalls and anti-patterns
- Platform-specific considerations
Announce: "Loaded [domain] expertise. Investigating with domain-specific context."
If domain skill not found: Inform user and offer to proceed with general methodology or create the expertise. </load_domain>
<when_to_load> Domain expertise should be loaded BEFORE investigation when domain is known.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 310 lines · 44 tokens per session scan B 89e3feb89745
debug-like-expert is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (4 stars, last pushed 6d ago), licensed MIT. It adds 44 tokens to every session and 2,488 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (enumerates other installed skills). It is 100% identical to debug-like-expert, differing in 0 lines, and is treated as a copy.
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