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 Dqz00116/skill-lib --skill code-analysisgit clone --depth 1 https://github.com/Dqz00116/skill-libWrote 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/dqz00116/skill-lib/code-analysis)<a href="https://agentmods.dev/skills/dqz00116/skill-lib/code-analysis"><img src="https://agentmods.dev/badge/skills/dqz00116/skill-lib/code-analysis/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/dqz00116/skill-lib/code-analysis"><img src="https://agentmods.dev/badge/skills/dqz00116/skill-lib/code-analysis.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 | $0.00019 | $0.02496 |
| Opus 5.5 | $0.00008 | $0.00998 |
| Sonnet 5.5 | $0.00004 | $0.00499 |
| Haiku 4.5 | $0.00002 | $0.00250 |
Grade A, and why
code-analysis 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.
How it starts
The opening of the file, as written. The whole thing — 368 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Analysis Skill
Overview
Standardized workflow for reading, analyzing, and documenting codebases with attention-driven focus.
When to Use
Use this skill when you need to:
- Understand a new code module or system
- Analyze architecture and design patterns
- Document technical implementation details
- Prepare for code refactoring or integration
- Create technical documentation for teams
- Focus on core components (high-attention code analysis)
Attention-Driven Code Analysis (NEW in v1.2)
Identify core components in code using heuristic rules, optimizing analysis focus.
Attention Scoring System
# Use the attention_focus.py module
from attention_focus import CodeAttentionScorer
scorer = CodeAttentionScorer()
components = scorer.analyze_code_structure(file_content, file_name)
focus = scorer.get_analysis_focus(components)
Scoring Criteria
| Factor | Weight | Description |
|---|---|---|
| Core Keywords | +3 | Manager, Controller, Handler, System, Core |
| Important Keywords | +2 | Helper, Util, Factory, Provider |
| Lines of Code | +2 (>100 lines) / +1 (50-100 lines) | Scale metric |
| Reference Count | +2 (>5 refs) / +1 (2-5 refs) | Dependency metric |
| Complexity | +1 | Methods>10 or Conditional statements>5 |
Attention Levels
| Level | Score | Analysis Depth |
|---|---|---|
| High | 8-10 | Detailed analysis + Full code + Design principles |
| Medium | 5-7 | Focused analysis + Key code snippets |
| Low | 0-4 | Brief mention + Function description |
Workflow with Attention Focus
1. Read code file
↓
2. Run attention_focus.py analysis
↓
3. Get prioritized component list
↓
4. Analyze HIGH attention components in detail
5. Analyze MEDIUM attention components briefly
6. Reference LOW attention components as needed
↓
7. Generate focused documentation
Benefits
- Reduce token consumption: Focus on 20% core code that provides 80% value
- Faster analysis: Skip boilerplate and utility code
- Better documentation: Highlight architectural decisions and critical paths
- Estimated improvement: 20-30% token reduction, 30% faster analysis
What ships with it
1 file 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.
- 1mo ago First seen · 368 lines · 19 tokens per session scan A 888fdc1fa5a9
code-analysis is a skill published in the GitHub repository Dqz00116/skill-lib (22 stars, last pushed 4mo ago), licensed MIT. It adds 19 tokens to every session and 2,496 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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