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 ArabelaTso/Skills-4-SE --skill code-summarizergit clone --depth 1 https://github.com/ArabelaTso/Skills-4-SEWrote 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/arabelatso/skills-4-se/code-summarizer)<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/code-summarizer"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/code-summarizer/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/arabelatso/skills-4-se/code-summarizer"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/code-summarizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00075 | $0.01775 |
| Opus 5.5 | $0.00030 | $0.00710 |
| Sonnet 5.5 | $0.00015 | $0.00355 |
| Haiku 4.5 | $0.00007 | $0.00178 |
Grade A, and why
code-summarizer 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 — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Summarizer
Generate clear, concise summaries of source code at any scale - from individual functions to entire codebases.
Overview
This skill helps analyze and summarize code by adapting the level of detail to the code's scale:
- Small-scale code (functions, classes, small files): Provide focused summaries of intention and implementation
- Large-scale code (modules, packages, entire repositories): Provide hierarchical summaries with progressive drill-down
Workflow Decision Tree
User provides code → Assess scale
├─ Small-scale (< 200 lines, single file/function)
│ └─ Generate focused summary
│
└─ Large-scale (> 200 lines, multiple files/modules)
├─ Generate high-level overview
├─ List main modules/components
└─ Prompt user to select specific parts for detailed analysis
Small-Scale Code Summarization
For functions, classes, or small files (typically < 200 lines), provide a focused summary that includes:
Summary Structure
-
Purpose Statement (1-2 sentences)
- What does this code do?
- What problem does it solve?
-
Core Logic (2-4 bullet points)
- Key algorithms or approaches used
- Important data transformations
- Critical control flow decisions
-
Key Details
- Input parameters and their purposes
- Return values and their meaning
- Important side effects or state changes
- Dependencies on external libraries or modules
-
Notable Patterns (if applicable)
- Design patterns used
- Optimization techniques
- Error handling approaches
Example Format
## Summary
**Purpose**: This function validates user email addresses and normalizes them to lowercase format before database storage.
**Core Logic**:
- Uses regex pattern matching to validate email format (RFC 5322 compliant)
- Strips whitespace and converts to lowercase for consistency
- Checks against a blocklist of disposable email domains
- Logs validation failures for security monitoring
**Key Details**:
- Input: `email` (string) - raw email address from user input
- Returns: `normalized_email` (string) or raises `ValidationError`
- Side effect: Logs to `security.log` on validation failure
- Dependencies: `re`, `logging`, custom `EmailBlocklist` class
**Notable Patterns**:
- Uses early return pattern for validation failures
- Implements defensive programming with input sanitization
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 · 250 lines · 75 tokens per session scan A 9232fa363303
code-summarizer is a skill published in the GitHub repository ArabelaTso/Skills-4-SE (258 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 75 tokens to every session and 1,775 once invoked, about $0.0003 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.
Other skills, from other repositories
teach
Teach the user a new skill or concept, within this workspace.
skill-code-quality
This skill should be used when implementing code quality standards, setting up linting/testing/security scans, enforcing coverage thresholds, or establishing CI/CD guardrails.
skill-pr-review
Comprehensive code review for pull requests using parallel multi-agent analysis. Audits CLAUDE.md compliance, checks for bugs, analyzes git history, reviews comments, and filters by confidence score. Use when reviewing a GitHub PR, mentions "code review", "review this PR", or "/code-review".
improve-codebase-architecture
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
skill-developer-relations
SDK development, external developer communication, code samples, API client libraries, community engagement, and developer experience. Use when creating SDKs, writing external guides, creating code samples, or improving developer experience.
diagnose
Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.