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 semantic-szz-analyzergit 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/semantic-szz-analyzer)<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/semantic-szz-analyzer"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/semantic-szz-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/skills/arabelatso/skills-4-se/semantic-szz-analyzer"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/semantic-szz-analyzer.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 | $0.00096 | $0.01273 |
| Opus 5 | $0.00048 | $0.00636 |
| Sonnet 5 | $0.00019 | $0.00255 |
| Haiku 4.5 | $0.00010 | $0.00127 |
Grade A, and why
semantic-szz-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 8d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semantic SZZ Analyzer
Overview
Semantic SZZ Analyzer extends the traditional SZZ (Sliwerski-Zimmermann-Zeller) algorithm by incorporating semantic analysis to identify bug-introducing commits more accurately. It distinguishes actual semantic changes from refactorings or code movements by analyzing control-flow and data-flow similarity across versions.
Core Capabilities
1. Semantic Change Detection
Analyze commits to distinguish between:
- Semantic changes: Modifications that alter program behavior
- Refactorings: Code restructuring without behavior changes
- Code movements: Relocations of code blocks without semantic impact
Use control-flow graphs (CFG) and data-flow analysis to compute similarity between code versions.
2. Bug-Introducing Commit Identification
Given a bug-fix commit, trace back through git history to identify the commit that introduced the bug:
- Extract changed lines from the bug-fix commit
- Use
git blameto find commits that last modified those lines - Apply semantic analysis to filter out false positives
- Rank candidates by semantic similarity and temporal proximity
3. False Positive Reduction
Traditional SZZ produces many false positives due to:
- Whitespace changes
- Comment modifications
- Import reorganization
- Variable renaming
- Code formatting
Semantic SZZ filters these by analyzing AST (Abstract Syntax Tree) structure and semantic equivalence.
Workflow
Step 1: Analyze Bug-Fix Commit
Start by identifying the bug-fix commit. Look for:
- Commits with keywords: "fix", "bug", "issue", "patch", "resolve"
- Commits linked to issue trackers
- Commits explicitly marked as fixes
Extract the changed lines and affected files.
Step 2: Identify Candidate Commits
Use git blame or git log -L to trace the history of changed lines:
git blame -L <start>,<end> <file> <bug-fix-commit>^
This identifies commits that last modified the buggy lines before the fix.
Step 3: Apply Semantic Analysis
What ships with it
8 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.
- 8d ago First seen · 172 lines · 96 tokens per session scan A 9de1f003cc68
semantic-szz-analyzer is a skill published in the GitHub repository ArabelaTso/Skills-4-SE (252 stars, last pushed 21d ago), licensed Apache-2.0. It adds 96 tokens to every session and 1,273 once invoked, about $0.0005 per session on Opus 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 2026-09-03.
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