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 zekiriabd/SDD-Pro --skill codeqlgit clone --depth 1 https://github.com/zekiriabd/SDD-ProWrote 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/zekiriabd/sdd-pro/codeql)<a href="https://agentmods.dev/skills/zekiriabd/sdd-pro/codeql"><img src="https://agentmods.dev/badge/skills/zekiriabd/sdd-pro/codeql.svg" alt="Measured on agentmods" 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 | $0.00102 | $0.03578 |
| Opus 5 | $0.00051 | $0.01789 |
| Sonnet 5 | $0.00020 | $0.00716 |
| Haiku 4.5 | $0.00010 | $0.00358 |
Grade A, and why
codeql 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 3d 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.
This is a copy
89% identical to codeql — 12 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 — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CodeQL Analysis
Supported languages: Python, JavaScript/TypeScript, Go, Java/Kotlin, C/C++, C#, Ruby, Swift.
Skill resources: Reference files and templates are located at {baseDir}/references/ and {baseDir}/workflows/.
Essential Principles
-
Database quality is non-negotiable. A database that builds is not automatically good. Always run quality assessment (file counts, baseline LoC, extractor errors) and compare against expected source files. A cached build produces zero useful extraction.
-
Data extensions catch what CodeQL misses. Even projects using standard frameworks (Django, Spring, Express) have custom wrappers around database calls, request parsing, or shell execution. Skipping the create-data-extensions workflow means missing vulnerabilities in project-specific code paths.
-
Explicit suite references prevent silent query dropping. Never pass pack names directly to
codeql database analyze— each pack'sdefaultSuiteFileapplies hidden filters that can produce zero results. Always generate a custom.qlssuite file. -
Zero findings needs investigation, not celebration. Zero results can indicate poor database quality, missing models, wrong query packs, or silent suite filtering. Investigate before reporting clean.
-
macOS Apple Silicon requires workarounds for compiled languages. Exit code 137 is
arm64e/arm64mismatch, not a build failure. Try Homebrew arm64 tools or Rosetta before falling back tobuild-mode=none. -
Follow workflows step by step. Once a workflow is selected, execute it step by step without skipping phases. Each phase gates the next — skipping quality assessment or data extensions leads to incomplete analysis.
Output Directory
All generated files (database, build logs, diagnostics, extensions, results) are stored in a single output directory.
- If the user specifies an output directory in their prompt, use it as
OUTPUT_DIR. - If not specified, default to
./static_analysis_codeql_1. If that already exists, increment to_2,_3, etc.
What ships with it
18 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.
- agents/openai.yaml 128 B
- assets/trail-of-bits-mark.svg 3.0 KB
- LICENSE-CC-BY-SA-4.0.txt 20 KB
- references/build-fixes.md 2.6 KB
- references/diagnostic-query-templates.md 9.8 KB
- references/extension-yaml-format.md 7.7 KB
- references/important-only-suite.md 5.6 KB
- references/language-details.md 5.0 KB
- references/macos-arm64e-workaround.md 6.7 KB
- references/performance-tuning.md 3.3 KB
- references/quality-assessment.md 6.8 KB
- references/ruleset-catalog.md 2.1 KB
- references/run-all-suite.md 4.2 KB
- references/sarif-processing.md 3.0 KB
- references/threat-models.md 3.0 KB
- workflows/build-database.md 9.4 KB
- workflows/create-data-extensions.md 9.7 KB
- workflows/run-analysis.md 11 KB
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.
- 3d ago First seen · 270 lines · 102 tokens per session scan A b88b4f0b2044
codeql is a skill published in the GitHub repository zekiriabd/SDD-Pro (191 stars, last pushed 4d ago), licensed Apache-2.0. It adds 102 tokens to every session and 3,578 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to codeql, differing in 12 lines, and is treated as a copy.
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