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 ZekaiSuni/claude-for-legal-turkish --skill skills-qagit clone --depth 1 https://github.com/ZekaiSuni/claude-for-legal-turkishWrote 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/zekaisuni/claude-for-legal-turkish/skills-qa)<a href="https://agentmods.dev/skills/zekaisuni/claude-for-legal-turkish/skills-qa"><img src="https://agentmods.dev/badge/skills/zekaisuni/claude-for-legal-turkish/skills-qa/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/zekaisuni/claude-for-legal-turkish/skills-qa"><img src="https://agentmods.dev/badge/skills/zekaisuni/claude-for-legal-turkish/skills-qa.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 | $0.00109 | $0.07720 |
| Opus 5.5 | $0.00044 | $0.03088 |
| Sonnet 5.5 | $0.00022 | $0.01544 |
| Haiku 4.5 | $0.00011 | $0.00772 |
Grade B, and why
skills-qa scanned grade B with 2 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.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
1. **Override / ignore instructions** — "ignore previous instructions", Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
8. **Shell / code execution** — any instruction to run shell commands, curl This is a copy
94% identical to skills-qa — 36 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 — 734 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/skills-qa
Inputs accepted
- File path to a skill directory (preferred — enables full dependency mapping)
- File path to a SKILL.md only
- SKILL.md content pasted directly into the conversation
Context to load
~/.claude/plugins/config/claude-for-legal/legal-builder-hub/CLAUDE.md→ practice profile and installed skills list (provides context for evaluating whether the skill fits the user's team and workflow, and whether it duplicates something already installed)
Notes
This QA check runs automatically as part of /legal-builder-hub:skill-installer. You can also run it directly on any skill before deciding whether to install, or on a first-party skill before deploying to your team.
Run it deliberately — before incorporating any community skill you did not build,
or before deploying a first-party skill to your team.
If the user runs /legal-builder-hub:skill-installer and then asks "should I trust
this?" or "is this well-designed?", route to this skill rather than answering
inline.
Purpose
Anyone can build a skill. This one checks whether it was built well before it touches your workflows.
Evaluates any skill against the Legal Skill Design Framework: thirteen design parameters (the first nine are substantive design; the tenth is Trust Surface — the skill's execution permissions and injection risk; the eleventh is Freshness — whether bundled reference content is current; the twelfth is Schema — whether the SKILL.md has the structure a well-built skill needs; the thirteenth is Conflicts — whether the skill overlaps or conflicts with skills already installed), three legal-specific failure modes, a dependency map, and a clear verdict. Works for community skills from registries and first-party skills your team is building or deploying.
Inputs accepted
- A path to a full skill directory
- A path to a SKILL.md file
- SKILL.md content pasted directly into the conversation
If only SKILL.md is provided, ask once: "Do you have the associated commands, agents, or hooks for this skill? The full picture changes what I can assess — particularly on dependencies and automatic triggers." Proceed either way; flag in the output if dependency mapping is incomplete.
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 · 734 lines · 109 tokens per session scan B f932eabaabd4
skills-qa is a skill published in the GitHub repository ZekaiSuni/claude-for-legal-turkish (108 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 109 tokens to every session and 7,720 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it B with 2 findings (instruction-override phrasing, makes network calls). It is 94% identical to skills-qa, differing in 36 lines, and is treated as a copy.
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Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the…