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 clearancegit 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/clearance)<a href="https://agentmods.dev/skills/zekaisuni/claude-for-legal-turkish/clearance"><img src="https://agentmods.dev/badge/skills/zekaisuni/claude-for-legal-turkish/clearance/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/clearance"><img src="https://agentmods.dev/badge/skills/zekaisuni/claude-for-legal-turkish/clearance.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 | $0.00069 | $0.06451 |
| Opus 5.5 | $0.00028 | $0.02580 |
| Sonnet 5.5 | $0.00014 | $0.01290 |
| Haiku 4.5 | $0.00007 | $0.00645 |
Grade A, and why
clearance 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.
This is a copy
86% identical to clearance — 28 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 — 498 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/clearance
This is a triage, not a clearance opinion. A trademark clearance opinion requires a full professional search and registered trademark counsel's judgment. A "no obvious conflicts" result means the triage didn't find anything — it does not mean the mark is clear. Clients have been sued over marks that passed a knockout search.
Instructions
- Read
~/.claude/plugins/config/claude-for-legal/ip-legal/CLAUDE.md. If it contains[PLACEHOLDER], stop and direct to/ip-legal:cold-start-interview. - Follow the workflow below.
- Run intake (mark, goods/services, classes, jurisdictions, visual/stylization).
- Knockout check for intrinsic bars — generic, descriptive, deceptive, geographic, surname, false connection, prohibited matter, functional.
- Similar-marks search against what's connected (Solve Intelligence, CourtListener, Descrybe, or whatever MCP is available). If nothing is connected, say so in the output and proceed with the factor analysis only.
- Walk the applicable SMK m.7/m.29 criteria for likelihood-of-confusion factors — Polaroid / Yargıtay / other. Flag each; never conclude.
- Write the triage memo to the matter folder (if a matter is active) or the practice outputs folder. Apply the work-product header per role.
- End with recommended next steps and the non-lawyer gate if the role is non-lawyer.
This skill never concludes a mark is clear. If uncertain, flag — the attorney decides.
Examples
/ip-legal:clearance "APEXLEAF for an outdoor apparel line, planned launch US + EU"
/ip-legal:clearance
(And the skill will ask for the mark, goods, classes, and jurisdictions.)
THIS IS A FIRST PASS, NOT A CLEARANCE OPINION
Say this at the top of every output. Do not drop it. Do not soften it.
This is a first pass, not a clearance opinion. A trademark clearance opinion requires a full professional search (TESS, state registries, common law sources, international registries, domain and social, trade dress and design marks where relevant) and attorney judgment on likelihood of confusion, which depends on factors a structured triage cannot fully assess. A "no obvious conflicts" result from this skill means the triage didn't find anything — it does not mean the mark is clear. Clients have been sued over marks that passed a knockout search. A registered trademark attorney evaluates before anyone adopts, files, or invests in this mark.
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 · 498 lines · 69 tokens per session scan A b507adf447e3
clearance 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 69 tokens to every session and 6,451 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to clearance, differing in 28 lines, and is treated as a copy.
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terms-of-service
Draft terms of service and a companion privacy notice matched to the product's form and feature modules, not legal advice, delivered as a Word document.
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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…