lq-ai

A synthetic skill used to test C5 tool_usage surfacing; not for production use.

Use when the user asks to find, read, or cite U.S. case law on a question — locating controlling or persuasive decisions via CourtListener, reading the opinion text, and grounding any statement in what the source actually says.

Use when the user wants to compare the same handful of terms across N contracts side-by-side in a grid — what is the term, survival period, carveouts, and governing law in each of these 5 NDAs? Returns a row-per-document × column-per-question grid with citations per cell. Reference skill for the M3-C output_format - table mode; intended as a starting point for operators to fork and tune for their own contract types.

Use when the user wants to compare the substantive commercial terms across N master services agreements side-by-side — term and renewal, payment terms, limitation of liability, and indemnification posture across each agreement. Returns a row-per-document × column-per-question grid with citations per cell. MSA-tuned reference skill for the M3-C `output_format - table` mode; intended as a fork-and-tune starting point for operators reviewing MSA portfolios.

Use when the user wants to compare the substantive NDA-specific terms across N non-disclosure agreements side-by-side — the definition of Confidential Information, permitted recipients, return/destruction obligation, and remedies clause across each agreement. Returns a row-per-document × column-per-question grid with citations per cell. NDA-tuned reference skill for the M3-C `output_format - table` mode; intended as a fork-and-tune starting point for operators reviewing NDA portfolios.

Use when extracting the negotiated positions from a single prior contract as input to the Easy Playbook auto-generation pipeline. Reads a contract's text, identifies the clauses that take a substantive position on a contract issue (definition of confidential information, limitation of liability, indemnification, payment terms, etc.), and emits a structured list of {issue, clause_text, source_offsets} for downstream clustering. Output is intermediate; the in-house attorney evaluates the final assembled playbook, not this stage.

A synthetic skill used by C1 loader tests; not for production use.

Minimal-frontmatter test fixture (no lq_ai namespace).

Test fixture used to exercise the tag filter.

This frontmatter has invalid YAML.

Synthetic well-formed skill used alongside a malformed sibling.

Frontmatter name doesn't match the folder name; loader should skip.

Use when the user provides a client alert, regulatory bulletin, law firm memo, or similar legal update and wants the time-sensitive action items, deadlines, and obligations extracted into a checklist organized by deadline. Distinguishes mandatory action items (effective dates of new requirements) from recommended action items (best practices) and informational items (context only). Produces a brief context summary plus deadline-organized checklist with citations to the source alert.

Use when the user has a piece of legal-jargon-heavy text and wants it rewritten in plain language for a specified non-legal audience. Common scenarios — email to a business stakeholder, legal disclaimer for a customer-facing page, memo for a non-legal audience, explanation of a contract clause for a deal team, summary of a regulatory development for an executive briefing. Produces the rewritten text plus a brief explanation of what was changed and why, including any preservation-of-meaning concerns where the rewrite required interpretation. Does not draft from scratch; the input is text that exists.

Use when the user has a contract loaded and asks a specific question about it — what a clause means, where something is addressed, whether a term is unusual, how a provision compares to standard practice, or what would happen in a specified scenario under the contract. Produces an answer calibrated to the question's shape (direct answer for fact lookups, paragraph with context for "is this unusual" questions, structured findings for multi-issue questions), with verbatim citations to specific clauses. Does not perform full reviews — for that, use the appropriate review skill (NDA Review, DPA Checklist Review, MSA Review, etc.).

Use when the user provides a Data Processing Agreement, Data Processing Addendum, or HIPAA Business Associate Agreement and asks whether it contains the terms required under the applicable data-protection regime. Produces a structured checklist scoring each required term as present, partial, missing, or unclear, with clause references and recommended language for any gaps. Supports GDPR Article 28, US state privacy laws (CCPA/CPRA, VCDPA, CPA, CTDPA, and similar), HIPAA BAAs, and general commercial DPAs without a specified regime.

Use when the user has typed a short or vague prompt and the system is configured to expand prompts before submission, or when the user explicitly invokes "Enhance Prompt" or asks the system to "improve this prompt before sending." Rewrites the user's input into a structured legal prompt with role, jurisdiction, task, constraints, and output format made explicit, and returns the expansion alongside a brief reasoning section so the user can review, edit, or skip before the expanded prompt is submitted to the model.

Use when the user uploads or pastes a Master Purchase Agreement, Master Supply Agreement, Master Goods Agreement, or commercial purchase MSA covering the procurement of physical goods, equipment, components, or non-software services and asks for review, redline, risk assessment, or recommendation on whether to sign. Conducts a structured review of MSA framework terms (price and payment, delivery and acceptance, warranties, indemnification, liability, IP, term and termination, force majeure, supply continuity, and others), calibrated to the user's perspective (buyer or supplier), with severity-rated findings, redline language for material gaps, and clause-level citations.

Use when the user uploads or pastes a Software-as-a-Service Master Services Agreement (MSA), Master Subscription Agreement, SaaS Agreement, or Cloud Services Agreement and asks for review, redline, risk assessment, or recommendation on whether to sign. Conducts a structured review of MSA framework terms (liability, indemnification, IP, data protection, warranties, term and termination, payment, and others), calibrated to the user's perspective (vendor or customer), with severity-rated findings, redline language for material gaps, and clause-level citations. Optionally surfaces conflicts between the MSA and a provided Order Form or SOW.

Use when the user uploads or pastes a non-disclosure agreement and asks for review, redline, risk assessment, or a recommendation on whether to sign. Identifies missing standard protections, one-sided or unusual provisions, and operational issues; produces a structured report with severity ratings and citations to specific clauses, calibrated to the user's perspective (discloser, recipient, or mutual).

Use when the user wants to create a new LQ.AI skill, turn a chat into a reusable skill, improve an existing skill, or asks "how do I build a skill that does X." Conducts a focused conversation to elicit what the skill should do, when it should trigger, what inputs and outputs it needs, and what edge cases matter, then produces a complete skill folder ready to save.

Use when the user has a vendor's published privacy policy (URL, PDF, or pasted text) and wants a fast triage assessment to decide whether deeper diligence is warranted. Produces a short structured report covering what the policy says about key data practices (collection, use, sharing, retention, transfers, rights) plus identification of red flags that warrant escalation to deeper review. Explicitly a first pass, not a full privacy assessment, DPA negotiation, or compliance certification.