matter-intake-scoping-scott-margetts
Matter scoping across the full pre-execution arc — organise client data into a structured brief, capture the agreed baseline, or reconstruct scope mid-flight. Use when making sense of client information before a proposal, scoping a new matter, running a kickoff, defining scope, mapping stakeholders, or inheriting a matter mid-flight. Trigger on: 'make sense of this', 'structure this for the proposal', 'scope this matter', 'new matter', 'kickoff', 'what are we doing', 'who are the stakeholders', 'what does success look like', 'matter setup', 'intake', 'I've inherited this matter', 'organise thi
npx skills add lawve-ai/awesome-legal-skills --skill matter-intake-scoping-scott-margetts --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
The skill organises unstructured client information into a structured brief and establishes a baseline for matter scope. It operates across four modes: pre-engagement data structuring, quick intake, full intake, and mid-matter recovery. It outputs a .docx matter record, not plain text, containing the pre-engagement brief, open questions, conflicts, and data points with source attribution and confidence levels. It identifies inputs from emails, org charts, prior files, data room indices, and commercial documents, and applies a source-attributed confidence framework with four levels. It surfaces conflicts without resolving them and flags external knowledge gaps using shared-knowledge files and standing assumptions. It calibrates assumptions against standing-assumptions.md and finally drafts a pre-engagement client brief with sections for conflicts, open questions, knowledge flags, matter context, extracted data points, assumptions, and suggested next steps. If requested, it can produce a DRAFT proposal or scope with partner-facing annotations."
How it works
- Identify the matter type and load the corresponding profile from references/matter-type-profiles/[type].md. If no profile exists, proceed without one and note the gap.
- Assemble inputs from client materials (emails, org charts, prior files, data room index, RFPs) and label each input with a source reference (e.g., [S1], [S2]). For each input, determine what it confirms, implies, or contradicts another source.
- Apply source-attributed confidence levels: Confirmed, Inferred (from inputs), Inferred (from general knowledge), Unknown. Mark external inferences clearly.
- Perform multi-source conflict detection and rate conflicts (Critical/High/Medium/Low).
- Use external knowledge flags by consulting matter-type profiles and shared-knowledge files to surface gaps as Inferred (from general knowledge) with recommended specialist confirmation.
- Calibrate assumptions using references/standing-assumptions.md, adjusting or surfacing as needed. If the standing list is empty for the matter type, generate candidate items from first principles.
- Draft the PRE-ENGAGEMENT CLIENT BRIEF with sections: Conflicts, Open questions, External knowledge flags, Matter context, Extracted data points, Assumptions candidate list, and Suggested next steps. Include Annex A: Source materials with citations.
- If the partner requests it, generate a DRAFT labelling-compliant proposal or scope with explicit placeholders and inline confidence labels for any inferred data.
All outputs are produced as .docx files unless explicitly requested otherwise. Outputs are matter records that belong in the matter folder. Inline text is not a substitute for the primary output."
# Matter Intake and Scoping ## Purpose Support the LPM across the full pre-execution arc: from unstructured client data to a structured brief the partner can write a proposal from, through to the agreed baseline that every other LPM discipline references. The LPM's role is structural, not determinative. In pre-engagement, the job is removing the painful data-assembly phase — taking whatever the client has thrown at the legal team and organising it so the partner and senior associate can start making legal and commercial decisions rather than hunting through emails. What the firm proposes to do, and at what price, belongs to the partner. The LPM makes that work fast. This skill operates in four modes: 1. **Pre-engagement** — organise unstructured client data into a structured brief. The primary mode. 2. **Quick intake** — capture the agreed baseline after engagement is confirmed. 3. **Full intake** — comprehensive baseline for large or complex matters. 4. **Mid-matter recovery** — reconstruct baseline when the LPM inherits mid-flight. ### Output format All outputs from this skill are produced as .docx files unless the user explicitly requests otherwise. These are matter records — th
- Purpose
- Output format
- Knowledge infrastructure
- Mode 1: Pre-engagement client data structuring
- The problem this solves
- What this mode produces
- Step 1: Identify the matter type and load the relevant profile
- Step 2: Assemble and catalogue the inputs
- Step 3: Apply source-attributed confidence layering
- Step 4: Multi-source conflict detection
- Step 5: Apply external knowledge flags
- Step 6: Calibrate the assumptions candidate list
- Step 7: Draft the pre-engagement client brief
- If the partner asks for a draft proposal or scope sections
What does the matter-intake-scoping-scott-margetts skill do?
Matter scoping across the full pre-execution arc — organise client data into a structured brief, capture the agreed baseline, or reconstruct scope mid-flight. Use when making sense of client information before a proposal, scoping a new matter, running a kickoff, defining scope, mapping stakeholders, or inheriting a matter mid-flight. Trigger on: 'make sense of this', 'structure this for the proposal', 'scope this matter', 'new matter', 'kickoff', 'what are we doing', 'who are the stakeholders', 'what does success look like', 'matter setup', 'intake', 'I've inherited this matter', 'organise thi
How do I install it?
Run `npx skills add lawve-ai/awesome-legal-skills --skill matter-intake-scoping-scott-margetts --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From lawve-ai/awesome-legal-skills, a repository with 618 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.