# Data Framing > Use when a data request is vague, high-level, or ambiguous. Triggers include "analyze this," "what should I look at?," "help me with this dataset," "build a model," "create a dashboard," "find insights," "what's driving X?," "how should we measure X?," unclear business decisions, unclear audiences, or missing metric/grain/timeframe/deliverable. Do NOT use for simple conceptual questions, already… - **Type:** Skill - **Install:** `agentstack add skill-dalanmtz-data-scientist-data-framing` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [DAlanMtz](https://agentstack.voostack.com/s/dalanmtz) - **Installs:** 0 - **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [DAlanMtz](https://github.com/DAlanMtz) - **Source:** https://github.com/DAlanMtz/data-scientist/tree/main/skills/data-framing ## Install ```sh agentstack add skill-dalanmtz-data-scientist-data-framing ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About > Part of the [data-scientist](https://github.com/DAlanMtz/data-scientist) skill suite. Install `data-scientist` for full lifecycle orchestration and routing. # Data Framing ## Purpose Clarify and structure vague, high-level, or strategic data requests before any analysis begins. The posture is curious and structured: ask the right questions to find the real decision, the correct metric, the right audience, and the appropriate analysis path — before the team spends time on the wrong thing. This skill does not run analysis. It frames it. The output is a framing brief, an analysis intake summary, a recommended workflow path, and a handoff packet for the next skill. ## When To Use This Skill Use `data-framing` when: - The request is vague: "analyze this," "find insights," "help me with this dataset." - The business decision, question, or outcome is unclear. - The audience or stakeholder is unknown. - The metric, target, or outcome variable is not specified. - The grain or unit of analysis is not defined. - The time window is unspecified or ambiguous. - The deliverable type is unclear (dashboard? report? model? SQL? one-pager?). - The user says "build a model" without a target, grain, or prediction point. - The user says "make a dashboard" without knowing what decision it supports or who sees it. - The user says "what should we measure for X?" - The request involves a new project intake with no prior context. ## When Not To Use This Skill | Situation | Use instead | |---|---| | Simple conceptual questions ("what is correlation?") | Answer directly | | Already clear, narrow tasks | Answer directly or route to the right specialist | | User has already specified question, data, target, grain, timeframe, deliverable | Go directly to the appropriate specialist | | Inspecting a known dataset | `data-explorer` | | Defining a known metric | `metric-analyst` | ## Relationship to Parent Skill | Responsibility | Owner | |---|---| | Deciding to route to data-framing | Parent `data-scientist` (`workflow/specialist-routing.md`) | | Clarifying the business decision and question | **This skill** | | Identifying audience and stakeholder | **This skill** | | Clarifying metric, target, grain, time window, deliverable | **This skill** | | Proposing analysis paths | **This skill** | | Handoff to the next specialist | **This skill** | | Running the analysis, cleaning, or modeling | Downstream specialist skills | ## Entry Gates Before producing framing output, confirm or state as assumptions: 1. **Enough to identify the likely decision or question** — what problem is the user trying to solve? 2. **Likely data source or domain** — even a rough category (customer data, sales data, events log, survey responses) is enough. 3. **Likely deliverable type** — is the user expecting a report, dashboard, model, SQL query, or exploration? If none of these are answerable, ask one open question before proceeding. ## Framing Workflow 1. **Identify the business or research decision.** What decision will this analysis inform? Who will make it? What changes if the analysis shows one result vs. another? 2. **Clarify the target outcome or metric.** What does success look like? Is there a KPI, outcome variable, target label, or business metric at the center of this? 3. **Clarify the audience or stakeholder.** Who receives the output? What is their technical level? What do they need to act on the result? 4. **Clarify the data source and availability.** What data exists? What tables, files, or systems are available? Is there a data dictionary? 5. **Clarify the grain or unit of analysis.** What does one row represent? Customer, order, session, product, week, market? 6. **Clarify the time window.** What period is in scope? Historical baseline? Forecast horizon? Cohort window? 7. **Clarify the deliverable type.** Dashboard? Executive memo? Model? SQL query? EDA report? Slide deck? 8. **Identify constraints, risks, and success criteria.** Data access limits, team skill constraints, deadline, sensitivity, regulatory considerations, and what "good enough" looks like. 9. **Propose 2–4 analysis paths** when the request is broad. Each path should name: the analytic approach, the data it requires, what it produces, and when it applies. 10. **Recommend the next skill handoff.** After framing, name the next skill in the sequence. ## Question Behavior - Ask questions only when missing details would materially change the analysis path. - For broad projects, ask grouped high-value questions — not one question at a time. - If the user requests speed ("don't ask, just go"), state assumptions and proceed to the next appropriate skill. - Do not ask endless questions. One round of focused questions is the limit before making reasonable assumptions and proceeding. - Never block on information that can be assumed from context. ## Output Formats - **Framing brief** — structured summary: decision, audience, metric/target, grain, time window, deliverable, constraints, recommended path. - **Analysis intake summary** — a short structured document suitable for sharing with a team. - **Recommended workflow path** — which skills to use, in what order, and why. - **Handoff packet** — the structured context passed to the next skill (see `workflow/handoff-contracts.md` in the parent skill). ## Framing Brief Template ``` **Framing Brief** Decision/question: [what will this analysis decide or answer?] Audience: [who receives the output and at what level of detail?] Metric/target: [KPI, outcome variable, or key measure] Grain: [one row = one what?] Time window: [period in scope] Data source: [tables, files, or systems available] Deliverable: [dashboard / report / model / SQL / EDA / other] Constraints: [access, timeline, team skill, sensitivity] Assumptions: [what was assumed due to missing information] Recommended path: [next skill and rationale] ``` ## Recommended Handoff Paths | After framing, route to… | When… | |---|---| | `data-explorer` | Real data is available and must be inspected before analysis | | `metric-analyst` | The core need is KPI definition or SQL metric logic, and data is already known | | `experiment-analyst` | The decision hinges on a test result or causal comparison | | `dashboard-designer` | The deliverable is a dashboard and metric definitions are already known | | `insight-reporter` | The deliverable is a stakeholder communication and analysis already exists | | `production-analytics` | The deliverable is an operational or scheduled pipeline | | Stay in `data-scientist` | The task involves modeling or forecasting that requires the parent skill | Usually hands off to `data-explorer` when real data is involved. ## Hard Rules - Do not run analysis from this skill — framing only. - Do not invent findings. The output is a structure for analysis, not analysis itself. - Do not block on missing details that can be assumed from context. - If the user says not to ask unless blocking, state assumptions and hand off. - Always name the next skill in the recommended path. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [DAlanMtz](https://github.com/DAlanMtz) - **Source:** [DAlanMtz/data-scientist](https://github.com/DAlanMtz/data-scientist) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-dalanmtz-data-scientist-data-framing - Seller: https://agentstack.voostack.com/s/dalanmtz - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.