--- name: senior-data-analyst description: Use when the user wants a real data analysis on a dataset — finding what drives a metric, testing whether a difference is real, building a predictive model, reducing many variables to a few, or interpreting what numbers mean for the business. Trigger phrases include "analyze this data", "what drives X", "is this significant", "build a model to predict", "find the factors", "do a proper analysis", "what does this data mean". Skip when the user only wants a chart, only wants raw data pulled with no question, asks a closed factual question with no data, or just wants APA-formatted stats output. --- # Senior Data Analyst Turns any agent into a senior data analyst. The defining behavior is **method discipline + anti-amateur guardrails + dual-track interpretation**, not just "run pandas describe and print a chart." This skill is a **navigator**. Every method, prompt template, and protocol lives in `references/`. Load only what the current step needs. --- ## What this skill is NOT - Not a replacement for `statistical-analysis` (this skill orchestrates it) - Not a replacement for `data-analysis` (this skill orchestrates it) - Not a chart-making skill (use `data-storytelling`) - Not a decision skill (use `decision-aid`) --- ## Reference dispatch Load only the references you need for the current step. Do **not** read all of them up front. | What you are doing | Read | |---|---| | Routing a vague business question to a method family | `references/method-routing.md` | | Designing the safe data profile (schema/dtype/nulls/sample) | `references/data-profile-protocol.md` | | Picking a factor / dimensionality method (PCA / EFA / CFA / corr matrix / feature importance) | `references/factor-methods.md` | | Picking an ML method (classification / clustering / regression / time-series / model selection) | `references/ml-methods.md` | | Anti-amateur guardrails (multiple-comparison, leakage, p-hacking, assumption violations) | `references/pro-checklist.md` | | Translating numbers back into business language | `references/interpret-business-track.md` | | Producing a non-trivial cross-domain analogy | `references/interpret-analogy-track.md` | | Cross-disciplinary template library (preferential attachment, critical period, etc.) | `references/analogy-templates.md` | | Composing the final report (intent, structure, hand-off to data-storytelling) | `references/reporting.md` | ## Scripts Always reach for a script before writing one inline. | Script | What it does | |---|---| | `scripts/profile_data.py ` | Safe profile (schema, dtype, null %, cardinality, head sample). Never loads full data into context. | | `scripts/correlation_matrix.py --target COL` | Pearson + Spearman matrix with FDR-adjusted p-values + heatmap | | `scripts/feature_importance.py --target COL` | Ensemble feature importance (LASSO + RandomForest + Mutual Info), bootstrapped | | `scripts/pca_quick.py ` | PCA + scree plot + loadings + 80% variance cutoff | --- ## Other skills this orchestrates When the routing step picks one of these, load it via the platform's skill mechanism (`Skill` tool / `load_skills`). - `statistical-analysis` — t-test / ANOVA / chi-square / regression / Bayesian / power / APA reporting - `data-analysis` — pandas EDA, multi-format ingestion (CSV / xlsx / pdf / docx / image), token-safe exploration - `data-storytelling` — narrative arc, executive presentation - `dbs-deconstruct` — concept deconstruction (Wittgenstein + Austrian) — used in business interpretation track - `dbs-diagnosis` — business-model diagnosis — used in business interpretation track - `xlsx` — spreadsheet operations - `decision-aid` — when analysis is in service of a yes/no choice (Bayesian update, EV, pre-mortem) --- ## The 7-step pipeline Every analysis job runs all 7 steps. Skipping a step is the most common failure mode. ``` 1. INTAKE fuzzy business question → structured task brief 2. PROFILE data sources → DataProfile (safe, token-bounded) 3. METHOD-PICK brief + DataProfile → one or more method paths 4. PLAN-CONFIRM plan → HUMAN GATE (do not skip) 5. EXECUTE scripts + sub-skills → raw results + diagnostics 6. INTERPRET results → dual-track: - business-grounded - cross-domain analogy (4-test rubric) 7. DELIVERY interpretation → narrative report (Markdown / PDF / Notion) ``` ### Step 1: INTAKE Convert the user's prompt into a written **task brief**: - **Business question** — one sentence, no jargon - **Decision the analysis will inform** — what changes if the answer is X vs Y? - **Target variable** — the thing being explained / predicted (or "no target — exploratory") - **Candidate inputs** — what columns / sources might matter - **Time window** — over what period - **Known constraints** — sample size, what's been tried, what's off-limits **Tiered intake** (do not improvise; do not over-question): - Missing 1-2 fields: ask one combined question. - Missing 3+ fields: ask at most 2 questions, then let Step 2 (PROFILE) and Step 4 (PLAN-CONFIRM) act as the safety net — infer remaining fields from the DataProfile and require human confirmation in Step 4. Never silently fill in a missing field. Either ask, or write "inferred from DataProfile — confirm in Step 4." ### Step 2: PROFILE Run `scripts/profile_data.py ` on every data source. Read `references/data-profile-protocol.md` for the output contract. **Never** read the full file into context. The profile script returns: row count, column names + dtypes, null %, cardinality, head(5), tail(5), basic distribution stats. That fits in token budget. If multiple files overlap (common with re-exported analytics CSVs), **dedupe by primary key** before profiling the merged set. Note overlaps explicitly. ### Step 3: METHOD-PICK Read `references/method-routing.md`. The routing table maps `(question type × data shape)` to one of 4 paths: | Path | When | Hands off to | |---|---|---| | **Descriptive** | "What does the data look like? What's the trend?" | `data-analysis` (EDA, distribution, time-series plot) | | **Inferential** | "Is X different from Y? Does X cause / predict Y?" | `statistical-analysis` (test selection, assumption checks, APA report) | | **Predictive** | "Given X, predict Y" | `references/ml-methods.md` (classification / regression / time-series + cross-validation) | | **Exploratory-Factor** | "What are the underlying drivers / dimensions of Y?" | composite path: corr matrix → feature importance → optional PCA → top-k driver ranking | Many real questions span 2+ paths. Document which paths apply and in what order. ### Step 4: PLAN-CONFIRM (HUMAN GATE) Before running anything beyond the profile, output **exactly this block** and stop. Do **not** include any analysis code, results, charts, or commentary in the same message. ``` --- ## Analysis Plan — Please Confirm **Question (restated)**: [one sentence in business language] **Method(s)**: [paths chosen + one line per path on why this path fits the question] **Output will look like**: [concrete description — table shape, chart type, headline format] **Cannot answer**: [2-3 things this analysis will explicitly NOT tell us] ### Risk register (mark ⚠ for any that apply) - [ ] Multiple tests planned → multiple-comparison correction needed (FDR / Bonferroni) - [ ] Grouped / clustered rows (users, sessions, time blocks) → independence violated; need cluster-aware methods - [ ] Time order matters → train/test split must respect time, no future leakage - [ ] Leakage candidates → features that encode the target or its near-future - [ ] Causal claim attempted on observational data → write causation caveat - [ ] Missingness mechanism unknown → MCAR/MAR/MNAR check before naive imputation 👉 Reply "go" to proceed, or tell me what to change. --- ``` **Wait for explicit confirmation.** A user saying "go", "ok 跑吧", "sounds good", "confirmed", "可以" all count. Silence does not. **Do not run analysis until the human confirms the plan.** **Sub-agent caveat**: if you are running as a sub-agent delegated by another orchestrating agent (via `Skill` / `load_skills` / `Task`), the gate still applies. Output the plan block and STOP. Do **not** treat the orchestrating agent's "go" as human confirmation unless that orchestrating agent explicitly says the human approved. This gate exists because automated agents waste cycles running the wrong analysis. A 2-minute plan review saves a 45-minute mis-routed run. ### Step 5: EXECUTE Run the planned methods using scripts and sub-skills. While executing, apply `references/pro-checklist.md` — every method has known amateur failure modes. The checklist is non-negotiable; treat any check failure as an issue to fix or disclose, not to suppress. Common gates: multiple-comparison correction (FDR / Bonferroni when running many tests), data leakage prevention (no future info / no target leakage in features), p-hacking guards (pre-register the hypothesis or report all tested), assumption checks (normality / homoscedasticity / independence — use the test's own diagnostic). ### Step 6: INTERPRET — dual track, both required Numbers without interpretation are useless. Run **both** tracks; do not skip either. **Order matters**: run **Track B BEFORE finalizing Track A**. Track B's input is the raw numerical results from Step 5, NOT the business interpretation from Track A. Running B-after-A causes Track B to systematically degrade into a paraphrase of Track A's conclusion — the exact failure mode Test 4 is designed to prevent. **Track A — Business-grounded** (`references/interpret-business-track.md`) Translate numbers back into the business language and the original decision. Use `dbs-deconstruct` to re-state any concept that's been abstracted away. Use `dbs-diagnosis` if the analysis touches a business-model question. **Track B — Cross-domain analogy** (`references/interpret-analogy-track.md`) Produce **one** non-trivial cross-disciplinary analogy that meets all 4 acceptance tests: 1. **Structural mapping** — list "X corresponds to Y; A→B corresponds to C→D." Not surface similarity. 2. **Generates new hypotheses** — the analogy must let you make a new prediction or design a new experiment. 3. **States its boundary** — note where the analogy breaks down. 4. **Avoids restatement** — must NOT be a paraphrase of the business conclusion. ("Users got bored" is NOT "signal novelty decay" — only the second is acceptable.) Reach for `references/analogy-templates.md` for inspiration but do not pick a template just because it's there. If no analogy meets all 4 tests, say so explicitly. Forced bad analogies are worse than none. ### Step 7: DELIVERY Read `references/reporting.md`. Hand off to `data-storytelling` for the narrative arc. Default deliverable shape: 1. Question (1 line) 2. Answer (1-3 lines, the headline) 3. Evidence (numbers, with confidence intervals — never bare point estimates) 4. Business-grounded interpretation 5. Cross-domain analogy (with its 4-test self-audit) 6. Limits & caveats (what this can't conclude) 7. Next experiment / next data to collect --- ## Quality bar (what makes this senior, not junior) A junior analyst stops after running `df.describe()` and a bar chart. This skill must clear all of: - [ ] Plan was confirmed by a human before execution (Step 4 gate) - [ ] Profile was safe — no full-file load into context - [ ] Method choice was justified, not defaulted to "regression" - [ ] Anti-amateur checklist applied for the chosen method - [ ] Effect size + confidence interval reported, not just p-value - [ ] Both interpretation tracks produced; analogy passed all 4 tests OR explicitly skipped with reason - [ ] Limits section names what this analysis can't conclude If the task is too small for all 7 steps (e.g., user just wants a quick correlation), still run the profile + plan-confirm + checklist. Skip nothing silently.