القائمة
Data cleaning, profiling, transformation, and quality gates -- prepares raw data for visualization and analysis
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/fabioc-aloha/Alex_ACT_Visual_Storytelling --skill data-preparationيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
| type | skill |
| lifecycle | stable |
| inheritance | inheritable |
| name | data-preparation |
| description | Data cleaning, profiling, transformation, and quality gates -- prepares raw data for visualization and analysis |
| tier | standard |
| applyTo | **/*data*,**/*clean*,**/*transform*,**/*prep*,**/*pivot*,**/*aggregate* |
| currency | 2026-08-15T00:00:00.000Z |
| lastReviewed | 2026-08-15T00:00:00.000Z |
Prepare raw data for visualization. This module fires after ingestion and before chart selection. It does not analyze or visualize; it makes data ready for those steps.
State why you are skipping. Silent skips hide data problems.
Before cleaning, understand what you have. Run these checks on every column:
| Check | What to look for |
|---|---|
| Row count | Is it what you expected? Zero rows = ingestion failed |
| Column types | Dates stored as strings? Numbers as text? |
| Null count | Which columns have nulls? What percentage? |
| Unique count | Cardinality: is this a dimension (low) or measure (high)? |
| Min / Max / Mean | For numeric columns: are ranges plausible? |
| Sample values | Eyeball 5 rows. Do they look right? |
Report the profile as a table before proceeding. The user (or orchestrator) needs to see it to decide what cleaning is needed.
Apply fixes in this order. Each fix is a decision; state what you chose and why.
| Signal | Action |
|---|---|
| Exact duplicate rows | Remove. Log count removed. |
| Near-duplicates (same key, different values) | Flag for user decision. Do not silently drop. |
| Strategy | When to use |
|---|---|
| Drop row | Null is in a must-have column and row is < 5% of data |
| Fill with default |
| Column has an obvious default (e.g., 0 for optional count) |
| Fill with median/mode | Numeric column, nulls are < 10%, distribution is stable |
| Leave as-is | The visual can handle nulls (e.g., gap in a line chart) |
Never fill nulls silently. State the strategy and count affected.
| Problem | Fix |
|---|---|
| Date as string | Parse to date. State the format detected. |
| Number as string | Convert. Check for non-numeric values first. |
| Boolean as string | Map "yes/no", "true/false", "1/0" to boolean. |
| Mixed types in one column | Flag for user. This usually means the schema is wrong. |
| Context | Action |
|---|---|
| Outlier is a data error (negative revenue, future date) | Remove or fix. Log. |
| Outlier is real but extreme | Keep. Note it for the visual (it may need axis adjustment). |
| Unsure | Flag for user. Do not remove real data silently. |
Reshape the data to match what the target visual needs.
Aggregate when the raw data is too granular for the visual.
| Visual need | Aggregation |
|---|---|
| Monthly trend | Group by month, sum or average the measure |
| Category comparison | Group by category, sum the measure |
| Proportion | Group by category, compute share of total |
| Distribution | Bin the values, count per bin |
Always state the grain change: "Aggregated from daily to monthly by summing revenue."
| Need | Direction |
|---|---|
| One column per category (wide format) | Pivot |
| One row per observation (long format) | Unpivot |
Most visualizations prefer long format. Pivot only when the delivery module explicitly needs wide format (e.g., side-by-side bar chart in ASCII).
Add columns only when the brief requires a metric that is not in the raw data:
Name computed columns clearly. Do not overwrite source columns.
Every metric passed downstream must carry enough lineage to reproduce it:
| Formula | Source fields | Grain | Units | Rounding | Baseline |
|---|---|---|---|---|---|
| (current - previous) / previous | revenue, date | month | percent | 1 decimal | previous month |
Apply these rules:
Before passing data downstream, assert:
| Gate | Assertion | Fail action |
|---|---|---|
| No nulls in key columns | Dimensions and must-have measures are null-free | Push back: data is not ready |
| Row count is plausible | Within expected range (not 0, not 10x expected) | Push back: ingestion may have failed |
| Types are correct | Every column has the expected type after coercion | Push back: type fix needed |
| Grain is correct | One row per expected unit (e.g., one per month per region) | Push back: dedup or aggregate needed |
If any gate fails, push back to the orchestrator with a specific message: "Quality gate failed: 12 nulls in revenue column. Recommend: drop rows (< 1% of data)."
Pass to the next module:
| Mall plugin | This module's role |
|---|---|
data-analysis | Runs after this module; assumes clean data |
data-quality-monitoring | Monitors ongoing quality; this module does initial prep |
data-visualization | Designs the visual; needs correctly shaped data from this module |
| Do not | Do instead |
|---|---|
| Clean silently | Log every change with count and reason |
| Remove outliers without context | Keep real data; flag for user if unsure |
| Aggregate without stating the grain change | "Aggregated from X to Y by Z" |
| Add computed columns the brief did not ask for | Only add what the questions require |
| Skip profiling because "the data looks fine" | Profile always; it costs one table and catches surprises |
