Forecast Variance Analysis

Analyze variances between your actual, plan, and forecast data. Scenario names are discovered from your data, never assumed — many orgs have no Budget scenario at all (plan data often lives in a separate planning-version field), so the plan side of the comparison is resolved at runtime (Step 1b).

Essential for FP&A reviews, planning adjustments, and performance tracking.

Arguments

Excel Context Mode (Claude in Excel)

When this skill is invoked from within Claude in Excel (the task pane add-in), switch to in-sheet enrichment mode instead of generating output files. This mode integrates the row-commentary approach from datarails-excel-multi-table-analysis and is the preferred mode when the user is working directly in a workbook.

How to detect the context: Excel context mode activates when the user has an open workbook and wants in-sheet enrichment — with or without an existing range. This applies in Claude for Excel (task pane), Claude Code with an open workbook, or any context where the user is working directly in a spreadsheet. Do not generate .xlsx or .pptx output files. Write all output directly into the active workbook using whichever Excel cell-write tool is available to you. Never use the Write tool to write spreadsheet content in this context. If no cell-write tool is available, fall back to file output mode and tell the user.

Step 0 — Verify Excel context (always first in Excel context)

Before any data pull or schema discovery, run the agent.get_session probe through the bridge. agent.get_session is not an MCP tool — you run it by executing Office.js via the execute_office_js tool to write the request to the __dr_agent sheet and read the response (see the Excel Context Contract in CLAUDE.md, §Transport). Do not call the datarails-finance-os MCP connector for this.

• Probe fails (no execute_office_js / Office.js tool, or no bridge sheet) → not in Excel context. Fall back to file-output mode. Stop here.
• Probe succeeds → Excel context confirmed. Now branch on login state:
  • Flex (response has isLoggedIn): if isLoggedIn is false, tell the user to sign in to Datarails and stop — do not reach Step 0b. If true, proceed to Step 0b.
  • COM (response has no isLoggedIn — it exposes isConnected instead): a successful probe means the session is active. Proceed to Step 0b. Do not treat isConnected: false as a login failure.

Do NOT gate analysis on isConnected. Refresh, DR-formula reads, and evaluate all work on an unconnected workbook. connect_file is required only for create_dynamic_range and drilldown_*, and only on COM (Flex sessions have no isConnected field). Check isConnected at the drill-down step (Step 6), not here, and only when the session payload contains it. If a drill needs it and isConnected is false, ask: "This drill-down requires the workbook connected to Datarails. Connect now with connect_file?" — wait for explicit yes (mutating command, never auto-call).

Step 0b — Data refresh (ask once per session, Excel context only): If agent.get_session succeeded, ask: "Should I refresh data from Datarails before pulling? (Recommended if you haven't refreshed today.)" If the user confirms, call refresh_ribbon (timeoutMs: 600000) and wait for terminal status. Skip if the user says no. On follow-up questions in the same session, do NOT re-ask or re-refresh — data is already fresh. In non-Excel context, skip entirely — refresh_ribbon is not available.

Step 1 — Discover client-specific dimensions (always first)

Every Datarails customer structures their Financials table differently. Never hardcode dimension field names. Before pulling any data, discover the financials table and its two key dimensions.

If you already discovered these this conversation, reuse them — skip to Step 2.

list_data_models first. Pick the financials table: the one whose name (or alias) matches /financial|cube|p&?l|ledger|gl/i, else the largest by row count. Note both its numeric id and its alias (alias may be empty). Prefer the alias path when an alias exists — friendlier field names, far fewer tokens.

Then read the schema — list_aliased_fields(<alias>) if the table has an alias, else get_fields_by_id(<id>) (capture each field's numeric id — the by-id tools address fields by id) — and identify:

Alias coverage is per field, not per table. A table having an alias does not mean its fields are aliased — real orgs often expose only a handful of aliased fields (e.g. ~5 of ~185 on a mapped financials table), and the load-bearing fields (amount, scenario, account groups, dates) are frequently not among them. Treat the alias/by-id choice per field: get_fields_by_id(<id>) returns every field with its numeric id and its alias (empty if none). Address a field by alias (via the *_by_alias tools) when it has one, else by numeric id (via the *_by_id tools). By-id always works — never abandon the query because the aliased set is thin.

If multiple candidates exist for either dimension, pick the most specific / granular one. If no candidate is identifiable by name (e.g., non-English field names), list the discovered fields and ask the user which to use. Document both discovered field names — use them in every aggregation for this analysis session. These two dimensions are mandatory; omitting either produces commentary that is not actionable.

If the schema genuinely has no identifiable cost center field, flag this in the Read Me block and fall back to the next-best grouping available (e.g., account hierarchy). Never silently drop the dimension.

Async fetch — aggregations and distinct values run as start → poll. start_aggregation_by_id/_by_alias and start_distinct_values_by_id/_by_alias take the same arguments as the retired blocking calls (dimensions/metrics/filters; table id + field id, or alias + field alias) and return immediately with {"status": "pending", "handle": {...}}. Echo that handle back verbatim to the matching get_aggregation_result_by_* / get_distinct_values_result_by_* tool: a {"status": "running", "retry_after_seconds": N} response means poll again with the same handle after ~N seconds (≈5s) — it is not an error, and large jobs may take several polls; when ready, the result arrives in the familiar shape (for distinct values, pass limit to the result tool). An expired/unknown-handle error means restart with the start_* tool. Transitional fallback: if the start_* tools aren't available on the connector (older server), the blocking twins get_aggregated_data_by_* / get_distinct_values_by_* still work with the same arguments.

Data-scope discovery — run before any aggregate (reuse anything already discovered this conversation).

1. Scenario domain. Pull distinct values of the scenario field (start_distinct_values_by_alias/_by_id → poll the matching result tool) — never assume a scenario name exists (Budget frequently doesn't; many orgs carry only {Actuals, Forecast}). For budget/plan questions, if no budget-like scenario exists, look for a planning-version-like field (alias/name matching /plan|version|cycle|budget/i) and use its versions as the plan side; if neither exists, say so and offer a comparison across the scenarios that do exist.
2. Account grain. Pull distinct values of each account-hierarchy level field (L0/L1/L2-like). Use the level whose values partition P&L flows into revenue/COGS/opex-like buckets — on many orgs the top level is the balance-sheet equation (ASSET/LIABILITY/EQUITY/INCOME) and P&L line items live one level deeper. For P&L work, scope to P&L flows and exclude balance-sheet buckets; never present asset/liability/equity totals as revenue or expenses.
3. Period scope. Discover the date field's range (distinct values of the reporting-month field, or MIN and MAX in two separate calls — one aggregation per field per call). Default every P&L question to the latest complete fiscal year (or trailing 12 closed months) — never an unscoped all-time total: financials tables are multi-year cumulative and mix balance-sheet stock with P&L flow. Label every output with the period + scenario it covers.
4. Reading GROUP BY responses. Each response returns exactly one row per requested group — no subtotal rows and no grand-total row. A total is obtained by summing the rows — there is no total row to read. Null groups arrive explicitly labeled [null] and are real groups; read null counts from that bucket. Defensive filter: keep only rows in which every requested dimension key is present — a roll-up row omits one or more keys entirely, whereas a genuine null is present with the value [null]. On a correct response this is a no-op; it guards against a stale cached response still carrying legacy subtotal and grand-total rows, each of which equals the whole total and would inflate any sum. When COUNT-ing rows per group, aggregate a different field than the GROUP BY dimension itself — a same-field COUNT of the grouped dimension can 500.
5. Truncated results. Any data tool may return {"data": [...], "truncated": true, "total_rows": N, "returned_rows": M, "guidance": "..."} when the result exceeds the response size limit (~100 KB). The data prefix is incomplete — never compute totals, shares, or trends from it, and never present it as the full result. Follow the guidance: narrow the query (fewer dimensions, more filters, fewer selected columns) or use a business metric for a named KPI, then re-fetch.

Step 1b — Resolve the scenario domain and the plan side (never assume Budget exists)

Scenario names vary by org, and a Budget scenario frequently does not exist — filtering on it returns an empty result and a silently wrong analysis. Resolve every side of the comparison against discovered values before any pull:

1. Discover the scenario domain. Pull distinct values of the discovered scenario field — start_distinct_values_by_alias(alias=<financials_alias>, field=<scenario_field>) if the field is aliased, else start_distinct_values_by_id(table_id=<id>, field_id=<scenario_field_id>) → poll the matching get_distinct_values_result_by_alias/_by_id(handle) until ready (async-fetch pattern; pass limit to the result tool if you need to cap the values).
2. Resolve the actual side — the discovered value matching /actual/i.
3. Resolve the plan side:
   • A budget-like scenario exists (a discovered value matches /budget|plan|aop|target/i) → use it as the plan-side scenario filter.
   • No budget-like scenario → look for a planning-version-like field in the Step 1 schema (alias/name matching /plan|version|cycle|budget/i). If one exists, pull its distinct values the same way, present the versions, and ask the user which plan version to compare against (default to the most recent only if the user already indicated it). Use {<planning_version_field>: <chosen version>} as the plan-side filter in Step 3 instead of a scenario value — and check which scenario values the rows under that version carry before also filtering on scenario.
   • Neither exists → tell the user what you found, then offer what IS possible:

     I found actuals data but couldn't locate any budget or plan data in your financials table — there's no budget-like scenario and no planning-version field.

     The available scenarios are: [list the discovered values]

     Options:

     • Would you like me to compare Actuals against Forecast instead?
     • Or should I just show you the Actuals breakdown?

4. Resolve the forecast side (if in scope) — the discovered value matching /forecast/i; if absent, drop it from the comparison and say so.
5. If --scenarios was passed, map each requested name onto the discovered domain (case-insensitive). Any name with no match goes through the plan-side fallback above — never filter on a scenario value you did not see in the distinct-values response.

If the plan side is incomplete (it exists but doesn't cover all the categories or periods that Actuals covers), say so and offer:

• Compare only the overlapping categories/periods
• Show where plan data is missing

Record the resolved sides — actual, plan (scenario value or plan-version filter), forecast — and use them verbatim in every Step 3 pull and in every output label.

Step 2 — Read the active sheet (Phase 0)

Before pulling data, read what is in the active sheet:

• Enrichment mode: the user has a range (rows × period columns, variance grid, P&L block). Identify row labels, column headers, data bounds, and the empty space to the right where Commentary + Source Ref columns will go.
• Cold-question mode: the sheet is empty or unrelated. Build a fresh variance block from scratch, including Commentary + Source Ref columns from the start.

Do not assume which mode — read the sheet first.

Step 3 — Pull data using the discovered dimensions (in parallel)

Issue all scenario pulls in a single turn (concurrent tool calls). Use start_aggregation_by_alias (preferred) or its by-id twin start_aggregation_by_id. For each pull, always include the discovered cost center and report field dimensions alongside the period dimension:

start_aggregation_by_alias(
  alias=<financials_alias>,
  dimensions=[<period_field>, <cost_center_field>, <report_field>],
  metrics=[{"field": <amount_field>, "agg": "SUM"}],
  filters=[
    {"name": <scenario_field>,      "values": [<side resolved in Step 1b>],                "is_excluded": false},
    {"name": <account_level_field>, "values": [<P&L flow buckets from data-scope item 2>], "is_excluded": false},
    {"name": <gaap_field>,          "values": [<discovered GAAP-adjustment values>],       "is_excluded": true}
  ]
)

→ poll get_aggregation_result_by_alias(handle) until ready (async-fetch pattern) — start all sides first, then poll their handles together.

All filter values are placeholders — every one comes from Step 1b / the data-scope discovery, never from this template. Plan side: when Step 1b resolved a plan-version fallback, replace the scenario filter with {"name": <planning_version_field>, "values": [<chosen version>], "is_excluded": false}. Account scope: use the account-hierarchy level whose discovered values partition P&L flows (data-scope item 2) — on many orgs the top level is the balance-sheet equation and P&L buckets live one level deeper. GAAP exclusion: only if a GAAP-adjustment-like field exists — discover its values first, otherwise omit the filter.

Scope by year either by adding <date_field> to dimensions and filtering the result client-side, or with an advanced date-range filter: {"name": <date_field>, "values": {"type": "advanced", "val": [{"condition": "total_range", "value": ["<jan1_epoch>", "<dec31_epoch>"]}]}} (epoch seconds as strings). Both work — date filtering is no longer rejected.

By-id fallback (no alias): start_aggregation_by_id(table_id=<id>, dimensions=[<period_field_id>, <cost_center_field_id>, <report_field_id>], metrics=[{"field_id": <amount_field_id>, "agg": "SUM"}], filters=[{"field_id": <scenario_field_id>, "values": [...]}]) → poll get_aggregation_result_by_id(handle) until ready (async-fetch pattern). If an alias call 500s on a dimension, re-inspect the Step 1 schema for a sibling and retry, or fall back to the by-id twin.

Assign each distinct pull a Source ID: S1, S2, S3, ...

An empty result from a scenario filter is a resolution failure, not a zero plan — go back to Step 1b rather than presenting $0 as the plan side.

Also pull HeadCount whenever compensation lines are in scope and the org has a headcount-like table (discover it via list_data_models; if none exists, skip the decomposition and say so). Comp $ alone cannot distinguish volume (headcount change) from rate (salary / bonus change) — always decompose. See the HC inference trap in datarails-excel-multi-table-analysis.

Verify data freshness for each table before quoting period boundaries. If a table is short of the requested period, narrow the window and flag it in the Read Me block and in each affected commentary row.

Step 4 — Write per-row commentary and source refs

Add two columns to the right of the user's range (or include them in the fresh block):

Commentary cell rules:

• One sentence per row, quantified (Δ$ and Δ%)
• Always call out both the cost center and the report field in the sentence — these are the two dimensions that make commentary actionable
• End with inline source refs: [S1, S2]
• If a decomposition applies (e.g., HC volume vs rate), include it
• If data freshness is a caveat, name it inline

Example commentary cells (illustrative — <report_field> and the plan-side label come from Step 1 / Step 1b):

Marketing expense +$120K (+14%) vs Plan, driven by Events cost center (+$95K, <report_field>: T&E); remaining $25K in Digital/Software. [S1, S2]

R&D compensation +$280K (+18%) vs Plan: $230K (82%) is rate-driven (Q4 bonus accrual on flat HC of 23), $50K (18%) is volume (2 new hires in Oct). [S1, S2, S3]

Source Ref cell: bracketed refs only — [S1, S2]. No commentary in this cell.

Citing live DR-formula reads (Excel context): When a $ figure comes from reading a DR cell (agent.read_range / agent.evaluate_drget) rather than a start_aggregation_by_alias pull, cite the cell's data.sources[] (sheet!cell + widget) returned by the read — every $ figure must carry a source. If a read returns empty sources, do not present the figure; re-read or tell the user it isn't Datarails-tracked.

Step 5 — Build the Sources sheet and Read Me block

Sources sheet (Sources tab — create if it doesn't exist):

One row per distinct Source ID. Every [S#] cited in Commentary must have a matching row here.

Read Me block (top of the analysis sheet, above the data):

SCOPE & FILTERS
  Period:    <explicit window, e.g., 2025-01 through 2025-09>
  Scenarios: <resolved sides from Step 1b, e.g., Actuals vs "FY25 Plan v2" (plan version) vs Forecast>
  Accounts:  <account_level_field> = <discovered P&L flow buckets>; <gaap_field> excluded (if present)
  Cost Center field:  <discovered field name>
  Report Field:       <discovered field name>

SOURCE TABLES (full detail in Sources sheet)
  S1 — Financials: actual side by period × cost center × report field
  S2 — Financials: plan side (budget-like scenario or plan version) by period × cost center × report field
  S3 — HeadCount: FTE by month × cost center (if applicable)

DATA FRESHNESS
  Financials: <latest loaded period>
  HeadCount:  <latest loaded period, or "N/A">

Step 6 — Drill-down menu (mandatory in Excel context — do not skip)

Excel context + enrichment mode only. Two conditions must both be true:

1. agent.get_session succeeded in Step 0 (Excel context confirmed).
2. You are in enrichment mode — the user's sheet had existing DR formula cells (DR.GET, DR.QTD, DR.YTD, DR.MTD, etc. — any DR function) that you added commentary alongside.

If either condition is false, skip this step entirely and state why:

• Non-Excel context → drilldown_list not available.
• Cold-question mode → data cells were written as raw values from the FinanceOS API (start_aggregation_by_alias → get_aggregation_result_by_alias), not DR formulas. drilldown_list targets DR formula cells — it has nothing to act on. Tell the user drill-down is unavailable in this mode.

Drill-down works on any DR function cell, not just DR.GET. The cell must resolve to a Datarails widget (DR.GET/QTD/YTD/MTD/...). Before firing, if isConnected is false, confirm connect_file with the user (see Step 0).

If both conditions are true: after writing commentary, you MUST output a Drill-Down Menu block directly in chat. Do not end the response without it.

The block must list every row where |variance| > 10% or flagged unfavorable, one row per line with its Δ$ and Δ%:

📋 Drill-Down Menu — rows with |variance| > 10%
  1. <Row label>  Δ$X.XM  (ΔY%)
  2. <Row label>  Δ$X.XM  (ΔY%)
  ...

Then explicitly ask: "Which rows would you like me to drill into for a cell-level breakdown? I'll call drilldown_list on each selected row."

Only invoke drilldown_list (params: sheetName, cellAddress; timeoutMs: 180000) after the user selects rows — do not drill automatically.

Anti-patterns (Excel context)

• Never omit the cost center dimension from commentary — "R&D over budget" is insufficient; name the cost center
• Never omit the report field dimension — "expenses up" is insufficient; name the line item
• Never hardcode dimension field names — always discover from schema; they vary by client
• Never filter on a scenario value you didn't discover — Budget frequently doesn't exist; resolve every side in Step 1b first
• Never write commentary without a [S#] ref — every claim must be auditable
• Never overwrite the user's existing data — add columns to the right only
• Never imply freshness you don't have — flag any missing periods explicitly

Variance Analysis

Variance math rules (all modes):

• Compute every side over the same explicit period window (data-scope item 3 — default the latest complete fiscal year or trailing 12 closed months, never an unscoped all-time total), pulled with identical dimensions and account scoping so rows line up.
• Label every table, chart, and commentary output with the period + the resolved sides it covers (e.g., "2025-01…2025-09, Actuals vs FY25 Plan v2").
• Apply data-scope item 4 to every aggregation response: every row is a real group and no total row is appended, so sums, shares, and variance denominators come from your own sum of the rows; read null counts only from the explicit [null] bucket; when COUNT-ing rows per group, aggregate a different field than the GROUP BY dimension itself.
• Variance $ = actual − plan; variance % = variance $ / |plan|. When the plan side is zero or missing for a row, report the $ variance and mark the % as n/m — never divide by zero or fabricate a value.

Budget Variance

• Actual vs plan-side amounts (budget-like scenario or plan version, resolved in Step 1b)
• Percentage difference
• Favorable/unfavorable identification
• Trend analysis

Forecast Variance

• Actual vs Forecast amounts
• Track forecast accuracy
• Identify forecast bias
• Forecast adjustment recommendations

Multi-Account Analysis

• Revenue variance by product/segment
• Expense variance by department
• Account-level drill-down
• Root cause identification

Datarails Brand Styling

When generating Excel or PowerPoint files, apply Datarails brand styling:

Font: Poppins (fall back to Calibri if unavailable). Weights: 400 regular, 600 semibold, 700 bold.

Colors:

Excel layout:

• Content starts at column B (column A is a narrow gutter)
• Rows 1-6: header banner with navy background, white title text, white subtitle
• Gridlines OFF. Freeze panes at B7.
• Footer as last row with generation date
• Every cell must have font, fill, alignment, and number format set

Number formats: _(* #,##0_);_(* (#,##0);_(* "-"_);_(@_) (default), $#,##0 (dollars), $#,##0.0,,"M" (millions), 0.0% (percent)

Variance coloring: Any cell showing a delta/change: green (2ECC71) if favorable, red (E74C3C) if unfavorable. Apply automatically based on value sign and metric context.

PowerPoint: Navy (0C142B) background, 16:9 widescreen, Poppins font, white text, amber (FFA30F) accent lines, card backgrounds 001F37.

DR.GET Formulas — Authoring Contract

If asked to add live / refreshable Datarails formulas (DR.GET) to a generated workbook, the only valid form is:

=DR.GET(Value, "[DimensionName]", CellRef, "[DimensionName]", CellRef, ...)

• Never transliterate an MCP/API call into a formula. DR.GET takes no table, field, or aggregation arguments — =DR.GET(Value,"financials","Amount","SUM",...) is invented syntax that the Datarails Add-in cannot parse or refresh.
• Dimension names go in square brackets inside quotes ("[Scenario]"). Dimension values are always cell references, never hardcoded strings.
• Date cells referenced by formulas hold end-of-month date serials computed from the calendar — never raw epoch timestamps from API responses (epochs land a day early with a time component and never match).
• Before writing any formula, create the workbook-scoped defined name Value referring to the string constant "Value" (wb.defined_names.add(DefinedName("Value", attr_text='"Value"'))) — otherwise Excel autocorrects the bare token to its built-in VALUE() and the formula breaks.
• Bare =DR.GET(...) only — never wrapped in IFERROR/IF/ROUND.

The get-formula skill (/dr-get-formula) is the full reference — parameter cells, validated dimension values, report layouts. Prefer it for whole formula workbooks; apply this contract when adding DR.GET formulas to a workbook here.

Output

Excel Report

• Summary: Total variances by scenario
• Variance Analysis: Account-by-account comparison
• Scenario Totals: Total by scenario
• Exception Report: Large variances highlighted

PowerPoint Summary

• Overview slide: Scenario totals
• Variance slide: Top variances
• Executive summary
• Key findings

Examples

Annual variance analysis

/dr-forecast-variance --year 2025

With custom scenarios

/dr-forecast-variance --year 2025 --scenarios Actuals,Budget

Scenario names passed via --scenarios are resolved against the discovered scenario domain (Step 1b). If a requested name (e.g. Budget) doesn't exist on the org, the plan side falls back to a planning-version field — or you'll be shown the scenarios that do exist and offered Actuals vs Forecast.

Specific period focus

/dr-forecast-variance --year 2025 --period 2025-Q4

Custom output

/dr-forecast-variance --year 2025 \
  --output-xlsx reports/variance_2025.xlsx \
  --output-pptx reports/variance_summary.pptx

Use Cases

Monthly FP&A Review

# Compare latest actuals vs budget
/dr-forecast-variance --year 2025 --scenarios Actuals,Budget

Forecast Accuracy Tracking

# Compare forecast prediction accuracy
/dr-forecast-variance --year 2025 --scenarios Actuals,Forecast

Full Scenario Planning

# Complete comparison for board review
/dr-forecast-variance --year 2025 --scenarios Actuals,Budget,Forecast

Department Review

# Analyze departmental performance vs budget
/dr-forecast-variance --year 2025

Investor Update

# Professional variance analysis for stakeholders
/dr-forecast-variance --year 2025

Performance

• Analysis: ~1-3 minutes
• Scales to multiple years
• Handles 100+ accounts
• Efficient aggregation

Interpretation

Favorable Variance

• Actual > Plan (Revenue) ✅
• Actual < Plan (Expense) ✅

Unfavorable Variance

• Actual < Plan (Revenue) ❌
• Actual > Plan (Expense) ❌

% Variance

• <5%: Excellent forecast/budget accuracy
• 5-10%: Good, within normal range
• 10%+: Needs investigation

Workflow

FP&A Process:

1. /dr-extract --year 2025       (Get actuals)
2. /dr-reconcile --year 2025     (Validate data)
3. /dr-forecast-variance --year 2025  (Analyze variances)
4. Present findings to leadership (Board meeting)

Advanced Usage

Track forecast improvement

# Monthly variance tracking
/dr-forecast-variance --year 2025 --scenarios Actuals,Forecast --period 2025-01
/dr-forecast-variance --year 2025 --scenarios Actuals,Forecast --period 2025-02
# Compare forecast accuracy trend

Multi-year comparison

/dr-forecast-variance --year 2024 --scenarios Actuals,Budget
/dr-forecast-variance --year 2025 --scenarios Actuals,Budget
# Compare year-over-year performance

Scenario sensitivity

# Three different budget scenarios
/dr-forecast-variance --year 2025 --scenarios Actuals,Budget_Conservative,Budget_Aggressive

Error Handling

"Scenario not found" - The requested name isn't in the discovered scenario domain. Re-run Step 1b: show the scenarios that DO exist, offer them, and try the planning-version fallback for the plan side.

"No variance data" - A side returned empty. If the plan-side pull filtered on a scenario value that was never discovered (e.g. Budget), that's the bug — go back to Step 1b; plan data often lives in a planning-version field, not a scenario.

"Large variance" - Review detailed Excel report for root causes

Integration

Works with:

• /dr-extract - Source of scenario data
• /dr-insights - Contextual trend analysis
• /dr-reconcile - Validate scenario consistency
• /dr-dashboard - Current performance view
• Excel Add-In bridge - In Excel context (see the Excel Context Contract in CLAUDE.md for exact params/timeouts):
  • agent.get_session — verify Excel context + login state (Step 0)
  • connect_file — connect workbook; only needed for create_dynamic_range / drilldown_*, confirm with user first
  • refresh_ribbon — refresh stale data before pulling (timeoutMs: 600000); ask once per session
  • agent.read_range / agent.evaluate_drget — read a DR formula cell's value + data.sources[] for citation
  • drilldown_list — cell-level breakdown on large-variance rows after analysis (timeoutMs: 180000)
  • publish_to_dashboard — publish summary range to a Datarails dashboard (mutating, confirm first)

Related Skills

• /dr-extract - Extract scenario data
• /dr-insights - Understand drivers of variances
• /dr-reconcile - Validate data consistency
• /dr-dashboard - Real-time performance
• datarails-excel-multi-table-analysis - Row-commentary pattern used in Excel context mode (per-row attribution, source refs, Sources sheet, HC decomposition)