| name | analyze |
| description | Deep-dive financial analysis — maps income and expenses, identifies savings opportunities, and creates a phased action plan with budget targets. Use when: analyze finances, financial checkup, deep dive, find savings, audit expenses, map income, understand where money goes, check financial health.
|
| compatibility | Requires KolShek CLI (kolshek) installed and configured with at least one provider. |
| metadata | {"author":"kolshek","version":"0.4.8"} |
| allowed-tools | Bash Read Write AskUserQuestion |
/kolshek:analyze
You are performing a comprehensive financial analysis. This is a one-time (or periodic) deep dive that maps the user's entire financial picture, identifies savings opportunities, and produces an actionable plan with budget targets.
Before You Start
Read references/cli-reference.md for the complete command reference, DB schema, exit codes, and SQL patterns.
Run startup checks:
kolshek providers list --json — if empty, guide user to kolshek providers add.kolshek transactions list --limit 1 --json — if empty, offer to fetch.kolshek query "SELECT MAX(completed_at) as last_sync FROM sync_log WHERE status = 'success'" --json — if over 24h old, suggest kolshek fetch.
Translation check: Run kolshek query "SELECT COUNT(*) as total, SUM(CASE WHEN description_en IS NOT NULL THEN 1 ELSE 0 END) as translated FROM transactions" --json. If most descriptions lack description_en, suggest running /kolshek:translate first — analysis with English descriptions is far more reliable.
Categorization check: Run kolshek query "SELECT COUNT(*) as total, SUM(CASE WHEN category IS NOT NULL AND category != '' THEN 1 ELSE 0 END) as categorized FROM transactions" --json. If most transactions are uncategorized, suggest running /kolshek:categorize first — category-based analysis requires categorized data.
Resolve config dir (needed for budget.toml later):
bun -e "import envPaths from 'env-paths'; console.log(envPaths('kolshek').config)"
Store this path — you will use it in Step 7.
Build classification exclusion list: Run kolshek categorize classify list --json to get all categories and their classifications. By default, exclude categories classified as cc_billing and transfer from expense analysis (these are internal movements, not real spending). Ask the user if they want to adjust — they may want to also exclude investment, debt, savings, or include categories that are normally excluded. Build $EXCLUDE_SQL from their choices, e.g.: category NOT IN (SELECT name FROM categories WHERE classification IN ('cc_billing', 'transfer')). If the user wants no exclusions, omit this clause entirely.
Step 1: Establish Analysis Window
Ask the user:
- "Full analysis — last 3 months" (recommended)
- "Extended analysis — last 6 months"
- "Custom date range"
Verify data coverage:
kolshek query "SELECT MIN(date) as earliest, MAX(date) as latest, COUNT(*) as total FROM transactions" --json
If data doesn't cover the requested window, warn the user and offer to fetch more: kolshek fetch --from <date> --json.
Set $FROM and $TO dates and $MONTHS count for use in subsequent steps.
Step 2: Income Mapping
Run for each month in the analysis window:
kolshek income <YYYY-MM> --json
The response includes summary.salary, summary.transfers, summary.refunds, summary.other and individual transactions with incomeType.
Also detect recurring vs one-time income sources:
kolshek query "SELECT COALESCE(t.description_en, t.description) as source, COUNT(DISTINCT strftime('%Y-%m', t.date)) as months_seen, ROUND(AVG(t.charged_amount), 2) as avg_amount, ROUND(SUM(t.charged_amount), 2) as total FROM transactions t JOIN accounts a ON t.account_id = a.id JOIN providers p ON a.provider_id = p.id WHERE t.charged_amount > 0 AND p.type = 'bank' AND t.date >= '$FROM' GROUP BY source ORDER BY total DESC" --json
Present:
Income Sources
| Source | Type | Months Seen | Avg/Month | Total |
|---|
| Salary - Acme Corp | Fixed (Salary) | 3/3 | ₪15,000 | ₪45,000 |
| Bit Transfer - Mom | One-time | 1/3 | ₪2,000 | ₪2,000 |
Monthly fixed income: ₪X | Variable: ₪Y | Total avg: ₪Z
Classification rules:
- Source appears in all months with similar amount → Fixed
- Source appears once or amounts vary wildly → One-time/Variable
- Salary-type income (from
kolshek income) → always Fixed
Important: All subsequent calculations compare against fixed income only — never include one-time income.
Step 3: Expense Mapping
Run per-month spending:
kolshek spending <YYYY-MM> --json
kolshek spending <YYYY-MM> --group-by provider --json
Run fixed vs variable analysis:
kolshek trends $MONTHS --mode fixed-variable --json
Classify each category into Mandatory or Discretionary:
- Mandatory: Housing, Groceries, Utilities, Healthcare, Transportation, Insurance, Education, Childcare
- Discretionary: Restaurants, Entertainment, Shopping, Fashion, Subscriptions, Travel, Gifts
- Mixed: Food (split between Groceries=mandatory, Restaurants=discretionary)
- Use the
fixedMerchants from trends --mode fixed-variable to identify fixed costs within each category
Present two tables:
Mandatory Expenses (avg/month)
| Category | Monthly Avg | % of Income | Fixed/Variable |
|---|
| Housing | ₪5,000 | 33% | Fixed |
| Groceries | ₪2,800 | 19% | Variable |
Discretionary Expenses (avg/month)
| Category | Monthly Avg | % of Income | Fixed/Variable |
|---|
| Restaurants | ₪1,200 | 8% | Variable |
| Entertainment | ₪800 | 5% | Variable |
Present per-provider (per-card) breakdown:
Per-Card Breakdown
| Provider | Monthly Avg | Mandatory % | Discretionary % |
|---|
| Visa Cal (personal) | ₪4,500 | 40% | 60% |
| Leumi (joint) | ₪8,000 | 85% | 15% |
Bottom line:
Fixed income: ₪X/month
Total expenses: ₪Y/month (Z% mandatory, W% discretionary)
Monthly surplus/deficit: ₪N
Step 4: Trends & Outliers
Run:
kolshek trends $MONTHS --json
kolshek insights --months $MONTHS --json
Run top 20 largest transactions (apply $EXCLUDE_SQL from Before You Start):
kolshek query "SELECT COALESCE(description_en, description) as merchant, ROUND(ABS(charged_amount), 2) as amount, date, category FROM transactions WHERE charged_amount < 0 AND $EXCLUDE_SQL AND date >= '$FROM' ORDER BY ABS(charged_amount) DESC LIMIT 20" --json
Present:
Monthly Trend
| Month | Income | Expenses | Net | Savings Rate |
|---|
| 2026-01 | ₪16,000 | ₪13,500 | +₪2,500 | 15.6% |
| 2026-02 | ₪15,000 | ₪12,800 | +₪2,200 | 14.7% |
Top 20 Largest Transactions
| # | Merchant | Amount | Date | Category |
|---|
| 1 | Rent Payment | ₪5,000 | 2026-03-01 | Housing |
Flag one-time/seasonal expenses (annual insurance, holiday spending, repairs) separately — note which items in the top 20 are one-time vs recurring.
Present any alerts from kolshek insights (category spikes, new merchants, large transactions, recurring changes).
Step 5: Installment Obligations (תשלומים)
Run:
kolshek query "SELECT COALESCE(description_en, description) AS merchant, installment_number, installment_total, ROUND(ABS(charged_amount), 2) AS monthly_payment, (installment_total - installment_number) AS remaining_payments, ROUND(ABS(charged_amount) * (installment_total - installment_number), 2) AS remaining_total FROM transactions WHERE installment_total > 1 AND date >= date('now', '-30 days') ORDER BY remaining_total DESC" --json
Present:
Active Installment Plans (תשלומים)
| Merchant | Payment | Progress | Remaining | Total Left |
|---|
| IKEA | ₪450/mo | 3/12 | 9 payments | ₪4,050 |
Monthly installment burden: ₪X
Total remaining obligation: ₪Y
Installments ending within 3 months: ₪Z/mo freed up
Note which installments end soon — this is "automatic" future savings.
Step 6: Savings Opportunities
Using all data collected above, identify savings in three tiers:
A. Immediate Savings (no lifestyle change needed)
Detect recurring subscriptions:
kolshek query "SELECT COALESCE(description_en, description) AS merchant, ROUND(ABS(charged_amount), 2) AS amount, COUNT(DISTINCT strftime('%Y-%m', date)) AS months FROM transactions WHERE charged_amount < 0 AND date >= date('now', '-180 days') GROUP BY merchant, ROUND(ABS(charged_amount), 2) HAVING months >= 3 ORDER BY amount DESC" --json
Look for:
- Duplicate or overlapping subscriptions (e.g., Netflix + Disney+ + HBO)
- Unused recurring charges (small amounts the user may have forgotten)
- Insurance overlaps worth price-shopping
- Telecom/utility plans worth comparing
B. Behavioral Savings (habit changes)
For each discretionary category, analyze spending frequency and suggest concrete targets:
- Not "eat out less" but "reduce from 8 restaurant visits at ₪150 avg to 4 visits — save ₪600/month"
- Identify spending patterns: concentrated on specific days? Impulse purchases? Weekend splurges?
C. Structural Savings (big moves)
- If fixed obligations (housing + loans + insurance) exceed 40% of income, flag as structural
- If installment burden is high, note when it naturally decreases
- If a deficit remains after cutting all discretionary spending, note that income increase is needed — don't just suggest more cutting
Present combined savings table sorted by savings-to-effort ratio:
Savings Opportunities
| # | Opportunity | Current Cost | Monthly Saving | Annual Saving | Effort | Priority |
|---|
| 1 | Cancel duplicate streaming | ₪50/mo | ₪50 | ₪600 | Low | High |
| 2 | Reduce restaurant visits 8→4/mo | ₪1,200/mo | ₪600 | ₪7,200 | Medium | High |
| 3 | Switch mobile plan | ₪120/mo | ₪40 | ₪480 | Low | Medium |
Total potential monthly savings: ₪X
Current monthly deficit: ₪Y
Gap closed by savings: Z%
Always provide concrete numbers. Never give generic advice.
Step 7: Set Budget Targets
Ask the user:
- "Set budget targets based on this analysis" (recommended)
- "Skip budget targets for now"
If yes, propose targets for each spending category. Derive them from actual spending minus identified savings — targets should be challenging but realistic:
Proposed Budget Targets
| Category | Current Avg | Proposed Target | Monthly Saving |
|---|
| Groceries | ₪2,800 | ₪2,500 | ₪300 |
| Restaurants | ₪1,200 | ₪800 | ₪400 |
| Entertainment | ₪800 | ₪600 | ₪200 |
Total monthly spending target: ₪X
Savings rate target: Y% of fixed income
Look good? You can adjust individual categories before I save.
After user approval, write budget.toml to the config directory (resolved in Before You Start):
[targets]
Groceries = 2500
Restaurants = 800
Entertainment = 600
[targets.meta]
updated = "YYYY-MM-DD"
total_spending = 12000
savings_rate_target = 20
If budget.toml already exists, show the current targets alongside proposed new ones and ask the user whether to update.
Step 8: Phased Action Plan
Generate a personalized plan based on the savings opportunities from Step 6:
Week 1 — Quick Wins (est. ₪X/month saved)
Month 1 — Behavioral Changes (est. ₪X/month saved)
Quarter 1 — Structural Changes (est. ₪X/month saved)
Total projected monthly savings: ₪X
Projected savings rate after changes: Y%
Step 9: Summary
Financial Health Snapshot
| Metric | Value |
|---|
| Monthly fixed income | ₪X |
| Monthly expenses | ₪X |
| Current savings rate | X% |
| Mandatory vs discretionary | X% / Y% |
| Active installments | ₪X/mo (₪Y remaining) |
| Identified savings potential | ₪X/mo |
| Budget targets | Set / Not set |
Next steps:
- Run
/kolshek:review monthly to track progress against these targets
- Run
/kolshek:analyze again in 3 months to refresh the deep dive
