| name | kite-portfolio |
| description | Analyses a Zerodha Kite stock portfolio OR performs on-demand stock deep-dive. Main entry for kite-portfolio. Use when the user says /portfolio or wants to choose which analysis to run. For direct sub-skill invocations use /kite-portfolio:performance, /kite-portfolio:stage, /kite-portfolio:full, or /kite-portfolio:stock <TICKER>.
|
Kite Portfolio Analysis Skill
MCP Tools used by this skill
mcp__kite__loginmcp__kite__get_profilemcp__kite__get_holdingsmcp__kite__get_historical_datamcp__kite__search_instruments (stock-analyser only)mcp__kite__get_ltp (stock-analyser only)
Modules
Three modules — choose based on user intent:
- Module 1 — Performance vs Benchmarks →
performance.md
- Module 2 — Stage & Earnings Analysis (Fundamental Scoring) →
stage-analysis.md
- Module 3 — Stock Analyser (on-demand deep-dive) →
stock-analyser.md
For full portfolio review: Module 1 writes HTML Parts 1–2, Module 2 appends Parts 3–4 + Tab 5 shell.
For /kite-portfolio:stock <TICKER>: Run Module 3 standalone. Does NOT require holdings data.
Module Routing — Read FIRST
If the invocation is /kite-portfolio:stock <TICKER> or any "analyse TICKER / deep dive on TICKER / research TICKER" request:
→ Module 3 V3. MCP calls happen HERE (in this session). analyse.mjs only handles the web-research + JSON-schema Claude call — it has no MCP access.
Step A — Cache check
ls ~/.portfolio/cache/<TICKER>-*.json 2>/dev/null | head -1
If a file exists and is <7 days old (and no --refresh): print cache hit, open browser, done.
Step B — Resolve ticker + fetch candles (THIS session, via MCP)
mcp__kite__search_instruments(query="<TICKER>", exchange="NSE") # try BSE if not found
mcp__kite__get_ltp(instruments=["<EXCHANGE>:<TRADINGSYMBOL>"])
mcp__kite__get_historical_data(instrument_token=<token>, from_date="<2Y ago> 00:00:00", to_date="<TODAY> 23:59:59", interval="day")
Step C — Compute technicals inline (Python via Bash)
Run this Python snippet to compute MAs, RSI, stage, and derived metrics from the candle data saved to a temp file:
python3 << 'PYEOF'
import json, sys
candles = json.load(open('/tmp/<TICKER>-candles.json'))
closes = [c['close'] for c in candles]
volumes = [c['volume'] for c in candles]
n = len(closes)
def ma(d): sl = closes[-d:] if n>=d else closes; return round(sum(sl)/len(sl),2)
ma50,ma150,ma200 = ma(50),ma(150),ma(200)
closes30ago = closes[:-30] if n>30 else closes
ma200_30ago = round(sum(closes30ago[-200:])/min(200,len(closes30ago)),2)
slope200 = round(ma200-ma200_30ago,2)
close_today = closes[-1]
high_1y = max(closes[-252:] if n>=252 else closes)
low_1y = min(closes[-252:] if n>=252 else closes)
def rsi14(cl):
if len(cl)<15: return 50
g=l=0
for i in range(len(cl)-13,len(cl)):
d=cl[i]-cl[i-1]
if d>0: g+=d
else: l+=abs(d)
ag,al=g/13,l/13
return 100 if al==0 else round(100-100/(1+ag/al))
rsi = rsi14(closes)
vol_avg_30d = round(sum(volumes[-30:])/30)
if ma50>ma150>ma200 and close_today>ma50 and slope200>0: stage='Stage 2B'
elif close_today>ma200 and slope200>0: stage='Stage 2A'
elif close_today>ma200 and abs(slope200)<0.5: stage='Stage 1'
elif close_today<ma200 and slope200>0: stage='Stage 3'
else: stage='Stage 4'
h = {'Stage 2B':30,'Stage 2A':25,:15,:8,:0}[stage]
pct_from_high = round((close_today-high_1y)/high_1y*,)
pct_above_200 = round((close_today-ma200)/ma200*,)
vol_ratio = round(volumes[-]/vol_avg_30d,) if vol_avg_30d else
print(json.dumps({"stage":stage,"rsi14":rsi,"h_score":h,"ma50":ma50,"ma150":ma150,"ma200":ma200,"slope200":slope200,"close_today":close_today,"high_1y":high_1y,"low_1y":low_1y,"pct_from_high":pct_from_high,"pct_above_200ma":pct_above_200,"volume_ratio":vol_ratio,"closes_14d":closes[-:],"volume_avg_30d":vol_avg_30d,"volume_today":volumes[-]}))
PYEOF
Step D — Run web research + scoring as a subagent (PRIMARY PATH)
Do NOT use analyse.mjs or claude --print subprocess — they time out. Always use Agent tool directly.
Spawn a subagent with this prompt (fill in the actual technical values from Step C):
You are a stock analyst. Perform a comprehensive deep-dive on <COMPANY NAME> (NSE: <TICKER>).
Return ONLY a valid JSON object in the EXACT schema below — no markdown, no commentary.
Technical context (do NOT recompute):
- Stage: <stage>, RSI14: <rsi14>, MA50: <ma50>, MA150: <ma150>, MA200: <ma200>
- Close: <close_today> | 52W High: <high_1y> | 52W Low: <low_1y>
- % from 52W high: <pct_from_high>% | % above 200MA: <pct_above_200ma>%
- Slope200: <slope200> | Volume ratio: <volume_ratio>x
Search these sources only (WebSearch): screener.in, trendlyne.com, tickertape.in,
moneycontrol.com, economictimes.indiatimes.com, businessstandard.com, livemint.com,
bseindia.com, nseindia.com, valueresearchonline.com
Run these 6 WebSearches in parallel:
1. "<TICKER> NSE quarterly results PAT revenue FY25 FY26 screener.in"
2. "<TICKER> Q4 FY26 concall management commentary order book"
3. "<TICKER> moat competitive advantage business model"
4. "<TICKER> balance sheet debt FCF ROE FY25 FY26"
5. "<TICKER> sector outlook 2025 2026"
6. "<TICKER> valuation PE ratio peers comparison"
SCORING RUBRIC (0–100):
A. Earnings Growth (0–10): accelerating PAT YoY = 9-10, stable growth = 6-8, flat = 4-5, declining = 0-3
B. Management Credibility (0–10): concall quality, guidance accuracy, promoter holding
C. Moat Strength (0–10): pricing power, switching costs, IP
D. Balance Sheet (0–10): D/E, FCF, ROE >15%
E. Sector Tailwind (0–10): policy, cycle
F. Competitive Position (0–10): market share trend
G. Valuation (0–10): P/E vs 3Y avg — cheap=8-10, fair=5-7, expensive=0-4
H. Technical Stage (0–30): Stage 2B=30, 2A=25, 1=15, 3=8, 4=0 → USE <h_score> (already computed)
Action: 85-100=STRONG ADD, 70-84=ADD, 55-69=STRONG HOLD, 40-54=HOLD, 30-39=WATCH, 15-29=TRIM, 0-14=EXIT
REQUIRED JSON SCHEMA (schema_version 3.0 — this exact structure, no deviations):
{
"meta": { "ticker": "...", "company": "...", "exchange": "NSE", "generated_at": "...", "report_date": "YYYY-MM-DD", "expires_at": "...", "ttl_days": 7, "schema_version": "3.0" },
"price": { "last_price": 0.0, "day_change_pct": null, "high_1y": 0.0, "low_1y": 0.0, "pct_from_high": 0.0 },
"technical": { "stage": "...", "stage_emoji": "🟢/🟡/🔴", "ma50": 0.0, "ma150": 0.0, "ma200": 0.0, "slope200": 0.0, "pct_above_200ma": 0.0, "rsi_14": 0, "volume_ratio": 0.0 },
"score": {
"total": 0, "action": "...", "action_css": "action-add/action-hold/action-trim/action-watch/action-exit",
"score_label": "...",
"dimensions": {
"earnings_growth": { "score": 0, "max": 10, "label": "Earnings Growth", "color": "green/amber/red" },
"mgmt_credibility": { "score": 0, "max": 10, "label": "Management Credibility", "color": "green/amber/red" },
"moat": { "score": 0, "max": 10, "label": "Moat Strength", "color": "green/amber/red" },
"balance_sheet": { "score": 0, "max": 10, "label": "Balance Sheet", "color": "green/amber/red" },
"sector_tailwind": { "score": 0, "max": 10, "label": "Sector Tailwind", "color": "green/amber/red" },
"competitive": { "score": 0, "max": 10, "label": "Competitive Position", "color": "green/amber/red" },
"valuation": { "score": 0, "max": 10, "label": "Valuation", "color": "green/amber/red" },
"technical_stage": { "score": 0, "max": 30, "label": "Technical Stage", "color": "green/amber/red" }
}
},
"earnings": {
"quarters": [
{ "label": "Q3FY26", "revenue_cr": 0.0, "revenue_est_cr": 0.0, "ebitda_cr": 0.0, "pat_cr": 0.0, "pat_est_cr": 0.0, "eps": 0.0, "yoy_rev_pct": 0.0, "yoy_pat_pct": 0.0, "beat_miss_rev": "BEAT/MISS/IN-LINE", "beat_miss_pat": "BEAT/MISS/IN-LINE", "market_reaction_pct": null, "analyst_sentiment": "POSITIVE/NEUTRAL/NEGATIVE" }
],
"ttm_pe": 0.0, "de_ratio": 0.0, "roe": 0.0, "roce": 0.0, "fcf_positive": false,
"sector_pe_avg": null, "sector_rev_growth_avg": null, "earnings_trend": "accelerating/stable/decelerating"
},
"concall": {
"quarter": "...", "source_url": "...", "attendees": ["..."],
"overall_sentiment": "POSITIVE/CAUTIOUSLY_POSITIVE/NEUTRAL/NEGATIVE",
"summary": "...",
"analyst_pulse": { "sentiment": "...", "consensus": "...", "target_range_low": null, "target_range_high": null, "analyst_count": null },
"sections": {
"strategic_updates": { "sentiment": "...", "heading": "Strategic Updates", "points": [{ "text": "...", "polarity": "positive/neutral/negative" }] },
"guidance_outlook": { "sentiment": "...", "heading": "Guidance Outlook", "near_term": [{ "text": "...", "polarity": "...", "timeline": "..." }], "medium_term": [], "long_term": [] },
"risk_analysis": { "sentiment": "...", "heading": "Risk Analysis", "points": [{ "text": "...", "polarity": "negative" }] },
"qa_summary": { "sentiment": "...", "heading": "Q&A Summary", "exchanges": [{ "q": "...", "a": "...", "assessment": "DIRECT/PARTIALLY DIRECT/EVASIVE — reason", "polarity": "..." }] },
"earning_triggers": { "sentiment": "...", "heading": "Earning Triggers", "points": [{ "text": "...", "polarity": "...", "timeline": "...", "probability": "HIGH/MEDIUM/LOW" }] },
"management_consistency": { "sentiment": "...", "heading": "Management Consistency", "follow_up_questions": ["..."] }
}
},
"management_integrity": {
"composite_grade": "A/B+/B/C+/C/D", "delivery_rate_pct": 0, "transparency_on_misses": "...", "analyst_treatment": "...", "tone_consistency": "...", "grade_rationale": "...",
"quarters": [{ "quarter": "...", "promise": "...", "delivered": "BEAT/IN-LINE/MISSED/PENDING", "actual": "...", "red_flags": [], "green_flags": [] }]
},
"forensics": {
"pl_3year": [{ "year": "FY24", "revenue_cr": 0.0, "ebitda_margin_pct": 0.0, "pat_cr": 0.0, "pat_margin_pct": 0.0, "roe_pct": 0.0, "roce_pct": 0.0, "eps": 0.0 }],
"cashflow_3year": [{ "year": "FY24", "cfo_cr": 0.0, "capex_cr": 0.0, "fcf_cr": 0.0, "cfo_pat_ratio": 0.0 }],
"balance_sheet": { "current_ratio": 0.0, "de_ratio": 0.0, "cash_cr": 0.0, "total_debt_cr": 0.0, "intangibles_cr": 0.0, "intangibles_flag": false, "intangibles_note": "...", "working_capital_days": 0 },
"red_flags": [{ "category": "...", "rating": "RED/AMBER/GREEN", "finding": "...", "watchlist": "..." }],
"ratios_12": [{ "name": "P/E (TTM)", "value": "...", "trend": "improving/stable/deteriorating", "traffic_light": "GREEN/AMBER/RED" }],
"forensics_verdict": "..."
},
"competitive": {
"industry_overview": "...", "company_positioning": "...", "player_type": "innovation-led/cost-led/scale-led", "position_trend": "GAINING/STABLE/LOSING",
"peers": [{ "name": "...", "market_cap_cr": 0, "revenue_cr": 0, "pat_cr": 0, "ebitda_margin_pct": 0.0, "pe": 0.0, "ev_revenue": 0.0, "roe_pct": 0.0, "market_share": "..." }],
"moat_radar": [{ "dimension": "...", "rating": 0, "max": 3, "label": "...", "note": "..." }]
},
"triggers": [{ "name": "...", "type": "...", "revenue_impact": "...", "timeline": "...", "probability": "HIGH/MEDIUM/LOW" }],
"sector": {
"name": "...",
"definition": "2-3 sentence plain-English description of what this sector does and why it matters",
"tam_usd_bn": 0,
"india_share_pct": 0.0,
"cagr_pct": 0.0,
"cycle": "Early Upcycle/Mid Cycle/Late Cycle/Downcycle",
"cycle_rationale": "...",
"positive_triggers": [{ "trigger": "...", "timeline": "...", "impact": "HIGH/MEDIUM/LOW" }],
"negative_triggers": [{ "trigger": "...", "timeline": "...", "impact": "HIGH/MEDIUM/LOW" }],
"company_position": "...",
"watchlist_metrics": ["...", "..."],
"sector_attractiveness": "⭐ Sector Rating: HIGH/MEDIUM/LOW — one line reason"
},
"verdict": {
"score": 0, "action": "...", "key_insight": "...",
"target_high": 0, "target_low": 0, "upside_pct": 0.0, "downside_pct": 0.0,
"entry_zone": "₹X–₹Y", "support": 0, "resistance": 0, "stop_loss": 0,
"key_monitorable": "...",
"valuation_check": "...",
"action_rationale": "...",
"bull_case": { "probability_pct": 0, "target": 0, "assumptions": ["...", "..."] },
"base_case": { "probability_pct": 0, "target": 0, "assumptions": ["...", "..."] },
"bear_case": { "probability_pct": 0, "target": 0, "assumptions": ["...", "..."] }
},
"references": [
{ "section": "earnings/concall/forensics/sector/competitive/management/technical", "source": "Screener.in", "url": "https://...", "note": "what was extracted" }
]
}
IMPORTANT — after writing JSON, add candle data via Python patch (Step E below).
After the subagent returns, write the JSON then run Step E, then open the report.
Step E — Patch candles + QoQ into JSON (after subagent returns)
python3 << 'PYEOF'
import json
import os; base = os.path.expanduser('~/.portfolio/stock-reports')
data = json.load(open(f'{base}/<TICKER>/latest.json'))
candles = json.load(open('/tmp/<TICKER>-candles.json'))
data['candles'] = [{'date': c['date'][:10], 'open': c['open'], 'high': c['high'], 'low': c['low'], 'close': c['close'], 'volume': c['volume']} for c in candles]
qs = data['earnings']['quarters']
for i, q in enumerate(qs):
if i + 1 < len(qs):
prev = qs[i + 1]
q['qoq_rev_pct'] = round((q['revenue_cr'] - prev['revenue_cr']) / prev['revenue_cr'] * 100, 1) if prev['revenue_cr'] else None
q['qoq_pat_pct'] = round((q['pat_cr'] - prev['pat_cr']) / prev['pat_cr'] * 100, 1) if prev['pat_cr'] else None
else:
q['qoq_rev_pct'] = None; q['qoq_pat_pct'] = None
json.dump(data, open(f'{base}/<TICKER>/latest.json', 'w'), indent=2)
print(f"Patched. Candles: {len(data['candles'])}")
PYEOF
After writing JSON, open both URLs:
open http://localhost:7891/stock/<TICKER>
open "http://localhost:7891/report/stock/<TICKER>"
Report the summary to the user:
Stock: <TICKER> — <company>
Score: <N>/100 → <ACTION>
Stage: <stage> | RSI: <rsi>
Opened: http://localhost:7891/report/stock/<TICKER>
Do NOT call get_profile() or get_holdings() for this path.
If the invocation is /portfolio, /kite-portfolio:performance, /kite-portfolio:stage, or /kite-portfolio:full:
→ Continue to Step 0 below.
Step 0 — ALWAYS FIRST for portfolio analysis (parallel)
mcp__kite__get_profile()
mcp__kite__get_holdings()
Extract per stock: tradingsymbol, exchange, instrument_token, quantity,
average_price, last_price, pnl, day_change_percentage
Compute once and reuse:
cost_value = quantity × average_pricecurrent_value = quantity × last_pricetotal_invested = Σ cost_valuetotal_current = Σ current_valuetotal_pnl = total_current − total_investedtotal_return_pct = (total_pnl / total_invested) × 100day_change_abs = last_price × (day_change_percentage/100) × quantity per stocktotal_day_change = Σ day_change_absweight = current_value / total_current per stock- tracking = True if
weight ≤ 0.002 (0.2%)
instrument_token is read directly from holdings — never call search_instruments.
Benchmark tokens (hardcoded — do not search)
| Index | Token |
|---|
| NIFTY 50 | 256265 |
| NIFTY 500 | 268041 |
| NIFTY SMLCAP 250 | 267273 |
Tracking Position Rule
Weight ≤ 0.2% → label TRACKING. Never recommend EXIT based on size alone.
Full stage + concall analysis still applies.
Speed Rules (finish in under 5 minutes)
Rule 1: Fire ALL historical data calls in ONE parallel batch (all stocks + benchmarks simultaneously).
Rule 2: Use from_date = today minus 365 days for ALL fetches. Single dataset serves both modules.
Rule 3: Fire ALL WebSearches in parallel with historical data fetches.
Rule 4: Write HTML in sequential Bash appends (4 parts, max 200 lines each). Never write full HTML in one string.
Rule 5: Extract ONLY these values from each candle dataset:
close_today, close_30d, close_90d, close_180d, close_365dma50, ma150, ma200 (approx if <200 trading days available)slope200 = ma200[today] − ma200[30d ago]high_1y, low_1ypct_from_high = ((close_today − high_1y) / high_1y) × 100 ← always ≤ 0pct_above_200ma = ((close_today − ma200) / ma200) × 100
Rule 6: Guards:
- Empty holdings → "No holdings found. Check your Kite session." and stop.
- Stock with <50 trading days → skip MA classification; show "INSUFFICIENT DATA".
- Colour palette exhaustion (>11 stocks) → cycle back with 60% opacity.
- Stage 1 and Stage 2 tracked separately — never merge into "uptrend capital".
Authentication
On session error:
mcp__kite__login()
Show login URL as markdown link. Wait for user confirmation.
Bridge Server Check
Before opening the portfolio report, check if the bridge server is running:
curl -s --max-time 2 http://localhost:7891/health
- If
{"status":"ok",...} → open report at http://localhost:7891/report (same-origin, Tab 5 works)
- If connection refused → open
~/Desktop/portfolio-report-YYYY-MM-DD.html directly, and note: "Start cd portfolio-bridge && npm start to enable Tab 5 Stock Analyser."
Why the bridge URL? Browsers block fetch() from file:// to localhost (CORS null-origin restriction). The bridge serves the report at http://localhost:7891/report so all Tab 5 fetch calls are same-origin and work without any restrictions.
JSON Output (replaces HTML generation)
Claude no longer generates HTML. Instead, write a single JSON file per run.
The static report/report.html (served by the bridge) reads JSON and renders client-side.
Module 1 only: writes ~/.portfolio/data/portfolio-YYYY-MM-DD.json with meta, portfolio (partial), benchmarks, holdings[].returns.
Module 2 only: writes same file with meta, portfolio (full), holdings[].technical/fundamental_score/earnings/concall/action/risk_flags.
Full review: Module 1 writes first, Module 2 reads, merges, and overwrites with complete data.
JSON data path: ~/.portfolio/data/portfolio-YYYY-MM-DD.json
Latest symlink: ~/.portfolio/data/latest.json
Schema reference: docs/portfolio-data-schema.md
Token budget: Module 1 JSON ≤ 1,500 tokens. Module 2 JSON ≤ 2,000 tokens. Total ≤ 3,000 tokens (vs ~12,000 for HTML generation).
After writing JSON, open the report:
curl -s --max-time 1 http://localhost:7891/health > /dev/null 2>&1 \
&& open http://localhost:7891/report \
|| echo "Bridge not running. Start: cd portfolio-bridge && npm start"
Output (chat — brief)
For portfolio report: current value, total P&L %, top 3 action flags (with scores), and
"Report saved to Desktop and opened in your browser."
For /kite-portfolio:stock: ticker, score/100, action, 1-line key insight, standalone HTML path.
Reference files
performance.md — Module 1: benchmark comparison + JSON write (performance fields)stage-analysis.md — Module 2: stage classification + fundamental scoring + JSON write (stage/concall fields)stock-analyser-v2.js — Module 3 V2: Claude Workflow script with 6 phases and eval gates (active)stock-analyser.md — Module 3 V1: reference spec only (not executed directly)json-output.md — JSON write spec, schema examples, merge pattern, token budgetdocs/portfolio-data-schema.md — full JSON field reference (master contract)docs/sample-portfolio-data.json — complete sample JSON for referencedocs/prompt-library-index.md — prompt ID → section mapping for stock-analyserreport/report.html — static report UI (never modify from skill — managed separately)
