How do I install the Daily Pnl Reviewer skill?

Run npx skillmds@latest add rahulcommercial/daily-pnl-reviewer in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Daily Pnl Reviewer safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Daily Pnl Reviewer?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Daily Pnl Reviewer free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Daily Pnl Reviewer?

rahulcommercial (@rahulcommercial) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Daily Pnl Reviewer by rahulcommercial

Name: Daily Pnl Reviewer
Author: rahulcommercial

Daily Pnl Reviewer

End-of-day review skill for Indian intraday traders — analyses today's trades, computes win rate / expectancy / max DD, flags behavioural patterns (revenge trading, oversizing, time-of-day skew), and writes a journal entry. Trigger after market close, when reviewing trade logs, or when user says "review my day".

File contents

Daily P&L Reviewer

The trade is only half the work. The review is where edge gets built.

When to use

After 15:30 IST when market is closed.
User uploads / points to today's trade log (JSON / CSV / SQLite).
User asks "how did I do today" or "review my trades".

Inputs expected

A list of trades with at minimum:

entry_time, exit_time
instrument, side, qty
entry_price, exit_price
pnl (₹)
rationale / setup tag (optional but valuable)

Metrics to compute

Performance

Net P&L (after estimated STT + brokerage + GST)
Win rate = wins / total
Avg win, avg loss, expectancy = (win_rate × avg_win) − (loss_rate × avg_loss)
Profit factor = total wins ₹ / total losses ₹

Risk

Max intraday drawdown
Largest single loss as % of starting capital
Did daily loss cap trigger?

Behavioural flags (these matter more than P&L)

Flag	How to detect
Revenge trading	New entry within 5 min of a losing exit, larger size than baseline
Oversizing	Position size > 2× median for the day
Late-day chasing	New entries after 14:45 IST that aren't part of a closing-bell setup
Setup drift	Trades without a tagged setup, or "vibe" entries
Holding losers	Avg time-in-loss > 1.5× avg time-in-win

Output: the journal entry

Write to ~/projects/zerodha-signal-app/journal/YYYY-MM-DD.md (or user-specified path):

# 2026-06-06 — Trading Journal

## Numbers
- Net P&L: ₹+2,340 (2.3% on ₹1L)
- Trades: 6 (4W / 2L), win rate 67%
- Expectancy: ₹+390 / trade
- Max DD: ₹-1,100 at 11:42 IST

## What worked
- 09:30 ORB on BankNifty — clean, on-plan, sized right.

## What didn't
- 13:15 CE buy on Nifty — entered without OI confirmation, exited for -₹600.

## Behavioural flags
- ⚠ Revenge entry at 13:25 (within 5 min of loss). Recovered, but pattern noted.

## Tomorrow's focus
- Don't enter inside 12:30–13:30 chop unless setup is A+.

Tone

Honest, blunt, no pep talk. Treat the user as an adult building a process.

---name: daily-pnl-reviewerdescription: End-of-day review skill for Indian intraday traders — analyses today's trades, computes win rate / expectancy / max DD, flags behavioural patterns (revenge trading, oversizing, time-of-day skew), and writes a journal entry. Trigger after market close, when reviewing trade logs, or when user says "review my day".---# Daily P&L ReviewerThe trade is only half the work. The review is where edge gets built.## When to use- After 15:30 IST when market is closed.- User uploads / points to today's trade log (JSON / CSV / SQLite).- User asks "how did I do today" or "review my trades".## Inputs expectedA list of trades with at minimum:- entry_time, exit_time- instrument, side, qty- entry_price, exit_price- pnl (₹)- rationale / setup tag (optional but valuable)## Metrics to compute### Performance- Net P&L (after estimated STT + brokerage + GST)- Win rate = wins / total- Avg win, avg loss, **expectancy** = (win_rate × avg_win) − (loss_rate × avg_loss)- Profit factor = total wins ₹ / total losses ₹### Risk- Max intraday drawdown- Largest single loss as % of starting capital- Did daily loss cap trigger?### Behavioural flags (these matter more than P&L)| Flag | How to detect ||---|---|| **Revenge trading** | New entry within 5 min of a losing exit, larger size than baseline || **Oversizing** | Position size > 2× median for the day || **Late-day chasing** | New entries after 14:45 IST that aren't part of a closing-bell setup || **Setup drift** | Trades without a tagged setup, or "vibe" entries || **Holding losers** | Avg time-in-loss > 1.5× avg time-in-win |## Output: the journal entryWrite to `~/projects/zerodha-signal-app/journal/YYYY-MM-DD.md` (or user-specified path):```markdown# 2026-06-06 — Trading Journal## Numbers- Net P&L: ₹+2,340 (2.3% on ₹1L)- Trades: 6 (4W / 2L), win rate 67%- Expectancy: ₹+390 / trade- Max DD: ₹-1,100 at 11:42 IST## What worked- 09:30 ORB on BankNifty — clean, on-plan, sized right.## What didn't- 13:15 CE buy on Nifty — entered without OI confirmation, exited for -₹600.## Behavioural flags- ⚠ Revenge entry at 13:25 (within 5 min of loss). Recovered, but pattern noted.## Tomorrow's focus- Don't enter inside 12:30–13:30 chop unless setup is A+.```## ToneHonest, blunt, no pep talk. Treat the user as an adult building a process.

Frequently asked questions