Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rahulcommercial/claude-code-for-indian-traders --skill daily-pnl-reviewergit clone --depth 1 https://github.com/rahulcommercial/claude-code-for-indian-tradersWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rahulcommercial/claude-code-for-indian-traders/daily-pnl-reviewer)<a href="https://agentmods.dev/skills/rahulcommercial/claude-code-for-indian-traders/daily-pnl-reviewer"><img src="https://agentmods.dev/badge/skills/rahulcommercial/claude-code-for-indian-traders/daily-pnl-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rahulcommercial/claude-code-for-indian-traders/daily-pnl-reviewer"><img src="https://agentmods.dev/badge/skills/rahulcommercial/claude-code-for-indian-traders/daily-pnl-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.00669 |
| Opus 5 | $0.00036 | $0.00334 |
| Sonnet 5 | $0.00014 | $0.00134 |
| Haiku 4.5 | $0.00007 | $0.00067 |
Grade A, and why
daily-pnl-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 77 lines · 71 tokens per session scan A de81089910b6
daily-pnl-reviewer is a skill published in the GitHub repository rahulcommercial/claude-code-for-indian-traders (2 stars, last pushed 3mo ago), licensed MIT. It adds 71 tokens to every session and 669 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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