Log every setup you pass on, so your weekly review sees both halves
When you pass on a setup you had planned for, log a one-line skip row right away with its grade and your reason. Each weekend an agent separates rule-based skips from unexplained ones, so your review covers the trades you didn't take.
How it works
- Add a skip sheet to your journal with fixed columns: date and time, setup tag, the grade you gave the setup in the moment (your own written grading scale), a reason code, and one free-text line. Reason codes come from your rules (outside my written criteria, risk limit reached, calendar rule) plus two that have no rule behind them: hesitated after a loss, and didn't trust it.
- Write the row within a few minutes of passing, before you know how the setup played out. A row written later gets a late flag and is left out of the weekly comparison.
- Keep the security out of the row the agent reads. The review is about your decision, not the name.
- Each weekend, paste or export the week's skip rows together with your taken trades (setup tag, grade, rule check) and run the review prompt.
- The agent counts skips by reason code and by grade, separates rule-based skips from unexplained ones, and lists every unexplained skip of a top-graded setup with its row number and your own note. It does not judge in hindsight whether a skip was right.
- You decide what follows: a checklist line for the hour after a loss, a playbook note, or nothing. If you ever want outcomes for skipped setups, measure them later with your own tested script, never by asking the model to guess.
Copy-paste prompt
You review the setups I chose not to take. You do not give trading advice, you never say what I should have
taken or should take next, and you never estimate how a skipped setup would have turned out.
Inputs: {{skip_rows}} (date, time, setup_tag, my_grade, reason_code, note, late_flag) and {{taken_rows}}
(date, setup_tag, my_grade, rule_check). My reason codes and what they mean: {{reason_codes}}. Week: {{week}}.
1. Ignore rows with late_flag set, and say how many you ignored.
2. Count skips by reason code and by my grade. Split them into "rule-based" (a code tied to one of my written
rules) and "no rule" (every other code).
3. List every "no rule" skip where my grade was one of {{top_grades}}, by row number and date, quoting my note.
4. Show taken versus skipped counts for each grade, so I can see whether I take my best-graded setups less often
than my weaker ones.
5. If the week has fewer than {{min_rows}} skip rows, write "too few rows to read anything into" after step 2 and stop.
No praise, no reassurance, no price talk and no names of securities.
Guardrails
- The agent never estimates what a skipped setup would have made. It works from counts and your own notes only.
- Rows written after you have seen the outcome carry a late flag and are excluded, so hindsight can't rewrite the log.
- Security names stay out of the rows the agent reads; the review is about decisions.
- The agent returns a summary for you to paste into your journal. Nothing is written without your confirmation.
- The agent has no broker access and cannot place orders.
What can go wrong
The log only works if you write the row at the moment you pass. Filled in on Sunday from memory, it turns into a list of the setups that later worked, and the review then tells you to trust yourself more, which is usually the wrong lesson. The second trap is grade inflation: if every skipped setup becomes an A in hindsight, keep your grading scale written down and compare grade counts against your taken trades. And don't ask the model how a skip "would have done". It will give you a confident number with nothing behind it.
Why it helps
A review built from your trade log only sees the trades you took. Half your decisions are the ones you didn't make, and the costly pattern often hides there: passing on your best-graded setups right after a loss, or because you didn't trust yourself that day. A skip row written in the moment, with a reason code, lets the weekly review tell the difference between a pass your rules required and a pass that was just hesitation.
How it fits with other entries
The rule paper-vs-practice entry (aft-0030) finds missed trades by comparing a historical test with your journal, after the fact. This entry records the pass as it happens, with your grade and reason, and needs no backtest. The weekly stats (aft-0007) and the playbook change (aft-0017) can use the counts as one more input, and the state log (aft-0026) can tell you whether unexplained skips cluster on bad-sleep days.
Where this comes from
A public post by @stackzz on X: a weekly AI review reads your fills, and your fills only know about the trades you clicked. His fix is a skip row written the minute you pass, with the grade and the reason, so the review can "argue with the other half of your decisions". The reason codes, the late flag and the counts-only review are our additions.
Source and references
Based on a public post by @stackzz on X pointing out that a weekly review built from your fills never sees the setups you passed on, and suggesting a skip row with the grade and reason written at the moment you pass. The reason codes, late flag and review prompt are our adaptation, in our own words.
Original: Stackzz on x
- Post on keeping a skip row so the weekly review sees the setups you passed on · Stackzz (@stackzz) on X (checked 19 Oct 2026)
- Hindsight bias · Investopedia (checked 19 Oct 2026)
Spotted an error, or want your post removed? Email hello@agentsfortraders.com.
Educational content only. Not investment advice or a personal recommendation. No tickers, signals or trade alerts. Trading involves risk of loss.
Conteúdo exclusivamente educativo. Não constitui aconselhamento nem recomendação de investimento. Sem tickers, sinais ou alertas de negociação. Negociar envolve risco de perda.
Related use cases
Summarise weekly journal stats by setup, with a minimum sample before any conclusion
Every weekend you paste pre-computed journal stats into an agent, which reports win rate, average R and expectancy by setup tag, regime and weekday, and labels any group below your minimum sample as not enough data.
Journaling & post-trade reviewPropose one evidence-backed playbook change from the weekly review
On Sunday the agent compiles your mistakes and best trades from the journal and proposes at most one change to your written playbook, with the evidence. You approve or reject it.
Discipline, checklists & psychologyFind what comes before your rule breaks with a pre-session state log
Before you trade, rate sleep, stress, mood and focus in a 20-second log. Each week an agent compares those ratings with your rule breaks and checklist skips, and shows a pattern only once the sample is big enough.
Backtesting & rule researchMeasure the gap between a rule on paper and how you actually trade it
For one rule you already use, a script compares the trades an honest historical test says the rule would have taken with the trades your journal shows. You get counts and average R for missed, extra and timing differences, statistics only.
The Daily 3
Three agent workflows for swing traders, free, on US trading days.