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Postmarket review

Skill Jinchengawu/stock-monitoring-kit/skills/postmarket-review

Create an evidence-first postmarket trading review for A-share, Hong Kong, or US markets. Use when the user asks to review a completed session, compare a premarket plan with outcomes, analyze daily/weekly BBI-KDJ-MACD-volume signals, inspect sector and capital flows, or improve a trading journal.From its SKILL.md

Install
npx -y skills add Jinchengawu/stock-monitoring-kit --skill postmarket-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 20 days oldThe repository was created 20 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

1.7 KB, 278 tokens by cl100k_base, as published. Nobody here has run it

Postmarket Review

Explain what changed during the session, which prior hypotheses survived, and what evidence matters next. Avoid hindsight-based storytelling.

Workflow

  1. Record session, timezone, close timestamp, positions or watchlist supplied by the user, and the premarket plan if available.
  2. Compare expected scenarios with actual index, sector, volume, flow, event, and news outcomes.
  3. If available, use evaluate_strategy on daily and weekly bars for each watched symbol.
  4. Distinguish:
    • Process quality: whether the decision followed the stated rules.
    • Outcome: profit, loss, or no action.
    • New information: facts unknowable before the session.
  5. Explain signal confluence across close-versus-BBI, volume anomaly, KDJ extremes, and latest MACD crossover. Do not infer causality from correlation alone.
  6. Translate findings into tomorrow's verification list, not unconditional orders.

Output

  • Session summary and data cutoff
  • Premarket hypothesis scorecard
  • Index, sector, and incremental-capital review
  • Watchlist daily/weekly signal changes
  • Decision journal: followed rule, deviation, evidence, lesson
  • New risks, catalysts, and invalidation conditions
  • Next-session monitoring checklist
  • Research-only disclaimer

State missing trade records or stale data instead of filling gaps with assumptions.

What ships with it: 1 file

518 B alongside SKILL.md

agents/

Gives 0 of the 12 instructions most review quality skills give in 278 tokens

Counted across 1,048 of the 1,783 authors here whose files we hold, read 2026-08-07

  • Ask questions one at a timein 81 of 1048, across 64 files
  • Provide a recommended answer for each questionin 73 of 1048, across 50 files
  • Explore the codebase instead of asking answerable questionsin 66 of 1048, across 42 files
  • Resolve dependencies between decisions one-by-onein 42 of 1048, across 17 files
  • Interview the user relentlessly about the planin 38 of 1048, across 13 files
  • Order findings by severityin 31 of 1048
  • Resolve each branch of the decision treein 27 of 1048, across 5 files
  • Run a grilling sessionin 26 of 1048, across 5 files
  • Update CONTEXT.md immediately when a term is resolvedin 26 of 1048, across 11 files
  • Propose precise canonical terms for vague languagein 25 of 1048, across 7 files
  • Create documentation files lazilyin 24 of 1048, across 5 files
  • Assign severity to every findingin 24 of 1048

Said here and by no other author read

  • Record session, timezone, close timestamp, positions, and premarket plan
  • Compare expected scenarios with actual market outcomes
  • Evaluate daily and weekly bars for each watched symbol
  • Distinguish process quality, outcome, and new information
  • Explain signal confluence across indicators
  • Translate findings into tomorrow's verification list

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 326,782. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.