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Reflect Performance Review

Organization
Nunchi-trade
reflect-performance-review

Reflect, Evaluate, Fine-tune, Learn, Evolve, Correct, Transform — nightly automated performance review

Overview

PublisherNunchi-trade
Repositoryagent-cli
Skill namereflect-performance-review
Stars
521
Forks
136
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Nunchi-trade on GitHub. Read the source before you install it.

Installation

Install the Reflect Performance Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Nunchi-trade/agent-cli/tree/main/skills/reflect
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Nunchi-trade/agent-cli.git /tmp/agent-cli
mkdir -p .claude/skills
cp -r /tmp/agent-cli/skills/reflect .claude/skills/reflect-performance-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reflect Performance Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Reflect Performance Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Reflect Performance Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

REFLECT — Reflect, Evaluate, Fine-tune, Learn, Evolve, Correct, Transform

Nightly automated performance review engine. APEX runs all day — REFLECT reviews every trade at night, computing metrics, detecting patterns, and producing data-driven improvement recommendations.

Architecture

trades.jsonl → ReflectEngine.compute() → ReflectMetrics → ReflectReporter.generate() → report.md
  1. Load: Read trade log from data/cli/trades.jsonl
  2. Pair: FIFO round-trip matching (buys to sells per instrument)
  3. Compute: Win rate, PF, FDR, holding periods, direction analysis, monster dependency
  4. Recommend: Rule-based recommendations from metrics
  5. Report: Full markdown report saved to data/reflect/YYYY-MM-DD.md
  6. Distill: 3-5 line summary for agent memory

Key Metrics

MetricFormulaHealthy Range
Win Ratewinning_trades / total_trades> 50%
Profit Factor (Gross)gross_wins / gross_losses> 1.5
Profit Factor (Net)(gross_wins - fees) / gross_losses> 1.2
FDR (Fee Drag Ratio)total_fees / gross_wins * 100< 20%
Monster Dependencybest_trade_pnl / net_pnl * 100< 50%
Max Consecutive Losseslongest loss streak< 5

Usage

bash
hl reflect run                      # Review since last report
hl reflect run --since 2026-03-01   # Review from specific date
hl reflect report                   # View latest report
hl reflect report --date 2026-03-03 # View specific date
hl reflect history                  # Show metric trend over time
hl reflect history -n 30            # Last 30 reports

Agent Mandate

You are the REFLECT reviewer. Your job is to analyze every trade from the past session, compute performance metrics, identify weaknesses, and produce actionable recommendations. You run nightly — the APEX runs by day, you review at night.

RULES:

  • Run REFLECT every night after trading stops — no exceptions
  • ALWAYS read the full report before the next trading session
  • Act on CRITICAL recommendations immediately (FDR > 30%, win rate < 35%)
  • Track recommendations across reports — if the same issue appears 3+ times, escalate
  • Save the distilled summary to agent memory for next-session context
  • NEVER ignore FDR warnings — fees silently kill profitability

Decision Rules

Metric StateSeverityAction
FDR > 30%CRITICALReduce trade frequency or widen entry criteria immediately
FDR 20-30%WARNINGMonitor — consider reducing size or frequency
FDR < 20%OKFees are manageable
Win rate < 35%CRITICALTighten entry criteria — Radar threshold to 200+
Win rate 35-45%WARNINGReview losing trades for pattern
Win rate > 50%OKEntries are working
Monster dep > 60%WARNINGOne trade carrying the session — diversify alpha
Monster dep > 80%CRITICALFragile — entire PnL depends on one lucky trade
Consec losses > 5WARNINGAdd loss streak circuit breaker to APEX
Long PnL < 0, Short PnL > 0WARNINGLong entries are leaking — reduce long bias
Holding < 5 min dominatesWARNINGOver-trading — increase min hold time

Anti-Patterns

  • Ignoring REFLECT reports: Running APEX without reviewing REFLECT is flying blind. The same mistakes repeat.
  • Acting on single-day anomalies: One bad day doesn't mean the strategy is broken. Look at 5+ day trends via hl reflect history.
  • Optimizing for win rate alone: High win rate with low profit factor means you're taking small wins and large losses. Focus on PF.
  • Not tracking FDR: Fees are invisible during trading but compound devastatingly. FDR is the single most important "hidden" metric.
  • Changing strategy after one REFLECT report: REFLECT recommendations need 3+ consistent appearances before strategy changes.

Error Recovery

ErrorCauseFix
No trades foundNo trading activity in periodNormal — nothing to review
Cannot pair round tripsUnmatched buys/sellsOpen positions — REFLECT pairs only closed trades
trades.jsonl not foundFirst run or wrong data dirRun at least one trade first
Report generation failedDisk full or permissionsCheck data/reflect/ directory permissions

Composition

REFLECT is the learning layer of the APEX system. Run REFLECT nightly after APEX stops. Feed REFLECT insights back into APEX configuration (Radar thresholds, DSL presets, position sizing). Over time, REFLECT recommendations should converge as the system improves.

Cron Template

bash
# Nightly REFLECT review at 11:55 PM
55 23 * * * cd ~/agent-cli && source .venv/bin/activate && hl reflect run >> logs/reflect.log 2>&1

Frequently asked questions

What does the Reflect Performance Review AI skill do?

Reflect, Evaluate, Fine-tune, Learn, Evolve, Correct, Transform — nightly automated performance review

Why use Reflect Performance Review on TypingMind?

Because you install it once and use it with any model. Reflect Performance Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Reflect Performance Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Nunchi-trade/agent-cli/tree/main/skills/reflect. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Reflect Performance Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Reflect Performance Review?

As many as you like. As long as a model supports skills, you can use Reflect Performance Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Reflect Performance Review AI skill free?

Yes. It is published on GitHub by Nunchi-trade under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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