What is signal-postmortem?

A review tool for comparing past trading signals with their later results over five- and twenty-day periods. It labels outcomes such as correct calls, false positives, missed opportunities, and market-regime mismatches.

Use it after trades close, to review batches of signals, find false-positive patterns, adjust signal-combination weights, and create a backlog of improvements.

It turns completed trade outcomes into a record of what worked and what failed, so recurring errors can be identified and signal methods can be improved.

signal-postmortem — Skill for Claude Code and Codex by BaggaT236

Install

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.

Any agent

npx skills add BaggaT236/AI-Trading-Skills --skill signal-postmortem

Clone the repo

git clone --depth 1 https://github.com/BaggaT236/AI-Trading-Skills

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/signal-postmortem/github.svg)](https://agentmods.dev/skills/baggat236/ai-trading-skills/signal-postmortem)

Your own site

<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/signal-postmortem"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/signal-postmortem/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/signal-postmortem"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/signal-postmortem.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,693 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 100% copy Near-identical to another mod in the catalogue.

Token cost

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	$0.00050	$0.01693
Opus 5	$0.00025	$0.00847
Sonnet 5	$0.00010	$0.00339
Haiku 4.5	$0.00005	$0.00169

Measured 11d ago against content hash 719ed5ee385d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

signal-postmortem 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 11d ago.

The scan reads SKILL.md. This mod also ships 5 executable files (scripts/postmortem_analyzer.py, scripts/postmortem_recorder.py, scripts/tests/conftest.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

This is a copy

100% identical to signal-postmortem — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/signal-postmortem/SKILL.md · 208 lines

How it starts

The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Signal Postmortem

Overview

Signal Postmortem records and analyzes the outcomes of trading signals generated by the edge pipeline, screeners, and other skills. It compares predicted edge direction against 5-day and 20-day realized returns, categorizes outcomes (true positive, false positive, missed opportunity, regime mismatch), and generates feedback for edge-signal-aggregator weight adjustments and skill improvement backlog entries.

When to Use

After a trade has been closed and you want to record the outcome
When reviewing a batch of signals that have reached their holding period (5 or 20 days)
To identify systematic false positive patterns from specific skills
To generate feedback for edge-signal-aggregator weight calibration
When building a skill improvement backlog from decision quality metrics
For periodic (weekly/monthly) signal quality audits

Prerequisites

Python 3.9+
FMP API key (optional, for fetching realized returns if not provided manually)
Standard library + requests for API calls
Input: signal records in JSON format (from edge-signal-aggregator or screener outputs)

API Key Setup (Optional)

If you want to automatically fetch price data for return calculations, set up the FMP API key:

export FMP_API_KEY=your_api_key_here

Alternatively, pass the key via command line with --api-key YOUR_KEY. Without an API key, you can still record outcomes manually by providing --exit-price and --exit-date.

Workflow

Step 1: Prepare Signal Records

Gather closed or matured signal records. Each record should include:

signal_id: Unique identifier
ticker: Stock symbol
signal_date: Date signal was generated
predicted_direction: LONG or SHORT
source_skill: Which skill generated the signal
entry_price: Price at signal generation (optional, for manual override)

# Example: List signals ready for postmortem (5+ days old)
python3 skills/signal-postmortem/scripts/postmortem_recorder.py \
  --list-ready \
  --signals-dir state/signals/ \
  --min-days 5

Read the full file on GitHub · 208 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

11d ago First seen · 208 lines · 50 tokens per session scan A 719ed5ee385d

Subscribe to this mod's changes

signal-postmortem is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 7d ago), licensed MIT. It adds 50 tokens to every session and 1,693 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to signal-postmortem, differing in 0 lines, and is treated as a copy.

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