Agent skill

Signal Postmortem

by tradermonty in tradermonty/claude-trading-skills

Record and analyze post-trade outcomes for signals generated by edge pipeline and other skills.

Install Signal Postmortem

skills CLI

$ npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install tradermonty/claude-trading-skills signal-postmortem --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/signal-postmortem .claude/skills/signal-postmortem && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "signal-postmortem" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/signal-postmortem into .claude/skills/signal-postmortem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "signal-postmortem", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/tradermonty/claude-trading-skills/tree/main/skills/signal-postmortem

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install tradermonty/claude-trading-skills signal-postmortem --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/signal-postmortem .agents/skills/signal-postmortem && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "signal-postmortem" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/signal-postmortem into .agents/skills/signal-postmortem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "signal-postmortem", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install tradermonty/claude-trading-skills signal-postmortem --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/signal-postmortem .cursor/skills/signal-postmortem && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "signal-postmortem" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/signal-postmortem into .cursor/skills/signal-postmortem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "signal-postmortem", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/tradermonty/claude-trading-skills.git --path skills/signal-postmortem

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install tradermonty/claude-trading-skills signal-postmortem --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/signal-postmortem .gemini/skills/signal-postmortem && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "signal-postmortem" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/signal-postmortem into .gemini/skills/signal-postmortem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "signal-postmortem", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install tradermonty/claude-trading-skills signal-postmortem

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/signal-postmortem .github/skills/signal-postmortem && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "signal-postmortem" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/signal-postmortem into .github/skills/signal-postmortem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "signal-postmortem", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install tradermonty/claude-trading-skills signal-postmortem --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/signal-postmortem .opencode/skills/signal-postmortem && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "signal-postmortem" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/signal-postmortem into .opencode/skills/signal-postmortem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "signal-postmortem", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: signal-postmortem
GitHub stars: 3k
Used in: 1 other repo
Token cost: ~1.7k tokens
SKILL.md length: 455 words
Files: 10 (incl. scripts, references)
Skills in repo: 74
Repo updated: Oct 5, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Record and analyze post-trade outcomes for signals generated by edge pipeline and other skills.

Works in 5 steps: Prepare Signal Records → Record Outcomes → Classify Outcomes → …
Tasks that involve Runbooks and postmortems
SKILL.md covers Overview, When to Use, Prerequisites and Workflow, plus 3 more sections
Runs Python scripts from its folder; calls python3; needs FMP_API_KEY

What it does

Signal Postmortem is an agent skill from tradermonty/claude-trading-skills. Record and analyze post-trade outcomes for signals generated by edge pipeline and other skills. Track false positives, missed opportunities, and regime mismatches. Feed results back to edge-signal-aggregator weights and skill improvement backlog.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts and reference files (for example `references/feedback-integration.md`, `references/outcome-classification.md` and `scripts/postmortem_analyzer.py`).

It sits in DevOps & Cloud, covering Runbooks and postmortems. The repository describes itself as: Claude Code skills for equity investors and traders — market analysis, technical charting, economic calendars, screeners, and trading strategy development. The licence is MIT.

When your agent uses it

Tasks that involve Runbooks and postmortems

Example prompts

“/signal-postmortem”

Requirements

Python 3
A credential in FMP_API_KEY
A credential in YOUR_KEY

Workflow steps

5 steps, taken from the step headings in SKILL.md.

Prepare Signal Records
Record Outcomes
Classify Outcomes
Generate Feedback Files
Review Summary Statistics

What it can do on your machine

Read from SKILL.md and the folder at commit eab8d5c. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 6 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- FMP_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Signal Postmortem loads about 1.7k tokens when it runs, and up to ~4.3k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 455 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~66
When it runs · the whole SKILL.md, loaded when a task matches: ~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~4.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from tradermonty/claude-trading-skills at commit eab8d5c, republished under its MIT licence (© tradermonty). 455 words, ~1,689 tokens.

Download SKILL.mdSave it as .claude/skills/signal-postmortem/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.

name: signal-postmortem
description: Record and analyze post-trade outcomes for signals generated by edge pipeline and other skills. Track false positives, missed opportunities, and regime mismatches. Feed results back to edge-signal-aggregator weights and skill improvement backlog.

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:

bash

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)

bash

# 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

Step 2: Record Outcomes

Run the postmortem recorder to fetch realized returns and classify outcomes.

bash

python3 skills/signal-postmortem/scripts/postmortem_recorder.py \
  --signals-file state/signals/aggregated_signals_2026-03-10.json \
  --holding-periods 5,20 \
  --output-dir reports/

For manual outcome recording (when price data is already available):

bash

python3 skills/signal-postmortem/scripts/postmortem_recorder.py \
  --signal-id sig_aapl_20260310_abc \
  --exit-price 178.50 \
  --exit-date 2026-03-15 \
  --outcome-notes "Closed at target, +3.2% in 5 days" \
  --output-dir reports/

Show full SKILL.md (187 more words)Show less

Step 3: Classify Outcomes

The recorder automatically classifies each signal into one of four categories:

Category	Definition
TRUE_POSITIVE	Predicted direction matched realized return sign
FALSE_POSITIVE	Predicted direction opposite to realized return
MISSED_OPPORTUNITY	Signal not taken but would have been profitable
REGIME_MISMATCH	Signal failed due to market regime change

Classification rules are documented in references/outcome-classification.md.

Step 4: Generate Feedback Files

Generate feedback for downstream consumers:

bash

# Generate weight adjustment suggestions for edge-signal-aggregator
python3 skills/signal-postmortem/scripts/postmortem_analyzer.py \
  --postmortems-dir reports/postmortems/ \
  --generate-weight-feedback \
  --output-dir reports/

# Generate skill improvement backlog entries
python3 skills/signal-postmortem/scripts/postmortem_analyzer.py \
  --postmortems-dir reports/postmortems/ \
  --generate-improvement-backlog \
  --output-dir reports/

Step 5: Review Summary Statistics

Generate aggregate statistics by skill, by ticker, and by time period:

bash

python3 skills/signal-postmortem/scripts/postmortem_analyzer.py \
  --postmortems-dir reports/postmortems/ \
  --summary \
  --group-by skill,month \
  --output-dir reports/

Output Format

Postmortem Record (JSON)

json

{
  "schema_version": "1.0",
  "postmortem_id": "pm_sig_aapl_20260310_abc",
  "signal_id": "sig_aapl_20260310_abc",
  "ticker": "AAPL",
  "signal_date": "2026-03-10",
  "source_skill": "edge-signal-aggregator",
  "predicted_direction": "LONG",
  "entry_price": 172.50,
  "realized_returns": {
    "5d": 0.032,
    "20d": 0.058
  },
  "exit_price": 178.50,
  "exit_date": "2026-03-15",
  "holding_days": 5,
  "outcome_category": "TRUE_POSITIVE",
  "regime_at_signal": "RISK_ON",
  "regime_at_exit": "RISK_ON",
  "outcome_notes": "Clean breakout, held through minor pullback",
  "recorded_at": "2026-03-17T10:30:00Z"
}

Weight Feedback (JSON)

json

{
  "schema_version": "1.0",
  "generated_at": "2026-03-17T10:35:00Z",
  "analysis_period": {
    "from": "2026-02-01",
    "to": "2026-03-15"
  },
  "skill_adjustments": [
    {
      "skill": "vcp-screener",
      "current_weight": 1.0,
      "suggested_weight": 0.85,
      "reason": "15% false positive rate in RISK_OFF regime",
      "sample_size": 42
    }
  ],
  "confidence": "MEDIUM",
  "min_sample_threshold": 20
}

Skill Improvement Backlog Entry (YAML)

yaml

- skill: vcp-screener
  issue_type: false_positive_cluster
  severity: medium
  evidence:
    false_positive_rate: 0.15
    sample_size: 42
    regime_correlation: RISK_OFF
  suggested_action: "Add regime filter or reduce signal confidence in RISK_OFF"
  generated_by: signal-postmortem
  generated_at: "2026-03-17T10:35:00Z"

Summary Report (Markdown)

Reports are saved to reports/ with filenames postmortem_summary_YYYY-MM-DD.md.

Resources

scripts/postmortem_recorder.py -- Records individual signal outcomes
scripts/postmortem_analyzer.py -- Generates feedback and summary statistics
references/outcome-classification.md -- Classification rules and edge cases
references/feedback-integration.md -- How to integrate feedback with downstream skills

Key Principles

Honest Attribution -- Every outcome is attributed to its source skill for accountability
Regime Awareness -- Regime context is recorded to distinguish skill failure from market regime shifts
Minimum Sample Size -- Weight adjustments require 20+ signals for statistical validity
Feedback Loop Closure -- Results flow back to improve both signal aggregation and skill quality

Files

SKILL.md and 9 other files (scripts, references) in skills/signal-postmortem of tradermonty/claude-trading-skills.

SKILL.md
references/feedback-integration.md
references/outcome-classification.md
requirements.txt
scripts/postmortem_analyzer.py
scripts/postmortem_recorder.py
scripts/tests/conftest.py
scripts/tests/test_cli_workflow.py
scripts/tests/test_postmortem_analyzer.py
scripts/tests/test_postmortem_recorder.py

Open the folder on GitHubat commit eab8d5c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in tradermonty/claude-trading-skills, which our catalogue first saw on October 7, 2026.

BaggaT236/AI-Trading-Skillsskills/signal-postmortem

Compare with similar skills

Signal Postmortem next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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GreptimeDB Release RunbookGreptimeTeam/greptimedb	6.7k	—	~1.4k	Automated safety check: Pass	Apache-2.0	today
Statemhenryqin1997/statem	1.3k	—	~1.2k	Automated safety check: Pass	Apache-2.0	1 mo ago

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Questions about Signal Postmortem

What does Signal Postmortem do?

Record and analyze post-trade outcomes for signals generated by edge pipeline and other skills. Signal Postmortem is an agent skill from tradermonty/claude-trading-skills. Record and analyze post-trade outcomes for signals generated by edge pipeline and other skills.

When should I use Signal Postmortem?

Signal Postmortem fits situations like: tasks that involve Runbooks and postmortems.

How do I install Signal Postmortem in Claude Code?

Run `npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a claude-code`. Or copy the skill folder (skills/signal-postmortem in tradermonty/claude-trading-skills) into .claude/skills/signal-postmortem in your project. Claude Code loads it when a task matches its description.

How do I install Signal Postmortem in Codex?

Run `npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a codex`. Or copy the skill folder (skills/signal-postmortem in tradermonty/claude-trading-skills) into .agents/skills/signal-postmortem in your project. Codex loads it when a task matches its description.

Can I use Signal Postmortem in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/signal-postmortem, .gemini/skills/signal-postmortem, .github/skills/signal-postmortem and .opencode/skills/signal-postmortem in your project.

What does Signal Postmortem need to run?

Going by SKILL.md and its folder, Signal Postmortem needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named FMP_API_KEY. Our summary lists: Python 3; A credential in FMP_API_KEY; A credential in YOUR_KEY.

Does Signal Postmortem access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Signal Postmortem safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Signal Postmortem use?

Signal Postmortem is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Signal Postmortem use?

About 1.7k tokens (SKILL.md is roughly 6.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.6k tokens, read only when the agent opens those files.

What are the alternatives to Signal Postmortem?

Skills that share tags, products or a category with Signal Postmortem: Author Migration (nrwl/nx, 29k stars), Write Notes Like Deepseek (czm15053/write-notes-like-deepseek, 497 stars), OpenRig Upgrade Procedure (mvschwarz/openrig, 6.2k stars) and GreptimeDB Release Runbook (GreptimeTeam/greptimedb, 6.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Signal Postmortem?

tradermonty (a GitHub user) maintains it in tradermonty/claude-trading-skills, which has 2,973 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 5, 2026.

Source: tradermonty/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Signal Postmortem

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Signal Postmortem

Overview

When to Use

Prerequisites

API Key Setup (Optional)

Workflow

Step 1: Prepare Signal Records

Step 2: Record Outcomes

Step 3: Classify Outcomes

Step 4: Generate Feedback Files

Step 5: Review Summary Statistics

Output Format

Postmortem Record (JSON)

Weight Feedback (JSON)

Skill Improvement Backlog Entry (YAML)

Summary Report (Markdown)

Resources

Key Principles

Files

Used in 1 other repository

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