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.
npx skills add mphinance/alpha-skills --skill signal-postmortemgit clone --depth 1 https://github.com/mphinance/alpha-skillsWrote 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.
[](https://agentmods.dev/skills/mphinance/alpha-skills/signal-postmortem)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/signal-postmortem"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-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.
<a href="https://agentmods.dev/skills/mphinance/alpha-skills/signal-postmortem"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/signal-postmortem.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.5 | $0.00020 | $0.00677 |
| Sonnet 5.5 | $0.00010 | $0.00339 |
| Haiku 4.5 | $0.00005 | $0.00169 |
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 1mo ago.
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.
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.
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 +
requestsfor 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 identifierticker: Stock symbolsignal_date: Date signal was generatedpredicted_direction: LONG or SHORTsource_skill: Which skill generated the signalentry_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
What ships with it
8 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.
- README.md 6.6 KB
- references/feedback-integration.md 5.1 KB
- references/outcome-classification.md 5.0 KB
- scripts/postmortem_analyzer.py 18 KB runs code
- scripts/postmortem_recorder.py 14 KB runs code
- scripts/tests/conftest.py 230 B runs code
- scripts/tests/test_postmortem_analyzer.py 11 KB runs code
- scripts/tests/test_postmortem_recorder.py 7.5 KB runs code
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.
- 1mo ago First seen · 208 lines · 50 tokens per session scan A 719ed5ee385d
signal-postmortem is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 50 tokens to every session and 1,693 once invoked, about $0.0002 per session on Opus 5.5. 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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