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 BaggaT236/AI-Trading-Skills --skill weekly-performance-digestgit clone --depth 1 https://github.com/BaggaT236/AI-Trading-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/baggat236/ai-trading-skills/weekly-performance-digest)<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/weekly-performance-digest"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/weekly-performance-digest/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/baggat236/ai-trading-skills/weekly-performance-digest"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/weekly-performance-digest.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 | $0.00063 | $0.01551 |
| Opus 5 | $0.00032 | $0.00776 |
| Sonnet 5 | $0.00013 | $0.00310 |
| Haiku 4.5 | $0.00006 | $0.00155 |
Grade A, and why
weekly-performance-digest 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 9d 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 weekly-performance-digest — 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Weekly Performance Digest
Overview
Weekly Performance Digest aggregates the trades you closed during a week into a single
performance report. It reads CLOSED theses tracked by trader-memory-core
(state/theses/th_*.yaml), computes headline metrics (win rate, expectancy, profit
factor, R-multiple, MAE/MFE), breaks results down across several pattern dimensions
(source skill, exit reason, thesis type, sector, mechanism tag, screening grade), and
surfaces the week's biggest winners, losers, and lessons. Output is a JSON record plus
a human-readable Markdown report. Pure calculation — no API key required.
When to Use
- At the end of a trading week to review aggregate realized performance
- To measure win rate and expectancy across all closed positions
- To see which source skills, exit reasons, sectors, or mechanisms drove wins vs losses
- To feed a month-end review (combine four weekly digests) or a postmortem
- For a quick "what worked / what didn't" snapshot grounded in real closed trades
When Not to Use
- For a single-trade deep review — use
trade-performance-coach - For signal-level true/false-positive classification — use
signal-postmortem - For buy/sell recommendations or position sizing — this skill is descriptive only
Prerequisites
- Python 3.9+ with
PyYAML(already a repo dependency) - A
trader-memory-corestate directory of thesis YAML files (state/theses/) - No API key required
Workflow
Step 1: Run the digest for a week
python3 skills/weekly-performance-digest/scripts/generate_weekly_digest.py \
--state-dir state/theses \
--from-date 2026-06-13 --to-date 2026-06-20 \
--output-dir reports/ -v
Defaults: --state-dir state/theses, --from-date = 7 days before --to-date,
--to-date = today, --output-dir reports/. With no date flags it digests the
trailing 7 days.
Step 2: Read the report
The run writes reports/weekly_digest_<to-date>.json and
reports/weekly_digest_<to-date>.md. Review the Markdown for the executive summary,
metrics table, pattern breakdowns, and top winners/losers; consume the JSON downstream.
What ships with it
4 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.
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.
- 9d ago First seen · 132 lines · 63 tokens per session scan A 11c98674c88a
weekly-performance-digest is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 9d ago), licensed MIT. It adds 63 tokens to every session and 1,551 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 weekly-performance-digest, differing in 0 lines, and is treated as a copy.
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