Claude Trading Skills is a collection of Claude Code workflows for individual investors who want structured market analysis, charting, economic-calendar review, screening, trade planning, journaling, and risk management. It is designed for people using long-term investing, ETFs, dividend stocks, and disciplined swing trading, and the catalogue entries package these workflows as skills, agents, commands, settings, and instructions.
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 tradermonty/claude-trading-skills --skill weekly-performance-digestgit clone --depth 1 https://github.com/tradermonty/claude-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/tradermonty/claude-trading-skills/weekly-performance-digest)<a href="https://agentmods.dev/skills/tradermonty/claude-trading-skills/weekly-performance-digest"><img src="https://agentmods.dev/badge/skills/tradermonty/claude-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/tradermonty/claude-trading-skills/weekly-performance-digest"><img src="https://agentmods.dev/badge/skills/tradermonty/claude-trading-skills/weekly-performance-digest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- weekly-performance-digest — 100% identical, 0 lines differ
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
5 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 tradermonty/claude-trading-skills (2,813 stars, last pushed today), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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