Thesis Creator
Stars-OC/thesis-creator
Walks Chinese undergraduates through writing a graduation thesis from topic to Word export, with text-similarity reduction and AI-text rate rewriting and checks.
Generate a weekly performance summary from closed trader-memory-core theses — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason…
Install Weekly Performance Digest
$ npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install tradermonty/claude-trading-skills weekly-performance-digest --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/weekly-performance-digest .claude/skills/weekly-performance-digest && rm -rf skills-srcUse ~/.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/
Install the "weekly-performance-digest" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/weekly-performance-digest into .claude/skills/weekly-performance-digest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weekly-performance-digest", 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 install https://github.com/tradermonty/claude-trading-skills/tree/main/skills/weekly-performance-digestType 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.
$ npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install tradermonty/claude-trading-skills weekly-performance-digest --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/weekly-performance-digest .agents/skills/weekly-performance-digest && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "weekly-performance-digest" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/weekly-performance-digest into .agents/skills/weekly-performance-digest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weekly-performance-digest", 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.
$ npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install tradermonty/claude-trading-skills weekly-performance-digest --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/weekly-performance-digest .cursor/skills/weekly-performance-digest && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "weekly-performance-digest" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/weekly-performance-digest into .cursor/skills/weekly-performance-digest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weekly-performance-digest", 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 https://github.com/tradermonty/claude-trading-skills.git --path skills/weekly-performance-digest--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install tradermonty/claude-trading-skills weekly-performance-digest --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/weekly-performance-digest .gemini/skills/weekly-performance-digest && rm -rf skills-srcUse ~/.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/
Install the "weekly-performance-digest" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/weekly-performance-digest into .gemini/skills/weekly-performance-digest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weekly-performance-digest", 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.
$ gh skill install tradermonty/claude-trading-skills weekly-performance-digestInstalls 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).
$ npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/weekly-performance-digest .github/skills/weekly-performance-digest && rm -rf skills-srcUse ~/.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/
Install the "weekly-performance-digest" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/weekly-performance-digest into .github/skills/weekly-performance-digest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weekly-performance-digest", 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.
$ npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install tradermonty/claude-trading-skills weekly-performance-digest --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/weekly-performance-digest .opencode/skills/weekly-performance-digest && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "weekly-performance-digest" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/weekly-performance-digest into .opencode/skills/weekly-performance-digest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "weekly-performance-digest", 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
weekly-performance-digest- GitHub stars
- 3k
- Used in
- 1 other repo
- Token cost
- ~1.4k tokens
- SKILL.md length
- 519 words
- Files
- 6 (incl. scripts, references)
- Skills in repo
- 74
- Repo updated
- First seen
- Licence
- MIT
At a glance
Generate a weekly performance summary from closed trader-memory-core theses — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason…
- Works in 3 steps: Run the digest for a week → Read the report → (optional): Feed downstream
- Tasks that involve Essays and academic help
- SKILL.md covers Overview, When to Use, When Not to Use and Prerequisites, plus 5 more sections
- Runs Python scripts from its folder; calls python3
What it does
Weekly Performance Digest is an agent skill from tradermonty/claude-trading-skills. Generate a weekly performance summary from closed trader-memory-core theses — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason, thesis type, sector, and mechanism. No API required; pure local calculation.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/weekly-digest-metrics.md`, `scripts/generate_weekly_digest.py` and `scripts/tests/conftest.py`).
It sits in Education, covering Essays and academic help and Newsletters. 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 Essays and academic help
- Tasks that involve Newsletters
Example prompts
- “/weekly-performance-digest”
Requirements
- Python 3
Workflow steps
3 steps, taken from the step headings in SKILL.md.
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 3 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 no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Weekly Performance Digest loads about 1.4k tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 519 words of instructions outside code blocks.
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). 519 words, ~1,424 tokens.
Download SKILL.mdSave it as
.claude/skills/weekly-performance-digest/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- weekly-performance-digest
- description
- Generate a weekly performance summary from closed trader-memory-core theses — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason, thesis type, sector, and mechanism. No API required; pure local calculation.
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/ -vDefaults: --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.
Step 3 (optional): Feed downstream
Combine several weekly JSON digests for a monthly review, or pass the JSON to a postmortem/coach step. The skill is descriptive — act on its findings via your normal review process.
Show full SKILL.md (229 more words)Show less
How It Works
- Trade selection. A trade counts in a week if its
exit.actual_datefalls in[from-date, to-date]andstatus == CLOSED. - Win/loss.
outcome.pnl_dollars > 0is a winner,< 0a loser,== 0breakeven;win_rate = winners / total_trades. - R-multiple.
pnl_dollars / ((entry.actual_price − exit.stop_loss) × position.shares). (Stop-loss is read fromexit.stop_loss, per the real thesis schema.) - Double-counting safeguard. A CLOSED thesis's
outcome.pnl_dollarsis the cumulative realized P&L across all trims plus the final leg. Headline metrics use that cumulative value over CLOSED theses only. The separatepartial_trimsblock scansstatus_history[]of PARTIALLY_CLOSED theses only (still open) and is reported for information — it is never added into the headline totals/win-rate. A position trimmed in week 1 then closed in week 2 therefore shows as a partial trim in week 1 and inside week 2's CLOSED headline; that is intended, not a duplicate.
Output Format
JSON (weekly_digest_<to-date>.json)
{
"schema_version": "1.0",
"report_type": "weekly_performance_digest",
"period": {"from": "2026-06-13", "to": "2026-06-20"},
"generated_at": "2026-06-20T21:39:07Z",
"summary": {
"total_trades": 2, "winners": 1, "losers": 1, "breakeven": 0,
"win_rate": 0.5, "expectancy": 25.0, "profit_factor": 2.0,
"total_realized_pnl": 50.0, "total_realized_pnl_pct": 4.17
},
"metrics": {
"avg_winner": 100.0, "avg_loser": -50.0,
"largest_winner": 100.0, "largest_loser": -50.0,
"avg_holding_days_winners": 9.0, "avg_holding_days_losers": 6.0,
"r_multiple_avg": 0.25, "r_multiple_stdev": 1.06,
"avg_mae_pct": -3.75, "avg_mfe_pct": 4.5
},
"pattern_analysis": {
"by_source_skill": {"...": {"wins": 1, "losses": 0, "total": 1, "win_rate": 1.0}},
"by_exit_reason": {}, "by_thesis_type": {}, "by_sector": {},
"by_mechanism_tag": {}, "by_screening_grade": {}
},
"partial_trims": {"count": 0, "total_realized_pnl": 0.0, "trims": []},
"lessons": {"top_wins": [], "top_losses": [], "process_improvements": []}
}Markdown (weekly_digest_<to-date>.md)
Sections: # Weekly Performance Digest, ## Executive Summary,
## Performance Metrics, ## Pattern Analysis, ## Lessons Learned
(### Top Winners / ### Top Losers / ### Process Improvements).
An empty week still produces a valid report with zeroed metrics (exit code 0).
Resources
scripts/generate_weekly_digest.py— digest generator (JSON + Markdown)references/weekly-digest-metrics.md— metric formulas and interpretation
Key Principles
- Closed trades only for headline numbers — cumulative
outcome.*, keyed on exit date. - No double-counting — partial trims are informational and excluded from totals.
- Pattern attribution — every win/loss is attributed across multiple dimensions.
- Descriptive, not prescriptive — the digest reports; you decide.
© tradermonty, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 5 other files (scripts, references) in skills/weekly-performance-digest of tradermonty/claude-trading-skills.
- SKILL.md
- references/weekly-digest-metrics.md
- requirements.txt
- scripts/generate_weekly_digest.py
- scripts/tests/conftest.py
- scripts/tests/test_generate_weekly_digest.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.
Compare with similar skills
Weekly Performance Digest 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Weekly Performance Digest this skilltradermonty/claude-trading-skills | 3k | 1 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Thesis CreatorStars-OC/thesis-creator | 230 | — | ~2.8k | Automated safety check: Pass | MIT | |
| Aigc Detectorfree-revalution/AIGC-Detector-Pro | 141 | — | ~2.8k | Automated safety check: Pass | MIT | |
| Articleaeonfun/aeon | 767 | — | ~8.2k | Automated safety check: Pass | MIT | |
| Ssc Evidencefranklee16/academic-research-skills | 223 | 1 repos | ~317 | Automated safety check: Pass | None | |
| Ylj Preemption Checkbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT |
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Categories
Questions about Weekly Performance Digest
What does Weekly Performance Digest do?
Generate a weekly performance summary from closed trader-memory-core theses — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason…. Weekly Performance Digest is an agent skill from tradermonty/claude-trading-skills. Generate a weekly performance summary from closed trader-memory-core theses — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason, thesis type, sector, and mechanism.
When should I use Weekly Performance Digest?
Weekly Performance Digest fits situations like: tasks that involve Essays and academic help; tasks that involve Newsletters.
How do I install Weekly Performance Digest in Claude Code?
Run `npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -a claude-code`. Or copy the skill folder (skills/weekly-performance-digest in tradermonty/claude-trading-skills) into .claude/skills/weekly-performance-digest in your project. Claude Code loads it when a task matches its description.
How do I install Weekly Performance Digest in Codex?
Run `npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -a codex`. Or copy the skill folder (skills/weekly-performance-digest in tradermonty/claude-trading-skills) into .agents/skills/weekly-performance-digest in your project. Codex loads it when a task matches its description.
Can I use Weekly Performance Digest 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 weekly-performance-digest -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/weekly-performance-digest, .gemini/skills/weekly-performance-digest, .github/skills/weekly-performance-digest and .opencode/skills/weekly-performance-digest in your project.
What does Weekly Performance Digest need to run?
Going by SKILL.md and its folder, Weekly Performance Digest needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Weekly Performance Digest 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 Weekly Performance Digest 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 Weekly Performance Digest use?
Weekly Performance Digest 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 Weekly Performance Digest use?
About 1.4k tokens (SKILL.md is roughly 5.7k 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 949 tokens, read only when the agent opens those files.
What are the alternatives to Weekly Performance Digest?
Skills that share tags, products or a category with Weekly Performance Digest: Thesis Creator (Stars-OC/thesis-creator, 230 stars), Aigc Detector (free-revalution/AIGC-Detector-Pro, 141 stars), Article (aeonfun/aeon, 767 stars) and Ssc Evidence (franklee16/academic-research-skills, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Weekly Performance Digest?
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.