Creating Financial Models
Chen-zexi/open-ptc-agent
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position).
Install Earnings Trade Analyzer
$ npx skills add tradermonty/claude-trading-skills --skill earnings-trade-analyzer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install tradermonty/claude-trading-skills earnings-trade-analyzer --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/earnings-trade-analyzer .claude/skills/earnings-trade-analyzer && 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 "earnings-trade-analyzer" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/earnings-trade-analyzer into .claude/skills/earnings-trade-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-trade-analyzer", 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/earnings-trade-analyzerType 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 earnings-trade-analyzer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install tradermonty/claude-trading-skills earnings-trade-analyzer --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/earnings-trade-analyzer .agents/skills/earnings-trade-analyzer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "earnings-trade-analyzer" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/earnings-trade-analyzer into .agents/skills/earnings-trade-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-trade-analyzer", 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 earnings-trade-analyzer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install tradermonty/claude-trading-skills earnings-trade-analyzer --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/earnings-trade-analyzer .cursor/skills/earnings-trade-analyzer && 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 "earnings-trade-analyzer" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/earnings-trade-analyzer into .cursor/skills/earnings-trade-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-trade-analyzer", 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/earnings-trade-analyzer--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 earnings-trade-analyzer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install tradermonty/claude-trading-skills earnings-trade-analyzer --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/earnings-trade-analyzer .gemini/skills/earnings-trade-analyzer && 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 "earnings-trade-analyzer" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/earnings-trade-analyzer into .gemini/skills/earnings-trade-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-trade-analyzer", 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 earnings-trade-analyzerInstalls 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 earnings-trade-analyzer -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/earnings-trade-analyzer .github/skills/earnings-trade-analyzer && 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 "earnings-trade-analyzer" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/earnings-trade-analyzer into .github/skills/earnings-trade-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-trade-analyzer", 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 earnings-trade-analyzer -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 earnings-trade-analyzer --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/earnings-trade-analyzer .opencode/skills/earnings-trade-analyzer && 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 "earnings-trade-analyzer" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/earnings-trade-analyzer into .opencode/skills/earnings-trade-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-trade-analyzer", 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
earnings-trade-analyzer- GitHub stars
- 3k
- Used in
- 1 other repo
- Token cost
- ~1.8k tokens
- SKILL.md length
- 774 words
- Files
- 21 (incl. scripts, references)
- Skills in repo
- 74
- Repo updated
- First seen
- Licence
- MIT
At a glance
Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position).
- Works in 4 steps: Run the Earnings Trade Analyzer → Review Results → Present Analysis → …
- User asks about earnings trade analysis
- SKILL.md covers When to Use, Prerequisites, Workflow and Output, plus 1 more section
- Runs Python scripts from its folder; calls python3 and curl; reaches financialmodelingprep.com; needs FMP_API_KEY
What it does
Earnings Trade Analyzer is an agent skill from tradermonty/claude-trading-skills. Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position). Scores each stock 0-100 and assigns A/B/C/D grades. Use when user asks about earnings trade analysis, post-earnings momentum screening, earnings gap scoring, or finding best recent earnings reactions.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 24 other files, including scripts and reference files (for example `references/scoring_methodology.md`, `scripts/_fmp_compat.py` and `scripts/_market_calendar.py`).
It sits in Business, Finance & HR. 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
- User asks about earnings trade analysis
- Post-earnings momentum screening
- Earnings gap scoring
- Finding best recent earnings reactions
Example prompts
- “/earnings-trade-analyzer”
Requirements
- Python 3
- A credential in FMP_API_KEY
Workflow steps
4 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 c8d58f0. 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 14 files in scripts/ (Python, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
python3curl
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
financialmodelingprep.com
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
Earnings Trade Analyzer loads about 1.8k tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 774 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 c8d58f0, republished under its MIT licence (© tradermonty). 774 words, ~1,846 tokens.
Download SKILL.mdSave it as
.claude/skills/earnings-trade-analyzer/SKILL.md (or your agent's skills folder). This skill also uses 20 other files; get the full folder from GitHub.- name
- earnings-trade-analyzer
- description
- Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position). Scores each stock 0-100 and assigns A/B/C/D grades. Use when user asks about earnings trade analysis, post-earnings momentum screening, earnings gap scoring, or finding best recent earnings reactions.
Earnings Trade Analyzer - Post-Earnings 5-Factor Scoring
Analyze recent post-earnings stocks using a 5-factor weighted scoring system to identify the strongest earnings reactions for potential momentum trades.
When to Use
- User asks for post-earnings trade analysis or earnings gap screening
- User wants to find the best recent earnings reactions
- User requests earnings momentum scoring or grading
- User asks about post-earnings accumulation day (PEAD) candidates
Prerequisites
- FMP API key (set
FMP_API_KEYenvironment variable or pass--api-key) - Free tier (250 calls/day) is sufficient for default screening (lookback 2 days, top 20)
- Paid tier recommended for larger lookback windows or full screening
Workflow
Step 1: Run the Earnings Trade Analyzer
Execute the analyzer script:
# Default: last 2 days of earnings, top 20 results
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py --output-dir reports/
# Custom lookback and market cap filter
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
--lookback-days 5 \
--min-market-cap 1000000000 \
--top 30 \
--output-dir reports/
# Deterministic anchor date (America/New_York); the window is anchored on the
# ET calendar date, not the runner's local clock. For reproducible runs/tests.
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
--as-of 2026-09-15 \
--output-dir reports/
# With entry quality filter
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
--apply-entry-filter \
--output-dir reports/Degraded endpoint / budget fallback for scheduled reviews
If the analyzer reports a 404, an implausible empty earnings calendar, or exhausts its API-call budget before producing scored candidates during a scheduled after-close/pre-market run, do not report "no earnings reactions" immediately. A clean empty response over a date window containing at least one XNYS session exits 1 with ZERO_RESULT_REASON=earnings_calendar_empty_with_market_sessions. If the shared XNYS calendar cannot classify the window, it exits 1 with ZERO_RESULT_REASON=market_calendar_unavailable. Budget or daily rate-limit exhaustion during profile fetching exits 1 with ZERO_RESULT_REASON=profiles_budget_exhausted. Treat each as a failed run to retry or fall back on, not a quiet day. Only a clean empty response over a zero-session window exits 0 as ZERO_RESULT_REASON=no_earnings_rows.
- First retry once with a narrower liquid-universe configuration so the full 5-factor scorer has a chance to complete, for example:
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
--lookback-days 2 \
--min-market-cap 5000000000 \
--top 20 \
--max-api-calls 600 \
--output-dir reports/<routine-date>- If the scored run still returns no candidates or cannot complete, verify the same range through the stable endpoint used by the compatibility shim and clearly label the result as an ungraded fallback:
curl "https://financialmodelingprep.com/stable/earnings-calendar?from=YYYY-MM-DD&to=YYYY-MM-DD&apikey=$FMP_API_KEY"Then optionally enrich returned US tickers through the analyzer's stable-first FMP client or per-symbol /stable/quote?symbol=<ticker> calls to rank by same-day changesPercentage, market cap, and liquidity. Use legacy /api/v3 quote calls only as a legacy-key fallback after stable has failed. Present these as preliminary / ungraded reactions because the 5-factor scorer did not run; do not assign A/B/C/D grades from the fallback alone.
No-candidate output pitfall: The analyzer may print Candidates after filtering: 0 / No candidates found matching criteria. and exit successfully without writing an earnings_trade_analyzer_*.json file. In that case, do not try to run PEAD Mode B from a nonexistent candidate file. Say explicitly that no scored analyzer JSON was produced, run the endpoint/quote enrichment fallback above if the routine needs an earnings section, and label any names as manual-review only. This success-exit path does not cover budget exhaustion during profile fetching: that case exits 1 (ZERO_RESULT_REASON=profiles_budget_exhausted) instead.
Show full SKILL.md (350 more words)Show less
Empty windows and today-only runs
The earnings calendar window is inclusive and uses the America/New_York
calendar date from --as-of (or the current ET date). A clean provider []
is a benign quiet-window result only when the shared XNYS calendar successfully
counts zero exchange sessions in that exact window, such as a weekend or
holiday. If the window contains an XNYS session, the same clean [] exits 1
with ZERO_RESULT_REASON=earnings_calendar_empty_with_market_sessions so a
provider drop is not reported as a quiet day. If the XNYS calendar cannot be
queried, the run also exits 1 with
ZERO_RESULT_REASON=market_calendar_unavailable.
--lookback-days 0 is valid and queries exactly the single ET as-of date. Use
it after the relevant announcements have been published (normally after the
session close); an empty response on an XNYS session remains intentionally
fail-closed. A non-empty response whose rows do not carry a symbol retains
the separate ZERO_RESULT_REASON=no_earnings_rows behavior; that case is not
the literal-empty-list session check above.
Step 2: Review Results
- Read the generated JSON and Markdown reports
- Load
references/scoring_methodology.mdfor scoring interpretation context - Focus on Grade A and B stocks for actionable setups
Step 3: Present Analysis
For each top candidate, present:
- Composite score and letter grade (A/B/C/D)
- Earnings gap size and direction
- Pre-earnings 20-day trend
- Volume ratio (20-day vs 60-day average)
- Position relative to 200-day and 50-day moving averages
- Weakest and strongest scoring components
Step 4: Provide Actionable Guidance
Based on grades:
- Grade A (85+): Strong earnings reaction with institutional accumulation - consider entry
- Grade B (70-84): Good earnings reaction worth monitoring - wait for pullback or confirmation
- Grade C (55-69): Mixed signals - use caution, additional analysis needed
- Grade D (<55): Weak setup - avoid or wait for better conditions
Output
earnings_trade_analyzer_YYYY-MM-DD_HHMMSS.json- Structured results with schema_version "1.0"earnings_trade_analyzer_YYYY-MM-DD_HHMMSS.md- Human-readable report with tables
Unknown earnings timing
FMP does not confirm a bmo/amc session for every earnings row; unconfirmed
rows report earnings_timing: "unknown" and the gap calculation assumes the
AMC window as a fallback. Both reports surface timing_unknown_count out of
timing_candidates_total so this assumption stays visible rather than
blending unnoticed into the scores.
Resources
references/scoring_methodology.md- 5-factor scoring system, grade thresholds, and entry quality filter rules
© 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 20 other files (scripts, references) in skills/earnings-trade-analyzer of tradermonty/claude-trading-skills.
- SKILL.md
- references/scoring_methodology.md
- requirements.txt
- scripts/_fmp_compat.py
- scripts/_market_calendar.py
- scripts/analyze_earnings_trades.py
- scripts/calculators/__init__.py
- scripts/calculators/gap_size_calculator.py
- scripts/calculators/ma200_calculator.py
- scripts/calculators/ma50_calculator.py
- scripts/calculators/pre_earnings_trend_calculator.py
- scripts/calculators/volume_trend_calculator.py
- scripts/fmp_client.py
- scripts/report_generator.py
- scripts/scorer.py
- scripts/tests/conftest.py
- scripts/tests/test_cli_reports.py
- … and 4 more
Open the folder on GitHubat commit c8d58f0
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
Earnings Trade Analyzer 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 |
|---|---|---|---|---|---|---|
| Earnings Trade Analyzer this skilltradermonty/claude-trading-skills | 3k | 1 repos | ~1.8k | Automated safety check: Pass | MIT | |
| Creating Financial ModelsChen-zexi/open-ptc-agent | 729 | 3 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Stock APIzhangxiangliang/stock-api | 2k | — | ~507 | Automated safety check: Pass | MIT | |
| Itr Walakaranb192/itr-wala | 871 | — | ~3.6k | Automated safety check: Pass | MIT | |
| Tushare Datazillionare/zillionare | 322 | 2 repos | ~2.3k | Automated safety check: Pass | None | |
| Cc Sdd New Agentgotalab/cc-sdd | 3.7k | — | ~1.1k | Automated safety check: Pass | MIT |
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Questions about Earnings Trade Analyzer
What does Earnings Trade Analyzer do?
Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position). Earnings Trade Analyzer is an agent skill from tradermonty/claude-trading-skills. Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position).
When should I use Earnings Trade Analyzer?
Earnings Trade Analyzer fits situations like: user asks about earnings trade analysis; post-earnings momentum screening; earnings gap scoring; finding best recent earnings reactions.
How do I install Earnings Trade Analyzer in Claude Code?
Run `npx skills add tradermonty/claude-trading-skills --skill earnings-trade-analyzer -a claude-code`. Or copy the skill folder (skills/earnings-trade-analyzer in tradermonty/claude-trading-skills) into .claude/skills/earnings-trade-analyzer in your project. Claude Code loads it when a task matches its description.
How do I install Earnings Trade Analyzer in Codex?
Run `npx skills add tradermonty/claude-trading-skills --skill earnings-trade-analyzer -a codex`. Or copy the skill folder (skills/earnings-trade-analyzer in tradermonty/claude-trading-skills) into .agents/skills/earnings-trade-analyzer in your project. Codex loads it when a task matches its description.
Can I use Earnings Trade Analyzer 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 earnings-trade-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/earnings-trade-analyzer, .gemini/skills/earnings-trade-analyzer, .github/skills/earnings-trade-analyzer and .opencode/skills/earnings-trade-analyzer in your project.
What does Earnings Trade Analyzer need to run?
Going by SKILL.md and its folder, Earnings Trade Analyzer needs Python for the scripts in its folder, the command-line tools its instructions call (python3 and curl) and credentials named FMP_API_KEY. Our summary lists: Python 3; A credential in FMP_API_KEY.
Does Earnings Trade Analyzer access the network?
SKILL.md names 1 domain. In commands or code: financialmodelingprep.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Earnings Trade Analyzer 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 Earnings Trade Analyzer use?
Earnings Trade Analyzer 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 Earnings Trade Analyzer use?
About 1.8k tokens (SKILL.md is roughly 7.4k 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 964 tokens, read only when the agent opens those files.
What are the alternatives to Earnings Trade Analyzer?
Skills that share tags, products or a category with Earnings Trade Analyzer: Creating Financial Models (Chen-zexi/open-ptc-agent, 729 stars), Stock API (zhangxiangliang/stock-api, 2k stars), Itr Wala (karanb192/itr-wala, 871 stars) and Tushare Data (zillionare/zillionare, 322 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Earnings Trade Analyzer?
tradermonty (a GitHub user) maintains it in tradermonty/claude-trading-skills, which has 2,982 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 11, 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.