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 us-undervalued-growth-screenergit 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/us-undervalued-growth-screener)<a href="https://agentmods.dev/skills/tradermonty/claude-trading-skills/us-undervalued-growth-screener"><img src="https://agentmods.dev/badge/skills/tradermonty/claude-trading-skills/us-undervalued-growth-screener/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/us-undervalued-growth-screener"><img src="https://agentmods.dev/badge/skills/tradermonty/claude-trading-skills/us-undervalued-growth-screener.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
- Socket pass
- Snyk pass
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 356 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 14 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00106 | $0.08521 |
| Opus 5.5 | $0.00042 | $0.03408 |
| Sonnet 5.5 | $0.00021 | $0.01704 |
| Haiku 4.5 | $0.00011 | $0.00852 |
Grade A, and why
us-undervalued-growth-screener 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 24d 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.
How it starts
The opening of the file, as written. The whole thing — 638 lines — stays where its author put it; the contents beside it link to each section on GitHub.
US Undervalued Growth Screener — v3.6 (Claude Code Direct-FMP)
Overview
Run an end-to-end US undervalued-growth/GARP screen from a minimal request. Find companies whose EPS or FCF per share can compound enough to support attractive two- to three-year returns without assuming multiple expansion, while controlling for accounting basis, forecast construction, SBC, dilution, leverage, cyclicality, corporate actions, peer context, source freshness, and evidence quality.
Claude Code is the preferred execution environment. In Claude Code, run the local direct-FMP pipeline once. The Python process performs bulk retrieval, persistent caching, FY1 normalization, liquidity calculation, four-lane discovery, and deterministic broad screening while keeping raw FMP payloads on disk and out of the model context. Claude reads only the compact run summary and selected candidate packets, then completes SEC/IR underwriting and the existing strict evaluation sequence.
Treat a request such as “use this skill to screen for undervalued-growth stocks” as complete. Resolve defaults, collect current data, choose a viable acquisition path, checkpoint the work, repair obtainable blockers, and return the finished result in the same task. Never ask the user to supply a ticker list, API-plan details, output path, or a separate “continue” instruction unless the user explicitly narrows the scope.
Deep-dive budget and lane coverage: The bounded direct-FMP path defaults to max_deep_dive_candidates: 3. Three selected names cannot represent all four research lanes. The default lane targets are core GARP 2, high-growth exception 1, quality near miss 1, and cyclical normalization 1 (five slots total). A three-name run prioritizes candidates across lanes; it does not promise one name per lane. For a five-slot lane-first selection, set max_deep_dive_candidates: 5 in a local copy of assets/claude-code-config.example.json and rerun the bounded pipeline with that config. Eligible candidates and diversification preferences still determine actual lane representation. The separate full-snapshot path requires full_snapshot_deep_dive_candidates: 5. See references/claude-code-execution.md for selection and budget-change details.
What ships with it
60 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.
- assets/annual-estimates.example.jsonl 1.5 KB
- assets/broad-screen-audit.example.json 6.9 KB
- assets/broad-screen-results.example.jsonl 10 KB
- assets/broad-screen-shortlist.example.json 1.1 KB
- assets/candidate-input.example.json 115 KB
- assets/claude-code-config.example.json 1.5 KB
- assets/discovery-audit.example.json 2.3 KB
- assets/discovery-pool.example.jsonl 1.5 KB
- assets/enriched-candidate-pool.example.jsonl 8.4 KB
- assets/enrichment-queue.example.json 3 B
- assets/global-sources.example.json 2.3 KB
- assets/listing-only-input.example.jsonl 1.3 KB
- assets/market-context.example.json 829 B
- assets/screening-config.example.json 3.4 KB
- assets/universe-audit-results.example.jsonl 3.3 KB
- assets/universe-band-audit.example.json 134 B
- assets/universe-input.example.jsonl 5.6 KB
- references/autonomous-execution.md 9.2 KB
- references/checkpointing.md 3.0 KB
- references/claude-code-execution.md 7.7 KB
- references/data-contract.md 30 KB
- references/full-universe-snapshot.md 10 KB
- references/methodology-ja.md 20 KB
- references/methodology.md 23 KB
- references/migration-v1-to-v2.md 2.9 KB
- references/migration-v2-to-v3.md 2.5 KB
- references/migration-v3-to-v3.1.md 2.7 KB
- references/migration-v3.1-to-v3.2.md 4.3 KB
- references/migration-v3.2-to-v3.3.md 2.0 KB
- references/migration-v3.3-to-v3.4.md 2.2 KB
- references/migration-v3.4-to-v3.5.md 1.7 KB
- references/migration-v3.5-to-v3.6.md 2.4 KB
- references/migration-v3.6-to-v3.6.1.md 14 KB
- references/original-prompt-mapping.md 8.3 KB
- references/output-template.md 10 KB
- references/research-checklist.md 17 KB
- references/review-regression-matrix.md 7.7 KB
- references/scoring-rubric.md 12 KB
- references/sector-kpis.md 9.8 KB
- requirements.txt 128 B
- scripts/build_discovery_pool.py 22 KB runs code
- scripts/build_provider_prefilter_pool.py 18 KB runs code
- scripts/bundle_run_artifacts.py 4.8 KB runs code
- scripts/coverage_semantics.py 7.7 KB runs code
- scripts/estimate_snapshot.py 32 KB runs code
- scripts/evaluate_candidates.py 167 KB runs code
- scripts/fmp_client.py 31 KB runs code
- scripts/manage_run_state.py 35 KB runs code
- scripts/normalize_estimates.py 29 KB runs code
- scripts/prepublish_audit.py 9.9 KB runs code
- scripts/research_contract.py 81 KB runs code
- scripts/run_pipeline.py 136 KB runs code
- scripts/screen_universe.py 87 KB runs code
- scripts/screening_semantics.py 13 KB runs code
- scripts/skill_version.py 754 B runs code
- scripts/tests/test_artifact_paths.py 4.9 KB runs code
- scripts/tests/test_budget_diagnostics.py 4.8 KB runs code
- scripts/tests/test_capability_cache.py 8.1 KB runs code
- scripts/tests/test_claude_code_pipeline.py 13 KB runs code
- scripts/tests/test_evaluate_candidates.py 130 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.
- 24d ago Changed · +2 lines 14e17b1ba538
- 1mo ago Changed · +5 lines 73eb9805493c
- 1mo ago Changed · +19 lines b7475a86d2c6
- 1mo ago Changed · +29 lines 78551b87d91c
- 1mo ago First seen · 583 lines · 106 tokens per session scan A 7395cf54e76e
us-undervalued-growth-screener is a skill published in the GitHub repository tradermonty/claude-trading-skills (3,373 stars, last pushed yesterday), licensed MIT. It adds 106 tokens to every session and 8,521 once invoked, about $0.0004 per session on Opus 5.5. 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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