Us Undervalued Growth Screener

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.

Non-Negotiable Runtime Preflight

Before reading or reusing any prior run artifact, verify the installed runtime:

python3 skills/us-undervalued-growth-screener/scripts/run_pipeline.py --version
python3 skills/us-undervalued-growth-screener/scripts/screen_universe.py --version
python3 skills/us-undervalued-growth-screener/scripts/build_discovery_pool.py --version
python3 skills/us-undervalued-growth-screener/scripts/build_provider_prefilter_pool.py --version
python3 skills/us-undervalued-growth-screener/scripts/normalize_estimates.py --version
python3 skills/us-undervalued-growth-screener/scripts/manage_run_state.py --version
python3 skills/us-undervalued-growth-screener/scripts/evaluate_candidates.py --version
python3 skills/us-undervalued-growth-screener/scripts/prepublish_audit.py --version
python3 skills/us-undervalued-growth-screener/scripts/bundle_run_artifacts.py --version

Every command must report the same metadata:

skill_version = 3.6.1
schema_version = 3
contract_revision = 3.5
runtime_fingerprint = ug-v3.6.1-claude-code-direct-fmp-20260830

Discard and regenerate any audit, checkpoint, or snapshot whose runtime metadata differs. Do not mix scripts, assets, or run artifacts from v3.1 through v3.5. A stale or cached same-name skill is a hard execution failure, not a warning.

Autonomous Completion Contract

For a minimal request, perform all of the following without handing control back to the user:

1. Fix analysis_as_of and the latest completed US regular-session close.
2. Collect current market context with field-level source support and freshness checks.
3. In Claude Code, invoke run_pipeline.py instead of issuing bulk FMP MCP calls. Keep provider payloads on disk and expose only compact summaries to the model.
4. Audit the requested NYSE/Nasdaq/NYSE American listing universe through adaptive, exhausted market-cap bands when provider responses saturate.
5. Build a reproducible economic candidate pool. Do not require complete financial statements across the whole market.
6. Distinguish enrichment attempted from enrichment resolved.
7. Apply the deterministic broad-screen script. Do not replace its statuses with ad hoc LLM cutoffs.
8. Select up to three economically plausible deep-dive candidates in Claude Code through deterministic multi-lane sampling: core GARP, high-growth exceptions, quality near misses, and cyclicals requiring normalization. Apply a two-name sector cap when alternatives exist. Growth thresholds remain guidelines, not isolated hard gates.
9. Resolve every row in the chosen candidate pool or record sourced exhaustion evidence.
10. Perform corporate-action preflight and primary-source underwriting for every selected symbol.
11. Save every selected symbol as a verified candidate record, including review_required, screened_out, and excluded outcomes.
12. Assemble the schema-v3 / contract-v3.5 snapshot.
13. Run evaluate_candidates.py --strict --require-final.
14. Apply the final quality eligibility gate; route weak-cash-flow, low-ROIC, overleveraged, heavily dilutive, fragile-low-case, or severe-LOE names to conditional or review_required.
15. Run prepublish_audit.py; repair every obtainable blocker and rerun.
16. Run bundle_run_artifacts.py to produce a self-contained audit ZIP containing every referenced artifact.
17. Present a formal ranking and downloadable bundle only when all gates pass.

An exit code of 2 means continue the same execution: enrich the queue, verify the pool-generation audit, complete selected deep dives, or repair contract failures. It never means “ask the user to say continue.” After attaching the audit, run manage_run_state.py next-action; execute the returned action and every returned symbol. Never ask whether to process two versus five selected names. To change the budget, rerun the broad screen first so omitted names become deferred_by_budget.

Completion Semantics

Final ranking from an audited bounded pool

A reproducibly generated bounded pool may support a scoped final ranking when:

candidate-pool generation audit is valid
candidate_pool_exhausted = true
all_rows_resolved = true
unresolved_count = 0
queue_count = 0
all selected symbols have verified candidate records
strict final evaluation passes

Label the conclusion scope, such as stratified_discovery_pool or provider_prefilter. Do not imply that unexamined market listings were economically screened.

Market-wide “no qualifying candidates”

A market-wide no-candidates conclusion requires all of the above plus full in-scope economic coverage:

conclusion_scope = full_listing_universe
candidate_pool covers every in-scope listing
in_scope_missing_count = 0
at least one row was economically assessable
selected_symbols = []

Bounded-pool “no qualifying candidates”

A bounded pool may conclude only:

no qualifying candidates in the audited bounded pool

It must not claim that the entire US small/mid-cap market has no qualifying company.

Read references/autonomous-execution.md before a live minimal-request run.

When to Use

Use this skill to:

• Discover and rank US-listed undervalued-growth or GARP stocks.
• Screen operating-company common stocks on NYSE, Nasdaq, and NYSE American.
• Test whether EPS or FCF-per-share growth alone supports roughly 30%–50% upside over two to three years.
• Refresh a prior screen after earnings, guidance, filings, corporate actions, or estimate revisions.
• Compare candidates on forward valuation, growth durability, ROIC, standard FCF, SBC, dilution, peers, cycle risk, and sector-specific KPIs.

Do not use it for:

• A generic single-ticker report after a stock is already selected; use us-stock-analysis.
• Pure dividend, momentum, technical-pattern, pre-revenue biotechnology, or merger-arbitrage screening.
• Automatic order placement.

Prerequisites

• Python 3.9 or later.
• requests for the generated direct-FMP client; deterministic evaluation and audit scripts otherwise use the standard library.
• FMP_API_KEY in the environment for Claude Code direct mode. Never commit or print the key.
• Current SEC, company-IR, and macro sources accessible for selected-company underwriting.
• Writable reports/ and .cache/ directories.
• No specific paid FMP plan is assumed. Bulk endpoint failures fall back to bounded per-symbol enrichment and are disclosed in diagnostics.

Default Scope

Unless the user specifies otherwise:

• Exchanges: NYSE, Nasdaq, NYSE American.