/us-undervalued-growth-screener

name: us-undervalued-growth-screener
description: Autonomously screen NYSE, Nasdaq, and NYSE American operating-company stocks for undervalued-growth/GARP opportunities using forward same-basis valuation, driver-derived EPS/FCF forecasts, primary-source financial verification, SBC and dilution controls, sector and cycle normalization, auditable candidate-pool coverage, and fail-closed final reporting. Use when asked to find, screen, rank, or refresh US undervalued-growth stocks, including minimal requests with no ticker list or parameters.

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.

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 `pr