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 can

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: Every command must report the same metadata: 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: 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: Bounded pool “no qualifying candidates” A bounded pool may conclude only: 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. Security type: active operating company common stock. Market cap focus: USD 500 million–20 billion. Minimum price: USD 5. Preferred average daily dollar volume: USD 5 million; hard floor USD 1 million. Claude Code provider prefilter pool: target 30 symbols after code side economics and verified liquidity; bounded per symbol fallback may inspect up to 80 names without loading their provider payloads into model context. Deep dive budget: three symbols by default in Claude Code, allocated across four deterministic research lanes. Once selected, every symbol must be resolved; lower the budget only by rerunning the broad screen so omitted names become deferred by budget . Maximum ranked output: ten, though the verified deep dive set may be smaller. Minimum formal constant multiple upside: 30% over a supported two or three year horizon. Multiple contraction stress: current forward multiple reduced by 20%. Minimum analysts for a rankable consensus horizon: three, unless a fully sourced independent forecast is constructed. Immutable request scope and bounded execution For a minimal request, the user requested market cap scope is always USD 500M–20B. Never rewrite the run config so a convenient 3–4B band becomes the requested scope. Record user requested scope and executed scope separately. A narrower executed scope is incomplete unless the user explicitly requested it; context or tool budget pressure is not authorization. Stream listing pages/bands to JSONL rather than loading every row into the conversation context. Liquidity evidence Never calculate ADDV from one session's volume. Candidate generation requires a provider average dollar volume measure or price × average volume with an explicit averaging window of at least 20 trading days and source IDs. Rows lacking valid average liquidity evidence remain needs enrichment and cannot enter the discovery pool. Current forward horizon A current P/E must be explicitly NTM or FY1 and must reconcile to a positive current forward EPS, price, fiscal year/period metadata, estimate date, analyst count, and source IDs. Generic outer year P/E values are invalid. A missing FY1/NTM row, a range crossing zero, or extreme forecast dispersion sends the name to enrichment or unavailable after enrichment ; it must never appear as a low P/E selection. Result Quality Controls Deterministic multi lane candidate discovery Do not let one global score fill the research budget with a single style or sector. Allocate the default five deep dives across these lanes when qualifying names exist: two core GARP names, one high growth exception with Forward P/E up to 30x, one quality near miss whose low valuation may compensate for sub guideline headline growth, one cyclical candidate that requires an explicit mid cycle model. Backfill unused slots by deterministic priority and limit selections to two per sector when alternatives exist. Report each selected name's selection lane . The LLM may explain these decisions but may not replace them with ad hoc cutoffs. Formal eligibility quality gate A name that passes the upside test is not automatically eligible . Formal ranking also requires: final score at least 70, SBC adjusted FCF yield at least 3% or EV/FCF at most 30x, ROIC at least 8%, Net Debt/EBITDA no more than 3.0x when applicable, diluted share CAGR no more than 5%, at least 15% upside under a supported low consensus case, no severe LOE tail loss worse than the configured threshold. One or two ordinary failures may produce conditional ; severe FCF or LOE failures and broader weakness produce review required . Never label a low P/E/high EV FCF transition story as a formal winner merely because average consensus EPS implies 30% upside. Independent forecast driver evidence Every ranked year 2/year 3 bridge must use construction method=independent driver model . Each revenue, margin, interest, tax, share count, FCF, and adjustment driver needs an origin and source IDs. Mark any driver solved backwards from target EPS with target solved=true ; this fails the bridge. A residual adjustment that merely forces the model to consensus is not independent validation. Publish only self contained results The final report is not publishable until prepublish audit.py verifies all referenced audit files, counts, hashes, scenario arithmetic, candidate statuses, final three labels, and absence of unfinished run language. Package the entire run directory with bundle run artifacts.py ; a ZIP containing only Markdown/JSON summaries is incomplete. Data Acquisition Strategy Preferred Claude Code path — direct FMP, compact model context Run one local command: The generated FMP client reuses the repository's central scripts/fmp client/ source of truth pattern. It writes raw responses and a persistent SQLite cache to disk. Do not paste or read the raw provider trees into the language model context. Read only run summary.json , NEXT ACTION.json , audit/broad screen audit.json , and the compact packets under candidate packets/ . For a market wide run on a plan without bulk estimates, collect every frozen universe shard first, then screen the verified snapshot: Run each shard once without resume ; use resume only to continue an existing partial shard. Both collection and screening are current only: collection fixes the estimate normalization basis, while

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