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financials-normalizer

Prepare public-company financial data for analysis models by standardizing periods, units, currencies, and account labels. Produce structured tables with source references, validation results, and clear flags for missing data, conflicts, or disclosure changes.

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원문원문 설명 보기

Use when normalizing public-company financials from source materials. Do not use for private data rooms or non-financial cleanup.

설치 방법 보기

이럴 때 유용합니다

  • Prepare financial statements for an analysis model
  • Compare figures across changes in segment disclosures
  • Separate management guidance from consensus estimates
  • Review missing sources and conflicting financial figures

포함된 파일(12)

  • SKILL.md10.3 KB
  • agents/openai.yaml301 B
  • assets/icon.svg456 B
  • references/integration-guide.md2.7 KB
  • references/line_item_aliases.csv1.6 KB
  • references/line-item-taxonomy.md7.4 KB
  • references/normalization-schema.md9.0 KB
  • references/qa-rules.md6.3 KB
  • references/source-protocol.md6.7 KB
  • scripts/build_source_index.py2.4 KB
  • scripts/normalize_extracted_financials.py22.5 KB
  • scripts/validate_normalized_financials.py4.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Financials Normalizer

Skill Configuration

Common Skill Instructions

MANDATORY: Before searching connectors, retrieving evidence, or drafting output, read and apply the shared runtime contract in ../public-equity-investing/SKILL.md## Cross-Skill Runtime Contract. Then check the router skill map and ../../shared/plugin-routing-playbook.md for adjacent skills that should be sequenced with this workflow. Do not run user-context setup or inspection during ordinary workflow work; route only explicit saved-context, source-setup, onboarding, or automation-setup requests to ../user-context/SKILL.md.

Source Resolution

Load ../../shared/workflow-source-resolution.md. Resolve only the categories needed for this workflow: company_filings_ir, earnings_transcripts_presentations, internal_research, portfolio_models_trackers, and market_data_estimates. Use the shared runtime contract to map each attempted category to an available app, connector, file, export, or user-provided input.

Relevant Dependency Categories

These are the source categories most likely to matter for this workflow. Use the router contract to resolve only the categories the task actually needs, prefer user-named sources first, and state any material source limitation.

  • Company Filings & IR
  • Portfolio Models & Trackers
  • Market Data & Estimates

Deliverable Intake

When invoked as support for an owning workflow, inherit its resolved deliverable preferences and do not re-prompt. Only when this skill independently owns a new substantive standalone normalization deliverable should it, before source gathering, analysis, or rendering, load ../../shared/deliverable-intake-policy.md and perform its adaptive request_user_input preflight for materially unresolved preferences.

Purpose

Load shared/equity-research-support-standard.md and shared/support-layer-routing-contract.md before substantial source, data, QA, or style work.

Turn messy public-company source financials into auditable, model-ready normalized statements, KPI schedules, consensus/guidance inputs, segment schedules, share-count support, net-debt and capital-allocation support, source citations, assumptions, conflicts, and QA flags for downstream Public Equity Investing workflows.

Boundary: shipped scripts create Source_Index.csv, Normalized_Financials_Long.csv, and Normalization_Issues.csv. Wide statements, KPI schedules, adjustment logs, conflict logs, assumption registers, and workbook/deck-ready tabs are instruction-led unless explicitly built from staging data.

For a standalone request for model-ready normalized financials, do not treat a long-form staging CSV alone as the analyst deliverable. Create a model-loading package from the staged rows: full-scope wide schedules relevant to the supplied financials, a disclosure/comparability bridge when definitions or presentation changed, material QA flags, and logged validation checks. Use XLSX when the user requests a workbook or will load/review the output in a workbook; otherwise a clearly organized CSV package plus a concise review summary is appropriate. This is a data-first skill; do not force an HTML artifact unless the user requests one.

Embedded Support Routing

This is an embedded service under the owning workflow unless the user explicitly asks for standalone normalization. Preserve the owning_workflow internally, such as equity-model-update, dcf-model-builder, three-statement-model-builder, comps-valuation, earnings-preview, earnings-deep-dive, memo-builder, thesis-tracker, scenario-sensitivity-generator, portfolio-risk-management, or dashboard-builder.

For substantial embedded work, preserve decision_impact, readiness_effect, artifact_role, and hidden_unless_requested in internal context or support artifacts. Do not print those internal field names in the owning workflow's user-facing artifact. Do not own the valuation, memo, earnings, or recommendation; state in natural language how normalization issues change estimate confidence, valuation support, target support, sizing, model readiness, or circulation readiness. Source_Index.csv, Normalized_Financials_Long.csv, Normalization_Issues.csv, run logs, manifests, and support notes are secondary/support artifacts when invoked by an owning workflow.

Non-Negotiables

  • Preserve raw/source materials.
  • Prefer user files/context, callable runtime apps/connectors when actually available, primary public sources, user-provided provider exports, then labeled assumptions. Never imply live provider access when it is unavailable.
  • Never invent missing financials; mark unavailable values as missing_required_source.
  • Keep normalized values traceable to source ID, source name/location, retrieved-at date, period, units, currency, and evidence label.
  • Missing source_id must remain visible as SRC-UNSPECIFIED, produce a QA flag, and block decision-grade handoff.
  • Retain conflicts rather than silently choosing values.
  • Flag stale, preliminary, unaudited, OCR-derived, or low-confidence data.
  • Do not infer fiscal period-end dates from quarter labels alone; use an explicit source date or mark the date missing and flag it.
  • Keep issuer outlook or guidance in kpi_schedule with issuer_management_claim; reserve consensus_estimate with estimate_consensus for externally sourced consensus estimates.
  • When a segment, KPI, non-GAAP definition, or balance-sheet presentation changes, preserve both bases and create a comparability bridge before calling any series model-loadable.
  • Apply comparability status at the affected series or line-item level. A changed cash presentation does not recast unrelated balance-sheet rows.
  • Use comparable_rounded when an unchanged reported series is comparable across periods but only available in rounded narrative units; disclose that it is unsuitable for exact tie-out.
  • Do not backsolve an undisclosed comparable value from rounded amounts or percentage growth for model loading. A labeled directional calculation may appear separately only when useful.
  • Material open exceptions must be surfaced in QA_Flags and in the readiness summary; an empty technical Normalization_Issues file does not mean the financials are clear for downstream use.

Workflow

  1. Classify job. Public-equity issuer financials, earnings/model update, consensus/provider export, ETF/index constituent support, portfolio/market-data support, or equity-risk debt/liquidity context.
  2. Build source index. Capture source ID, name/type, owner/provider, period, as-of date, retrieved-at date, location, source rank, freshness, and notes.
  3. Extract long-form staging. Use Normalized_Financials_Long before wide statements; preserve original line labels beside canonical labels.
  4. Normalize periods, scale, currency, signs, and labels. Keep reported, adjusted, pro forma, provider-standardized, estimated, and analyst-adjusted values separate.
  5. Reconcile disclosure changes. Identify renamed, regrouped, newly introduced, discontinued, recast, or definition-changed segments/KPIs/non-GAAP lines; preserve legacy and new bases; create a disclosure/comparability bridge with model treatment.
  6. Reconcile and QA. Check subtotals, roll-forwards, balance sheet balance, cash flow bridge, units/currency, duplicate periods, missing sources, stale/conflicting values, signs, unsupported KPIs, and completeness of the intended model-loading scope. Log performed checks and results; do not claim a check count that is not preserved in an output.
  7. Produce package. For standalone model-ready work, produce loadable wide schedules plus QA_Flags and Validation_Checks from the audited staging layer; include a disclosure/comparability bridge whenever presentation changed. For narrower extraction/support work, return only the deterministic CSV outputs or instruction-led tabs actually created.
  8. Hand off. State what is loadable, what is audit-only, and what remains partial or blocked before routing to models, earnings, comps, memo, thesis, scenario, risk, ETF/index, or deck/report skills. Route covenant/recovery/debt-security normalization to Credit Markets.

Sub-agent decomposition

For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: source inventory, line-item mapping, period/unit normalization, conflict log, and QA. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.

When embedded in a broader workflow, "lead" means lead for normalization only; the owning workflow remains the investment-artifact owner.

Evidence Labels

Use exact labels from references/normalization-schema.md, including fact_source_reported, fact_provider_standardized, derived_calculation, issuer_management_claim, management_adjusted, analyst_adjusted, analyst_interpretation, assumption_user_provided, assumption_inferred, estimate_consensus, stale_source, contradicted_source, missing_required_source, and unknown.

Confidence labels are high, medium, or low.

Scripts

python scripts/normalize_extracted_financials.py path/to/input.csv --output-dir output
python scripts/validate_normalized_financials.py output/Normalized_Financials_Long.csv

For workbook inputs, first extract the relevant tab/range with spreadsheet tools into a table/CSV; scripts must not destructively modify workbooks.

Final Response

Return:

  1. what was normalized: entity, sources, periods, units, currency, scope;
  2. outputs created;
  3. what can be loaded into a model and what remains audit-only, partial, or blocked;
  4. material QA findings and disclosure/comparability breaks;
  5. fact versus assumption summary and validation checks actually performed;
  6. recommended next step or missing source.

Reference Map

  • references/source-protocol.md: hierarchy, stale data, citations, conflicts.
  • references/normalization-schema.md: output schema, signs, scales, labels.
  • references/line-item-taxonomy.md: statement/KPI mappings.
  • references/qa-rules.md: reconciliation tests and red flags.
  • references/integration-guide.md: downstream Public Equity Investing handoffs.

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