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financials-normalizer
Use when normalizing public-company financials from source materials. Do not use for private data rooms or non-financial cleanup.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Use when normalizing public-company financials from source materials. Do not use for private data rooms or non-financial cleanup.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Start onboarding, initialize, inspect, save, update, forget, export, or explicitly reset the Public Equity Investing plugin's local user context, source setup, or optional automation setup. Use when the user explicitly asks to get started, orient, or manage Public Equity Investing saved preferences, source pointers, context storage, or recurring automation.
Run scheduled or manual Sales check-ins that summarize recent Sales work and recommend one next Sales workflow to try.
Load or manage the Sales plugin's durable user context, onboarding logic, setup progress, automation metadata, saved preferences, non-obvious CRM conventions, source-of-truth pointers, book-of-business sources, internal team resources, account channels, approval trackers, trusted examples, approved Sales Company Research saves, "please remember" requests, and broad future-facing instructions such as always/never/prefer/next-time feedback after a Sales draft.
Load or manage the Data Analytics plugin's durable source-routing preferences, onboarding logic, setup progress, and semantic-layer registry.
Assess whether tables, query results, files, or dataframes are trustworthy enough for analysis, modeling, dashboards, experiments, or pipelines. Use for grain, freshness, nulls, duplicates, schema drift, broken joins, referential integrity, distribution shifts, leakage, backfills, source mismatches, automated quality checks, and data-quality regressions.
Build source-backed analytical dashboards that help teams monitor performance, explore drivers, and act on product or business metrics. Use when the user needs a dashboard, scorecard, monitoring view, BI dashboard, MCP artifact dashboard, or Streamlit dashboard with clear metrics, filters, validation, and handoff.
| name | financials-normalizer |
| description | Use when normalizing public-company financials from source materials. Do not use for private data rooms or non-financial cleanup. |
Before searching connectors, retrieving evidence, or drafting output, run python3 skills/user-context/scripts/user_context_preflight.py with the shell working directory set to this plugin's root, and follow the returned saved_context, source_category_plan, and next_action. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
If next_action.id = "offer_orientation" and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
Load ../../shared/workflow-source-resolution.md. Use source_category_plan lazily and attempt only the categories needed for this workflow: company_filings_ir, earnings_transcripts_presentations, internal_research, portfolio_models_trackers, and market_data_estimates.
Apply the presentation-surface precedence in ../../shared/deliverable-intake-policy.md. This workflow's natural artifact is an XLSX normalization workbook. Do not choose chat-only output unless the user explicitly requests a lightweight response.
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 standalone reader-facing 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.
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.
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.
missing_required_source.source_id must remain visible as SRC-UNSPECIFIED, produce a QA flag, and block decision-grade handoff.kpi_schedule with issuer_management_claim; reserve consensus_estimate with estimate_consensus for externally sourced consensus estimates.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.QA_Flags and in the readiness summary; an empty technical Normalization_Issues file does not mean the financials are clear for downstream use.Normalized_Financials_Long before wide statements; preserve original line labels beside canonical labels.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.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.
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.
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.
Return:
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.