Agent skill

Reference Data

by JoelLewis in JoelLewis/finance_skills

Design and manage reference data systems — security master, client master, account master, identifier mapping, pricing data sources, golden source designation, and governance.

MITAuto-check passedData & Analytics

Install Reference Data

skills CLI
$ npx skills add JoelLewis/finance_skills --skill reference-data -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install JoelLewis/finance_skills reference-data --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/JoelLewis/finance_skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/data-integration/skills/reference-data .claude/skills/reference-data && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
reference-data
GitHub stars
205
Token cost
~5.4k tokens
SKILL.md length
2,512 words
Files
1
Skills in repo
91
Repo updated
First seen
Licence
MIT

At a glance

Design and manage reference data systems — security master, client master, account master, identifier mapping, pricing data sources, golden source designation, and governance.

  • Works in 8 steps: Security Master → Identifier Systems → Client Master → …
  • Evaluating a security master database
  • SKILL.md covers Core Concepts, Worked Examples, Common Pitfalls and Cross-References
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Reference Data is an agent skill from JoelLewis/finance_skills. Design and manage reference data systems — security master, client master, account master, identifier mapping, pricing data sources, golden source designation, and governance. Use when building or evaluating a security master database, mapping identifiers across systems (CUSIP to ISIN, SEDOL to FIGI), designing client master models for onboarding or KYC, defining account master attributes across custodians, designating golden sources and MDM patterns across systems, establishing a pricing vendor hierarchy with…

Its SKILL.md is about 5.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Data governance. The repository describes itself as: Claude Code skill plugins for financial services — 81 skills across 7 domain plugins covering investment management, compliance, advisory practice, trading, and operations. The licence is MIT.

When your agent uses it

  • Evaluating a security master database
  • Mapping identifiers across systems (CUSIP to ISIN
  • Designing client master models for onboarding
  • Defining account master attributes across custodians

Example prompts

  • “/reference-data”

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Security Master
  2. Identifier Systems
  3. Client Master
  4. Account Master
  5. Pricing Data
  6. Reference Data Governance
  7. Reference Data Distribution
  8. Vendor Management

What it can do on your machine

Read from SKILL.md and the folder at commit 5c498ea. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Reference Data loads about 5.4k tokens when it runs. Until then it costs about 232 tokens; SKILL.md has 2,512 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~232
When it runs · the whole SKILL.md, loaded when a task matches
~5.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from JoelLewis/finance_skills at commit 5c498ea, republished under its MIT licence (© JoelLewis). 2,512 words, ~5,359 tokens.

Download SKILL.mdSave it as .claude/skills/reference-data/SKILL.md (or your agent's skills folder).
name
reference-data
description
Design and manage reference data systems — security master, client master, account master, identifier mapping, pricing data sources, golden source designation, and governance. Use when building or evaluating a security master database, mapping identifiers across systems (CUSIP to ISIN, SEDOL to FIGI), designing client master models for onboarding or KYC, defining account master attributes across custodians, designating golden sources and MDM patterns across systems, establishing a pricing vendor hierarchy with fallback order, establishing reference data governance and stewardship, handling identifier changes from corporate actions, or troubleshooting issues traced to missing or changed identifiers. Trigger on: security master, CUSIP, ISIN, SEDOL, FIGI, client master, account master, pricing data, reference data, golden source, MDM, master data, identifier mapping, data governance, vendor hierarchy.

Reference Data

Core Concepts

1. Security Master

The security master is the authoritative repository of instrument-level reference data, serving as the foundation for portfolio management, trading, performance, reporting, and compliance systems.

Core fields: Identifiers (CUSIP, ISIN, SEDOL, FIGI, ticker, internal ID), classification (asset class, sub-class, GICS sector, country, currency), issuer information (name, LEI, ratings), terms and conditions (asset-class-specific contractual attributes), and pricing factors (multiplier, day count, settlement convention).

Asset-class-specific attributes: Equity (shares outstanding, market cap, sector, exchange, dividend frequency). Fixed income (coupon, maturity, call schedule, credit rating, seniority, day count). Funds (NAV, expense ratio, share class, distribution frequency, load structure). ETFs (NAV, indicative value, expense ratio, underlying index). Options (underlying, strike, expiration, type, multiplier, exercise style). Alternatives (strategy type, vintage year, commitment, capital call schedule, valuation frequency).

Security lifecycle events: IPO/listing (new record creation with all required fields), corporate actions (splits, mergers, spin-offs, name/ticker/exchange changes — the single largest source of security master data quality issues), and delisting/maturity (flag as inactive, retain historical record for performance and audit).

Golden source designation: The security master should be the firm's golden source for instrument reference data. All downstream systems retrieve security attributes from it rather than maintaining independent copies.

2. Identifier Systems

Financial instruments carry multiple overlapping identifiers. No single scheme is universally sufficient.

  • CUSIP — 9-character (6 issuer + 2 issue + 1 check digit), US/Canada securities, administered by CUSIP Global Services (FactSet). Changes on fundamental term changes (mergers, reorganizations).
  • ISIN — 12-character (2 country + 9 national ID + 1 check digit), ISO 6166, globally unique. Required for MiFID II, EMIR reporting. Wraps the national identifier (CUSIP in US, SEDOL in UK).
  • SEDOL — 7-character, assigned by the London Stock Exchange for LSE and UK/Irish listings. A single security may have different SEDOLs per listing exchange.
  • FIGI — 12-character, developed by Bloomberg under OMG standard. Open-source and freely available (unlike CUSIP/SEDOL). Distinguishes instrument-level, composite, and exchange-level identifiers.
  • Ticker symbols — Exchange-assigned, not globally unique, change frequently. Never use as a primary identifier; use only for display or trading convenience.
  • Internal identifiers — Firm-generated (UUID or sequential integer), immutable across the security's life. Serves as the stable key linking all external identifiers.

Identifier mapping: The security master must maintain a cross-reference table linking all identifiers per security. Challenges include one-to-many relationships (one ISIN to multiple SEDOLs), temporal changes from corporate actions (versioned mappings for point-in-time lookups), and vendor discrepancies requiring conflict resolution.

3. Client Master

The client master is the authoritative repository of client-level reference data supporting onboarding, account management, KYC/AML compliance, reporting, and relationship management.

Data model: Individuals (legal name, DOB, SSN/TIN, address, phone, email, citizenship, employment). Entities (legal name, EIN, formation state/country, entity type, formation date). Trusts (trust name, type, grantor, trustee, beneficiaries, governing law).

Identity management: Each client must have a single unique identifier regardless of how many accounts or roles they hold. Deduplication is critical for regulatory reporting, household billing, cross-account compliance (wash sales), and unified servicing.

Household and relationships: Household grouping links related clients for billing, reporting, and planning. Relationship types include spouse, parent/child, trustee/beneficiary, authorized signer, trusted contact. Advisor assignment (primary, secondary, service team) is tracked here.

KYC/AML data: Verification status, method, risk rating, PEP status, OFAC screening results, beneficial ownership (entities), source of funds/wealth, EDD flags.

Client preferences: Communication channel, statement delivery, language, investment preferences (ESG exclusions, sector restrictions), tax lot method preference.

Golden source: CRM is typically the golden source for relationship and advisory data; custodian is the golden source for regulatory identity data (legal name, SSN, address of record). The client master integrates both and enforces synchronization.

4. Account Master

The account master stores attributes and configuration of every client account, linking accounts to clients and driving trading, billing, reporting, and compliance.

Attributes: Registration (individual, joint, trust, IRA, Roth, SEP, 401(k), corporate, estate, UGMA/UTMA), account type (advisory, brokerage, wrap, retirement, education, charitable), tax status (taxable, tax-deferred, tax-exempt), custodian, advisor, model assignment, features (margin, options level, DRIP, cost basis method).

Account-to-client relationships: Owner, authorized party (POA, trading authorization), beneficiary, trustee, custodian (for UGMA/UTMA).

Status management: Active, restricted (regulatory hold, death notification, legal dispute), closed (retain historical data per books-and-records requirements), dormant (escheatment/unclaimed property exposure).

Cross-system identification: Custodian account number, PMS account ID, CRM account ID, and billing account ID may all differ. The account master maintains mappings and provides a canonical internal ID as the cross-system key.

5. Pricing Data

Pricing is the most time-sensitive reference data category. Incorrect prices propagate immediately into valuations, performance, billing, and compliance.

End-of-day pricing: Official exchange close (4:00 PM ET for US equities), mutual fund NAV (available 6:00-7:00 PM ET), evaluated pricing for infrequently traded fixed income (Bloomberg, ICE, Refinitiv models).

Pricing hierarchy: Define a preferred source per security type with automatic fallback. Example: US equities — exchange close, then Bloomberg, then Refinitiv. Corporate bonds — ICE evaluated, then Bloomberg BVAL, then Refinitiv. Alternatives — manager/GP valuation, then third-party appraisal, then internal model.

Fair value pricing: Adjust international securities' closing prices for subsequent market, currency, and news developments when foreign exchanges close hours before the US close. Prevents stale-price arbitrage in mutual funds.

Pricing validation and stale-price detection: Validation-rule design and execution (variance checks, zero/negative-price detection, stale-price windows, cross-source comparison, threshold calibration) are owned by the data-quality skill (data-integration plugin); reference-data owns the source hierarchy those rules validate against.

Manual overrides: Restricted to authorized users, documented with reason and source, logged in an audit trail, time-limited pending vendor correction.

6. Reference Data Governance

Governance establishes the structures, policies, and processes ensuring reference data is accurate, complete, timely, and consistent.

Data ownership: Each domain has a designated owner — security master (investment operations), client master (co-owned by compliance and advisory practice), account master (operations/client services), pricing (portfolio accounting/valuation). Owners are accountable for quality and authorize changes to definitions and sources.

Data stewardship: Stewards execute governance daily — monitoring quality dashboards, resolving exceptions, coordinating with vendors, approving overrides, maintaining data dictionaries.

Data quality metrics: Measure reference data on the standard quality dimensions (completeness, accuracy, timeliness, consistency). Dimension definitions, validation-rule design, profiling, exception management, and threshold calibration are owned by the data-quality skill (data-integration plugin).

MDM patterns: Registry (links only, no conflict resolution), consolidation (read-only golden record aggregated from sources), coexistence (bidirectional sync between MDM and sources), transaction/hub (single system of entry for all reference data).

Change management: Impact analysis before changes, non-production testing, downstream notification, rollback procedures, post-change validation.

Audit trail: Log every change with old value, new value, timestamp, user/process, and reason. Required for regulatory examination, dispute resolution, and root-cause analysis.

7. Reference Data Distribution

Mastered reference data must be distributed reliably to all consuming systems.

Publishing models: Event-driven/push (message bus or event stream for near-real-time propagation), polling/pull (scheduled queries — simpler but introduces latency), bulk/files (CSV/XML/JSON via SFTP or S3 for EOD snapshots), API/on-demand (point-of-need retrieval — eliminates cache staleness but creates runtime dependency).

Delta distribution: Send only changed records since the last distribution. Requires change tracking, sequencing, and full-refresh fallback capability.

Caching: Local caching in consuming systems reduces latency but risks staleness. Mitigate with TTL policies, event-driven cache invalidation, and version numbers.

Point-in-time retrieval: Support queries for the state of any record as of a specific date. Required for historical performance, regulatory reporting, and audit. Implement via effective-date/expiration-date columns or full history tables.

8. Vendor Management

Data vendors are the primary external source for security reference data, pricing, ratings, and identifiers.

Major vendors: Bloomberg (broad coverage, FIGI, real-time pricing), Refinitiv/LSEG (global coverage, evaluated pricing), ICE Data Services (fixed income pricing, indices), S&P Global (ratings, CUSIP, fundamentals), Moody's (credit ratings, risk data), FactSet (multi-source aggregation, CUSIP Global Services), MSCI (ESG ratings, factor data, indices).

Evaluation criteria: Coverage (asset classes, geographies), quality (accuracy, completeness, error correction), timeliness (availability relative to processing deadlines), format/delivery (API, file, schema stability), licensing terms (redistribution rights, per-user fees), cost (total cost of ownership), support quality.

Multi-vendor strategy: Source critical data from at least two vendors — primary, backup, and arbitration rules for disagreements.

Vendor monitoring: Track file arrival times, monitor record counts for unexpected drops, compare against independent sources, maintain quarterly vendor scorecards, escalate persistent quality issues.

Worked Examples

Example 1: Designing a Security Master for a Multi-Custodian Advisory Firm

Scenario: An RIA manages $1.2B across 1,500 accounts at Schwab and Fidelity, trading US/international equities, ETFs, mutual funds, corporate bonds, and municipal bonds. Each custodian feed populates separate security tables — no unified security master exists. Recurring issues: the same mutual fund appears with different names per custodian, corporate bonds lack consistent credit ratings, and a stock split was processed for Schwab but missed for Fidelity because corporate actions were handled independently.

Design considerations: The firm defines a security master record with immutable internal UUID, all external identifiers (CUSIP, ISIN, SEDOL, FIGI, ticker, custodian-specific IDs), classification fields (asset class, GICS sector, credit tier), issuer data (name, LEI, country), asset-class-specific terms, pricing fields, and status. A cross-reference table links each internal ID to all external identifiers with effective dates for point-in-time lookups. Bloomberg is the primary vendor for reference data and pricing; ICE is secondary for fixed income; custodian feeds provide reconciliation checks; CUSIP Global Services provides identifier change notifications. All corporate actions are processed through the security master first, then propagated to both custodians — eliminating inconsistent processing. Data quality controls include daily completeness and pricing validation checks, weekly cross-vendor pricing comparison, and monthly classification audits.

Analysis: The centralized security master eliminates the root cause — disparate, unreconciled security data. The internal UUID survives corporate actions, identifier changes, and custodian transitions. The most significant ongoing cost is the corporate action workflow, requiring a dedicated data operations analyst as security master steward.

Show full SKILL.md (936 more words)Show less
Example 2: Implementing Pricing Data Management with Validation and Exception Handling

Scenario: An asset manager runs nightly valuations for 30 separate accounts and 5 commingled funds holding 2,000 securities (US/international equities, corporate/municipal bonds, MBS). Pricing comes from a single vendor (Refinitiv). Past incidents: a municipal bond valued at zero for three days unnoticed, an international equity stale for a week during a local holiday, a corporate bond evaluated price off by 15% due to vendor error.

Design considerations: The firm establishes a tiered pricing hierarchy per asset class with automatic fallback (e.g., corporate bonds: ICE evaluated, then Refinitiv, then Bloomberg BVAL). Automated validation runs before loading: variance check (15% equity, 5% bonds), zero-price check, stale-price check (2 days equities, 5 days bonds, adjusted for local holidays), cross-vendor comparison (flag >2% divergence for fixed income), currency verification, reasonableness bounds. Exceptions are severity-classified (critical/high/medium/low) and appear on a pricing dashboard. Critical exceptions trigger immediate alerts; the pricing analyst investigates, resolves (accept with documentation, substitute from backup vendor, or manual override), and logs all actions. Unresolved exceptions escalate after defined time windows. For international equities, a fair value model adjusts foreign closing prices using US market movement, currency changes, and sector ETF data.

Analysis: The framework transforms pricing from passive receipt to active quality control. Multi-vendor hierarchy eliminates single-vendor dependency. Automated validation catches all three prior incident types. A well-run pricing operation targets exception rates below 2% of the universe per day, with same-day resolution for critical and high-severity exceptions.

Example 3: Building Client Master Data Governance for Regulatory Compliance

Scenario: A wealth management firm serving 3,000 households discovers poor client data quality: 12% incomplete addresses, 8% stale employment data (clients known to be retired still listed as employed), 5% of entity clients missing beneficial ownership documentation, and inconsistent household groupings causing billing errors (missing breakpoint discounts). Client data exists in three systems (Salesforce CRM, Schwab custodian, Orion PMS) with no designated golden source and divergent records. The firm must remediate and establish governance before FinCEN's investment adviser AML/CFT program requirements take effect — originally January 1, 2026, delayed by FinCEN to January 1, 2028 (status as of June 2026; verify current).

Design considerations: Data owners are assigned by domain: client identity (CCO — regulatory significance), client relationships (Head of Client Services — servicing and billing), client financial profile (CIO — suitability). Golden sources: Schwab for legal identity data (verified through CIP, used for 1099s), Salesforce for relationship/advisory data, Orion as a consumer only (synchronized from the other two, not edited directly). Remediation: incomplete addresses resolved by cross-referencing CRM against custodian and contacting clients for gaps (target 100% in 90 days); stale employment flagged for clients 60+ not updated in two years, with automated triggers when systematic withdrawals begin; missing beneficial ownership collected using FinCEN forms, prioritized by account size (target 100% in 60 days); household groupings identified via shared addresses/phones/names, confirmed by advisors, with overbilling refunds issued. Ongoing governance: daily automated quality checks with exception dashboard, weekly steward review of trends, monthly metrics reporting to operations committee, quarterly governance review with data owners, annual comprehensive data refresh for inactive clients.

Analysis: The program addresses both immediate gaps and structural causes. Golden source designation eliminates cross-system ambiguity. The remediation requires significant advisor engagement, best positioned as a client service improvement. FinCEN's effective date (January 1, 2028 after the delay) still warrants early remediation — complete, verified client identity data takes years to build.

Common Pitfalls

  1. Using ticker symbols as primary identifiers. Tickers are not globally unique, change frequently, and are recycled. Systems keyed on tickers break during corporate actions.
  2. Maintaining separate security records per custodian. The same instrument appears as multiple securities, causing duplicated positions and inconsistent corporate action processing.
  3. Treating pricing as passive data receipt. Loading vendor prices without validation allows zero prices, stale prices, and vendor errors to propagate into valuations and billing.
  4. No designated golden source for client data. Multiple systems maintaining client data without hierarchy causes drift, conflicts, and no authoritative record.
  5. Ignoring temporal versioning. Overwriting current state without preserving history prevents point-in-time reporting, historical performance, and audit support.
  6. Underinvesting in corporate action processing. Corporate actions are the primary source of security master failures — treating them as low-priority clerical work causes persistent reconciliation breaks.
  7. Single-vendor pricing dependency. No backup source means manual pricing when the vendor is late, missing, or incorrect — which does not scale.
  8. Neglecting data quality metrics. Without measuring completeness, accuracy, timeliness, and consistency, issues are invisible until they cause downstream failures.
  9. Point-to-point integrations instead of central reference data service. Each consuming system connecting directly to vendors creates N redundant copies with independent quality issues.
  10. Failing to plan for identifier changes during corporate actions. Systems that do not handle CUSIP/ISIN/ticker changes gracefully lose position history and break performance chains.

Cross-References

  • market-data (data-integration plugin) — Market data covers real-time pricing and trade data; reference data provides the instrument master that market data references.
  • data-quality (data-integration plugin) — General data quality principles (profiling, validation, monitoring) applied here specifically to financial reference data.
  • integration-patterns (data-integration plugin) — Integration patterns (event-driven, batch, API) are the mechanisms for distributing reference data.
  • portfolio-management-systems (advisory-practice plugin) — The PMS is a primary consumer of security master, pricing, and account master data.
  • reconciliation (client-operations plugin) — Reference data mismatches (identifiers, stale prices) are a leading cause of reconciliation breaks.
  • corporate-actions (client-operations plugin) — Corporate actions drive security master changes; this skill covers the resulting reference data updates.
  • know-your-customer (compliance plugin) — KYC requirements define what client data must be collected; the client master stores and governs it.
  • books-and-records (compliance plugin) — Reference data records are books and records subject to retention and examination requirements.

© JoelLewis, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/data-integration/skills/reference-data of JoelLewis/finance_skills.

Open the folder on GitHubat commit 5c498ea

Compare with similar skills

Reference Data next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Reference Data compared with similar skills
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Reference Data this skillJoelLewis/finance_skills205—~5.4kAutomated safety check: PassMIT
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Openalgo Chart Indicatormarketcalls/openalgo-charts138—~3.4kAutomated safety check: NotesApache-2.0
Data Quality Frameworkswshobson/agents40k10 repos~1.1kAutomated safety check: PassMIT
Tracing Downstream Lineageastronomer/agents4501 repos~1.2kAutomated safety check: PassApache-2.0
Tracing Upstream Lineageastronomer/agents4501 repos~1.1kAutomated safety check: PassApache-2.0

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Questions about Reference Data

What does Reference Data do?

Design and manage reference data systems — security master, client master, account master, identifier mapping, pricing data sources, golden source designation, and governance. Reference Data is an agent skill from JoelLewis/finance_skills. Design and manage reference data systems — security master, client master, account master, identifier mapping, pricing data sources, golden source designation, and governance.

When should I use Reference Data?

Reference Data fits situations like: evaluating a security master database; mapping identifiers across systems (CUSIP to ISIN; designing client master models for onboarding; defining account master attributes across custodians.

How do I install Reference Data in Claude Code?

Run `npx skills add JoelLewis/finance_skills --skill reference-data -a claude-code`. Or copy the skill folder (plugins/data-integration/skills/reference-data in JoelLewis/finance_skills) into .claude/skills/reference-data in your project. Claude Code loads it when a task matches its description.

How do I install Reference Data in Codex?

Run `npx skills add JoelLewis/finance_skills --skill reference-data -a codex`. Or copy the skill folder (plugins/data-integration/skills/reference-data in JoelLewis/finance_skills) into .agents/skills/reference-data in your project. Codex loads it when a task matches its description.

Can I use Reference Data in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add JoelLewis/finance_skills --skill reference-data -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/reference-data, .gemini/skills/reference-data, .github/skills/reference-data and .opencode/skills/reference-data in your project.

What does Reference Data need to run?

SKILL.md names no scripts, command-line tools or credentials: Reference Data is instructions for the agent only.

Does Reference Data access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Reference Data safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Reference Data use?

Reference Data is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Reference Data use?

About 5.4k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Reference Data?

Skills that share tags, products or a category with Reference Data: Jeecg System (jeecgboot/skills, 237 stars), Openalgo Chart Indicator (marketcalls/openalgo-charts, 138 stars), Data Quality Frameworks (wshobson/agents, 40k stars) and Tracing Downstream Lineage (astronomer/agents, 450 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Reference Data?

JoelLewis (a GitHub user) maintains it in JoelLewis/finance_skills, which has 205 GitHub stars. The repository holds 91 skills in this directory. The repository was last updated on July 18, 2026.

Source: JoelLewis/finance_skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.