Modeler
sidequery/sidemantic
Build, validate, and manage semantic models using Sidemantic.
Analytics engineering across data modeling, dbt, transformation, and semantic layers.
$ npx skills add borghei/Claude-Skills --skill analytics-engineer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install borghei/Claude-Skills analytics-engineer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/data-analytics/analytics-engineer .claude/skills/analytics-engineer && rm -rf skills-srcUse ~/.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/
Install the "analytics-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/data-analytics/analytics-engineer into .claude/skills/analytics-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-engineer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/borghei/Claude-Skills/tree/main/data-analytics/analytics-engineerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add borghei/Claude-Skills --skill analytics-engineer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install borghei/Claude-Skills analytics-engineer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/data-analytics/analytics-engineer .agents/skills/analytics-engineer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analytics-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/data-analytics/analytics-engineer into .agents/skills/analytics-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-engineer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill analytics-engineer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install borghei/Claude-Skills analytics-engineer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/data-analytics/analytics-engineer .cursor/skills/analytics-engineer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "analytics-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/data-analytics/analytics-engineer into .cursor/skills/analytics-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-engineer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/borghei/Claude-Skills.git --path data-analytics/analytics-engineer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add borghei/Claude-Skills --skill analytics-engineer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install borghei/Claude-Skills analytics-engineer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/data-analytics/analytics-engineer .gemini/skills/analytics-engineer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "analytics-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/data-analytics/analytics-engineer into .gemini/skills/analytics-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-engineer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install borghei/Claude-Skills analytics-engineerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add borghei/Claude-Skills --skill analytics-engineer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/data-analytics/analytics-engineer .github/skills/analytics-engineer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "analytics-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/data-analytics/analytics-engineer into .github/skills/analytics-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-engineer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill analytics-engineer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install borghei/Claude-Skills analytics-engineer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/data-analytics/analytics-engineer .opencode/skills/analytics-engineer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "analytics-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/data-analytics/analytics-engineer into .opencode/skills/analytics-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analytics-engineer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
analytics-engineerAnalytics engineering across data modeling, dbt, transformation, and semantic layers.
Analytics Engineer is an agent skill from borghei/Claude-Skills. Analytics engineering across data modeling, dbt, transformation, and semantic layers. Use when building dbt models, designing star schemas, writing staging or mart SQL, configuring data tests, or optimizing warehouse queries.
Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including scripts, reference files and assets (for example `REFERENCE.md`, `assets/dbt_project.yml` and `assets/schema.yml`).
It sits in Data & Analytics, covering Data pipelines and ETL, Data warehousing and Database schema design. It works with dbt and SQL. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.
8 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit c9a1487. It shows what the files ask for, not the result of running them.
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.
Ships 4 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
dbtpythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Analytics Engineer loads about 3.4k tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 61 tokens; SKILL.md has 993 words of instructions outside code blocks.
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.
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); the scripts in this folder are not scanned.
The full file from borghei/Claude-Skills at commit c9a1487, republished under its MIT licence (© borghei). 993 words, ~3,401 tokens.
.claude/skills/analytics-engineer/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.The agent operates as a senior analytics engineer, building scalable dbt transformation layers, designing dimensional models, writing tested SQL, and managing semantic-layer metric definitions.
Before building the models, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
stg_ model per source table. Rename columns, cast types, filter soft-deletes, and add metadata columns. Validate: dbt build --select stg_*.int_ models (e.g., int_orders_enriched). Keep each CTE single-purpose.dim_ and fct_ models for consumption. Configure materialization (view for staging, incremental for large facts, table for small marts).unique + not_null. Foreign keys get relationships. Add accepted_values for enums. Write model descriptions in YAML.dbt build, confirm test pass rate = 100%, check row counts against source, and verify dashboard numbers match.analytics/
dbt_project.yml
models/
staging/ # stg_<source>__<table>.sql (one per source table)
intermediate/ # int_<entity>_<verb>.sql (reusable logic)
marts/
core/ # dim_*.sql, fct_*.sql (consumption-ready)
marketing/
finance/
macros/ # Reusable Jinja helpers
tests/ # Custom generic + singular tests
seeds/ # Static CSV lookups
snapshots/ # SCD Type 2 capturesStaging model (models/staging/crm/stg_crm__customers.sql):
WITH source AS (
SELECT * FROM {{ source('crm', 'customers') }}
),
renamed AS (
SELECT
id AS customer_id,
TRIM(LOWER(name)) AS customer_name,
TRIM(LOWER(email)) AS email,
created_at::timestamp AS created_at,
updated_at::timestamp AS updated_at,
is_active::boolean AS is_active,
_fivetran_synced AS _loaded_at
FROM source
WHERE _fivetran_deleted = false
)
SELECT * FROM renamedMart model (models/marts/core/dim_customer.sql):
WITH customers AS (
SELECT * FROM {{ ref('stg_crm__customers') }}
),
customer_orders AS (
SELECT
customer_id,
MIN(order_date) AS first_order_date,
MAX(order_date) AS most_recent_order_date,
COUNT(*) AS lifetime_orders,
SUM(order_amount) AS lifetime_value
FROM {{ ref('stg_orders__orders') }}
GROUP BY customer_id
),
final AS (
SELECT
c.customer_id,
c.customer_name,
c.email,
c.created_at,
co.first_order_date,
co.most_recent_order_date,
co.lifetime_orders,
co.lifetime_value,
CASE
WHEN co.lifetime_value >= 10000 THEN 'platinum'
WHEN co.lifetime_value >= 5000 THEN 'gold'
WHEN co.lifetime_value >= 1000 THEN 'silver'
ELSE 'bronze'
END AS customer_tier
FROM customers c
LEFT JOIN customer_orders co
ON c.customer_id = co.customer_id
)
SELECT * FROM finalTest configuration (models/marts/core/_core__models.yml):
version: 2
models:
- name: dim_customer
description: Customer dimension with lifetime order metrics and tier classification.
columns:
- name: customer_id
tests: [unique, not_null]
- name: email
tests: [unique, not_null]
- name: customer_tier
tests:
- accepted_values:
values: ['platinum', 'gold', 'silver', 'bronze']
- name: lifetime_value
tests:
- dbt_utils.expression_is_true:
expression: ">= 0"-- models/marts/core/fct_orders.sql
{{
config(
materialized='incremental',
unique_key='order_id',
partition_by={'field': 'order_date', 'data_type': 'date'},
cluster_by=['customer_id', 'product_id']
)
}}
WITH orders AS (
SELECT * FROM {{ ref('stg_orders__orders') }}
{% if is_incremental() %}
WHERE order_date >= (SELECT MAX(order_date) FROM {{ this }})
{% endif %}
),
order_items AS (
SELECT * FROM {{ ref('stg_orders__order_items') }}
),
final AS (
SELECT
o.order_id,
o.order_date,
o.customer_id,
oi.product_id,
o.store_id,
oi.quantity,
oi.unit_price,
oi.quantity * oi.unit_price AS line_total,
o.discount_amount,
o.tax_amount,
o.total_amount
FROM orders o
INNER JOIN order_items oi ON o.order_id = oi.order_id
)
SELECT * FROM final| Layer | Materialization | Rationale |
|---|---|---|
| Staging | View | Thin wrappers; no storage cost |
| Intermediate | Ephemeral / View | Business logic; referenced multiple times |
| Marts (small) | Table | Query performance for BI tools |
| Marts (large) | Incremental | Efficient appends for large fact tables |
# models/marts/core/_core__metrics.yml
metrics:
- name: revenue
label: Total Revenue
model: ref('fct_orders')
calculation_method: sum
expression: total_amount
timestamp: order_date
time_grains: [day, week, month, quarter, year]
dimensions: [customer_tier, product_category, store_region]
filters:
- field: is_cancelled
operator: '='
value: 'false'
- name: average_order_value
label: Average Order Value
model: ref('fct_orders')
calculation_method: average
expression: total_amount
timestamp: order_date
time_grains: [day, week, month]-- macros/cents_to_dollars.sql
{% macro cents_to_dollars(column_name) %}
({{ column_name }} / 100.0)::decimal(18,2)
{% endmacro %}
-- macros/get_incremental_filter.sql
{% macro get_incremental_filter(column_name, lookback_days=3) %}
{% if is_incremental() %}
WHERE {{ column_name }} >= (
SELECT DATEADD(day, -{{ lookback_days }}, MAX({{ column_name }}))
FROM {{ this }}
)
{% endif %}
{% endmacro %}# Only run modified models and their downstream dependents
dbt run --select state:modified+ --defer --state ./target-base
dbt test --select state:modified+ --defer --state ./target-baseFor full CI/CD pipeline configuration, see REFERENCE.md.
REFERENCE.md -- Extended patterns: source config, custom tests, CI/CD workflows, exposures, documentation templatesreferences/modeling_patterns.md -- Data modeling best practicesreferences/dbt_style_guide.md -- SQL and dbt conventionsreferences/testing_guide.md -- Testing strategiesreferences/optimization.md -- Performance tuningpython scripts/impact_analyzer.py --model dim_customer
python scripts/schema_diff.py --source prod --target dev
python scripts/doc_generator.py --format markdown
python scripts/quality_scorer.py --model fct_orders| Tool | Purpose | Key Flags |
|---|---|---|
impact_analyzer.py | Trace downstream impact of a dbt model via BFS on the manifest DAG | --model <name>, --manifest <path>, --json |
schema_diff.py | Compare two dbt catalog.json files to detect column additions, removals, and type changes | --source <path>, --target <path>, --json |
doc_generator.py | Generate markdown documentation (column dictionary, dependencies, tests) for a dbt model | --model <name>, --manifest <path>, --catalog <path> |
quality_scorer.py | Score a dbt model 0-100 based on documentation, testing, and layer-convention adherence | --model <name>, --manifest <path>, --json |
| Problem | Likely Cause | Resolution |
|---|---|---|
dbt build fails with "relation does not exist" | Upstream model was not run or materialization changed | Run dbt build --select +<model> to build the full upstream chain |
| Incremental model produces duplicates | unique_key does not match the actual grain | Verify the unique_key config matches the primary key columns; run a full refresh with --full-refresh |
Test failures on not_null after deployment | Source data introduced unexpected NULLs in a previously clean column | Add a staging-layer COALESCE or adjust the test to warn severity while investigating upstream |
Schema drift detected by schema_diff.py | Upstream source changed column types or removed columns | Coordinate with the data engineering team; update staging model casts and regenerate documentation |
| Semantic-layer metric values differ from dashboard | Dashboard applies its own filters or calculations outside the semantic layer | Move all calculation logic into the semantic layer; audit dashboard-level computed fields |
Slow dbt run on large incremental models | Lookback window is too wide or partition pruning is not engaged | Narrow the incremental filter, verify partition_by config, and check warehouse query plan |
quality_scorer.py reports low score despite good coverage | Staging model contains JOINs or GROUP BY operations triggering layer-violation penalties | Refactor aggregation logic into intermediate or mart models; keep staging models as thin wrappers |
dbt build with a 100% test pass rate before merging to production.unique + not_null).quality_scorer.py reports >= 80/100 for every mart model.In scope: dbt project design, dimensional modeling (Kimball methodology), SQL transformation logic, data testing, semantic-layer metric definition, CI/CD for dbt, and warehouse query optimization.
Out of scope: Raw data ingestion and extraction (ELT/ETL orchestration tools like Fivetran or Airbyte), data infrastructure provisioning, BI tool configuration beyond semantic-layer integration, and real-time streaming pipelines.
Limitations: The Python tools operate on dbt manifest/catalog JSON artifacts and do not query the warehouse directly. Scoring heuristics in quality_scorer.py use rule-based deductions that may not cover every project convention. All scripts use the Python standard library only -- no external dependencies required.
engineering/senior-data-engineer): Coordinates on source table contracts, ingestion SLAs, and schema change notifications.data-analytics/business-intelligence): Consumes mart models and semantic-layer metrics; dashboard specs reference model outputs.data-analytics/data-analyst): Writes ad-hoc queries against mart models; reports data quality issues back to the analytics engineer.data-analytics/ml-ops-engineer): Feature engineering pipelines may depend on intermediate or mart models as upstream inputs.templates/): Slim CI patterns (state:modified+) integrate into GitHub Actions or similar runners for automated PR validation.© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 13 other files (scripts, references, assets) in data-analytics/analytics-engineer of borghei/Claude-Skills.
Open the folder on GitHubat commit c9a1487
Analytics Engineer 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Analytics Engineer this skillborghei/Claude-Skills | 874 | — | ~3.4k | Automated safety check: Pass | MIT | |
| Modelersidequery/sidemantic | 129 | — | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Analyzing Dataastronomer/agents | 450 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Snowflake Developmentsickn33/agentic-awesome-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Snowflake Developmentalirezarezvani/claude-skills | 28k | — | ~3.2k | Automated safety check: Pass | MIT | |
| Uipath Process MiningUiPath/skills | 166 | — | ~4.3k | Automated safety check: Notes | MIT |
sidequery/sidemantic
Build, validate, and manage semantic models using Sidemantic.
astronomer/agents
Queries the data warehouse with SQL and answers business questions about data.
sickn33/agentic-awesome-skills
Comprehensive Snowflake development assistant covering SQL best practices, data pipeline design (Dynamic Tables, Streams, Tasks, Snowpipe), Cortex AI functions, Cortex Agents, Snowpark Python, dbt…
alirezarezvani/claude-skills
A skill your agent uses when writing Snowflake SQL, building data pipelines with Dynamic Tables or Streams/Tasks, using Cortex AI functions, creating Cortex Agents, writing Snowpark Python…
UiPath/skills
UiPath Process Mining via uip pm — build and operate a process app end-to-end from a CSV / event log: templates, data mapping, upload, ingest, the dbt (Snowflake) transformation layer, publish, and…
liam-machine/erd-studio
Friendly, step-by-step setup for ERD Studio in an existing dbt project, for people who may be new to dbt or data modelling.
borghei/Claude-Skills
Run delivery when AI coding and ops agents take tickets. An agent skill from borghei/Claude-Skills.
borghei/Claude-Skills
Check AI-generated marketing content and reviews for required disclosures under the EU AI Act, FTC rules and platform AI-label policies.
borghei/Claude-Skills
Idea to AI-generated prototype to customer validation to engineering handoff.
borghei/Claude-Skills
Ansoff Matrix — 4-quadrant framework for growth options: market penetration, market/product development, and diversification.
borghei/Claude-Skills
OKR brainstorming and validation using the Radical Focus framework — outcome objectives, measurable key results, counter-metrics.
borghei/Claude-Skills
Build, evaluate, and stress-test a Business Model Canvas (Osterwalder) across all 9 blocks.
Categories
Analytics engineering across data modeling, dbt, transformation, and semantic layers. Analytics Engineer is an agent skill from borghei/Claude-Skills. Analytics engineering across data modeling, dbt, transformation, and semantic layers.
Analytics Engineer fits situations like: building dbt models; designing star schemas; writing staging; configuring data tests.
Run `npx skills add borghei/Claude-Skills --skill analytics-engineer -a claude-code`. Or copy the skill folder (data-analytics/analytics-engineer in borghei/Claude-Skills) into .claude/skills/analytics-engineer in your project. Claude Code loads it when a task matches its description.
Run `npx skills add borghei/Claude-Skills --skill analytics-engineer -a codex`. Or copy the skill folder (data-analytics/analytics-engineer in borghei/Claude-Skills) into .agents/skills/analytics-engineer in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill analytics-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analytics-engineer, .gemini/skills/analytics-engineer, .github/skills/analytics-engineer and .opencode/skills/analytics-engineer in your project.
Going by SKILL.md and its folder, Analytics Engineer needs Python for the scripts in its folder and the command-line tools its instructions call (dbt and python). Our summary lists: Python 3.
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.
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Analytics Engineer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.9k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Analytics Engineer: Modeler (sidequery/sidemantic, 129 stars), Analyzing Data (astronomer/agents, 450 stars), Snowflake Development (sickn33/agentic-awesome-skills, 47k stars) and Snowflake Development (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 874 GitHub stars. The repository holds 364 skills in this directory. The repository was last updated on October 7, 2026.
Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.