Borrowing it
Nothing to install: this file belongs to parthakker/nfl-analytics. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/parthakker/nfl-analytics/main/.claude/skills/new-view/SKILL.mdgit clone --depth 1 https://github.com/parthakker/nfl-analyticsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/parthakker/nfl-analytics/new-view)<a href="https://agentmods.dev/skills/parthakker/nfl-analytics/new-view"><img src="https://agentmods.dev/badge/skills/parthakker/nfl-analytics/new-view.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.00298 |
| Opus 5 | $0.00015 | $0.00149 |
| Sonnet 5 | $0.00006 | $0.00060 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
new-view scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
New warehouse view checklist
- Load the
warehouse-queriesskill first (grains, join keys, traps). - SQL into the VIEWS dict in
scripts/build_views.py— WITH a leading comment stating the grain and any convention (team-perspective spread, ref_key aggregation, canon_team usage). Tables the view needs must be created before the VIEWS loop. python -m nfl_analytics.cli views— confirm the row count printed for the new view is plausible.- Invariant test in
tests/warehouse/(row floor, symmetry/identity, or a pinned known value — whatever would catch silent corruption). Must pass on the CI fixture too (derive seasons from the DB; pytest.skip if the fixture slice can't contain the data). - Dictionary: add/extend the relevant
docs/dictionary/*.md. - Catalog: one line in
.claude/skills/warehouse-queries/SKILL.mdviews list. - If the fixture needs the view's source tables: check scripts/make_fixture.py
SLICED/FULL_COPY lists, rebuild via
nfl fixtureif so.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 24 lines · 30 tokens per session scan A dbef100275e8
new-view is a skill published in the GitHub repository parthakker/nfl-analytics (0 stars, last pushed 5d ago), licensed MIT. It adds 30 tokens to every session and 298 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
review-prs
Review a GitHub pull request in the googleapis/mcp-toolbox repo against the team's reviewer checklist: PR title/description conventions, linked issue, logic errors and unhandled edge cases, breaking changes, test coverage, docs updates, security (input handling), and new dependencies. Use whenever a maintainer asks…
fix-failing-tests
Diagnose a failing test in the googleapis/mcp-toolbox repo and land a fix by reasoning from the actual error: read the failure, reproduce it, shrink it until the cause is forced into the open, then fix the cause. Use this whenever a test or CI job is red, a build breaks after a change, many packages fail at once, or a…
azure-cosmos-db-py
Build Azure Cosmos DB NoSQL services with Python/FastAPI following production-grade patterns. Use when implementing database client setup with dual auth (DefaultAzureCredential + emulator), service layer classes with CRUD operations, partition key strategies, parameterized queries, or TDD patterns for Cosmos. Triggers…
hithink-finance-data
A local data-management skill for the HiThink Finance command-line tool and its DuckDB database. DuckDB is a database stored in a local file.
gh-issue
Size-audit, write, and split BanyanDB issues that somebody else or an automated TDD workflow can implement. Use whenever the user asks to file or revise an issue, decide whether an issue is too large, make an issue TDD-ready, turn a design into tickets, or split an umbrella into executable leaves. Do not draft or file…
Drizzle ORM Testing
Testing patterns for Drizzle ORM covering migration testing, query builder testing, transaction testing, and database integration testing with PostgreSQL, SQLite, and MySQL.