Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nonlinear-xyz/factory-kit/factory-db-migrationnpx skills add nonlinear-xyz/factory-kit --skill factory-db-migrationgit clone --depth 1 https://github.com/nonlinear-xyz/factory-kitWrote 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/nonlinear-xyz/factory-kit/factory-db-migration)<a href="https://agentmods.dev/skills/nonlinear-xyz/factory-kit/factory-db-migration"><img src="https://agentmods.dev/badge/skills/nonlinear-xyz/factory-kit/factory-db-migration.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 | $0.00155 | $0.05253 |
| Opus 5 | $0.00077 | $0.02627 |
| Sonnet 5 | $0.00031 | $0.01051 |
| Haiku 4.5 | $0.00015 | $0.00525 |
Grade A, and why
factory-db-migration 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.
How it starts
The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Factory DB migration
This skill is the runbook discipline for making a destructive database change. It is invoked before the first time a migration, backfill, or seed touches prod — and re-invoked on every periodic re-import.
The factory ships factory-data-layer.md for how schemas are designed and factory-deployment.md for where migrations execute in CI. This skill is the third leg: how to run the change safely once both upstream pieces are in place. Read all three before proposing a non-trivial prod write.
How to use this skill
- Before any first-of-its-kind destructive prod write — read the Principle of each section. If you can't articulate the principle in your own words, you don't yet understand the change well enough to ship it.
- During design review — use the section list as a checklist: do we have preflight? Idempotency proof? Rollback? Pre-write snapshot? Human gates?
- During incident response —
factory-pitfalls.mdindexes the failure modes here; jump to the one that matches.
The data is ground truth — the schema accommodates it
Principle. When a constraint rejects historical data, the constraint encoded a wrong belief. The data records what actually happened; the schema is an artifact of someone's understanding of what they thought happened.
Why. Constraints exist to prevent invalid future states. They cannot retroactively make the past invalid. When a constraint added in 2026-05 rejects data from 2024, the meaningful question is "what did 2024 actually look like, and was the constraint added with full domain knowledge?" — not "how do we mangle the 2024 data to fit?" Silently coercing historical data into the new shape destroys information about what the business actually did. That's a category-of-bug worse than the missing constraint.
The pattern: a periodic import surfaces a constraint violation. Before deciding it's a data error, ask: (1) what did the constraint encode? (2) who added it? (3) did they know about the historical reality? If the answer to (3) is no, the constraint is the bug.
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 · 228 lines · 155 tokens per session scan A c5501bc1b617
factory-db-migration is a skill published in the GitHub repository nonlinear-xyz/factory-kit (9 stars, last pushed 1mo ago), licensed MIT. It adds 155 tokens to every session and 5,253 once invoked, about $0.0008 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
databases
· Configure/tune/migrate PostgreSQL, MongoDB, MySQL/MariaDB, MSSQL. Triggers: 'database', 'postgres', 'mysql', 'mongodb', 'database schema', 'database migration', 'pgbouncer', 'EXPLAIN'. Not for HTTP APIs (use backend-api).
audit-data-integrity
Use when auditing and fixing end-to-end data integrity in a scope — invariant violations, partial or conflicting writes, unsafe migrations/backfills, duplicate or reordered effects, precision or time corruption, stale derived stores, broken retention/deletion, and unrecoverable drift. Triggers on "audit data…
schema-evolve
This skill should be used when the user asks about 'schema drift', 'schema evolution', 'evolve schema', 'schema sync', 'sync schemas', 'update schema fields', 'schema field frequency', 'missing schema fields', 'unused schema fields', 'schema proposal', 'schema cardinality', 'check schema', 'schema audit', 'schema…
lark-base
飞书多维表格（Base）操作：建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、workflow、角色权限；遇到 Base/多维表格/bitable 或 /base/ 链接时使用。文件导入/导出转 lark-drive，认证/授权转 lark-shared。.
ecto-patterns
Ecto patterns — schemas, changesets, queries, migrations, Multi, associations, preloads, upserts. Use when editing Repo calls, Ecto.Query, or schema fields. Skip for Ash.
moai-platform-database-cloud
Cloud database platform specialist covering Neon (serverless PostgreSQL), Supabase (PostgreSQL 16 with real-time), and Firebase Firestore (NoSQL with offline sync). Use when choosing or setting up cloud databases.