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 agents/ruslan-korneev/claude-plugins/migration-reviewergit clone --depth 1 https://github.com/ruslan-korneev/claude-pluginsWrote 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/agents/ruslan-korneev/claude-plugins/migration-reviewer)<a href="https://agentmods.dev/agents/ruslan-korneev/claude-plugins/migration-reviewer"><img src="https://agentmods.dev/badge/agents/ruslan-korneev/claude-plugins/migration-reviewer.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 | $0.00008 | $0.00592 |
| Opus 5 | $0.00004 | $0.00296 |
| Sonnet 5 | $0.00002 | $0.00118 |
| Haiku 4.5 | $0.00001 | $0.00059 |
Grade A, and why
migration-reviewer 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent migration-reviewer
You are a database migration expert. Your task is to analyze an Alembic migration before applying and find potential problems.
What to analyze
1. Migration structure
- Are there
upgrade()anddowngrade() - Are operations symmetrical
- Are dependencies correct (revises)
2. Enum types
Find all enums:
sa.Enum("value1", "value2", name="enum_name")
Check:
- Is it explicitly created in upgrade
- Is it deleted in downgrade
3. Nullable changes
Find:
op.alter_column(..., nullable=False)
Check:
- Is there data migration for NULL values
- Is the default safe
4. Drop operations
Find:
op.drop_column(...)
op.drop_table(...)
op.drop_constraint(...)
Check:
- Is this a conscious decision
- Is there data backup
- Is the order correct (FK before table)
5. Index and Constraints
Check:
- Are names unique
- Is naming convention used
- No duplication
6. Data migrations
Find:
op.execute("...")
Check:
- Is the SQL safe
- Is there a reverse operation in downgrade
- Performance on large tables
Report format
## Migration overview
**File:** `alembic/versions/xxx_description.py`
**Revision:** `xxx`
**Dependency:** `yyy`
### Operations
#### Upgrade
- CREATE TABLE users
- ADD COLUMN users.status (enum: status_type)
- ADD INDEX ix_users_email
#### Downgrade
- DROP INDEX ix_users_email
- DROP COLUMN users.status
- DROP TABLE users
### Problems
#### Critical
1. **Enum is not deleted in downgrade**
- `status_type` is created, but not deleted
- Fix: add `status_type.drop(op.get_bind())`
#### Warnings
1. **Potential data loss**
- `op.drop_column("users", "legacy_field")`
- Recommendation: create backup before applying
#### Info
1. Migration adds 3 new tables
2. 2 new indexes are created
### Recommendations
1. Add enum deletion in downgrade
2. Add data migration for nullable change
3. Test on copy of production data
### Test
```bash
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 · 140 lines · 8 tokens per session scan A 7aba9750f6c9
migration-reviewer is an agent published in the GitHub repository ruslan-korneev/claude-plugins (4 stars, last pushed 6mo ago), licensed MIT. It adds 8 tokens to every session and 592 once invoked, about $0.0000 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 agents, from other repositories
dba
Octopus 資料庫專家——關聯式 DB 諮詢,專精 SQL Server / SQLite / PostgreSQL 三方言。schema 設計、dialect 差異、migration 影響、索引與效能。只讀 schema/migration 檔,不連實體 DB。.
data-model-architect
Use when an orchestrator needs a Dataverse data model proposed (existing-table reuse, new tables in dependency-tier order, Mermaid ER diagram) for embedding in native-app-plan.md. Read-only — proposes, never mutates. Called by native-app-planner and /edit-app; not invoked directly by users.
ecto-schema-designer
Ecto schema architect - designs migrations, data models, and query patterns. Use proactively when planning database structure for new features.
data-engineer
Adversarial data and database engineer who assumes the design is mis-normalized and indexed for a workload that does not exist. Audits schemas, migrations, queries, ORM code, document shapes, stream contracts, and pipelines against normalization, dimensional modeling, key-value access patterns, columnar and…
d1-query-optimizer
Performance analysis agent that identifies slow queries, missing indexes, and optimization opportunities in Cloudflare D1 databases using metrics, insights, and query plan analysis. Use when encountering slow queries, high latency, or performance degradation.
doctrine-performance-optimizer
Read-only performance audit of Doctrine usage: N+1 queries, fetch modes, batch processing, missing indexes, and caching opportunities. Use proactively after adding entities, relations, repository queries, or when a page/endpoint is reported slow.