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nanograph-lance纳米喷枪

Agent Skill

nanograph-lance 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

291

周安装

12

GitHub Stars

1

下载量

95
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:nanograph-lance(纳米喷枪)
来源仓库:https://github.com/nanograph/nanograph-skills
仓库路径:skills/nanograph-lance
安装命令:
npx skills add https://github.com/nanograph/nanograph-skills --skill nanograph-lance
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/nanograph/nanograph-skills --skill nanograph-lance

简介

安全迁移纳米图像数据库从 Lance 存储格式 2.0 至 2.2。

  • 提供导出-重建-验证-交换全流程自动化指导。
  • 适用于数据集压缩优化与版本升级维护场景。
  • 需确认原始数据库完整性与新格式兼容性。
  • 建议在测试环境验证后再执行生产迁移。nanograph-lance 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Lance Storage Format Migration (v2 → v2.2)

Summary

Safely migrate a nanograph database from Lance storage format 2.0 to 2.2.

  • Export-rebuild-verify-swap workflow that never modifies the original database until verified
  • Format 2.2 provides better compression for text, enums, dates, and vector columns
  • Step-by-step checklist with rollback instructions at every stage

nanograph 1.x links against Lance v3, which writes new datasets in Lance storage format 2.2. Databases created with earlier nanograph versions use format 2.0. Both remain readable and operational in nanograph 1.x.

Note: "Lance v3" refers to the Lance SDK/library version that nanograph links against, while "format 2.0" and "format 2.2" refer to the on-disk storage format that the SDK reads and writes.

This skill guides you through a safe export-rebuild-verify-swap migration. The process creates a fresh database alongside the old one — the old database is never modified until the new one is fully verified.

Why upgrade to 2.2

Better compression — nanograph datasets are mostly text, enums, dates, graph relationship columns, and vector columns. Format 2.2 improves compression across all of these, resulting in less disk usage, cheaper backups, and less I/O during open, scan, compact, and cleanup operations.

Safe gradual adoption — nanograph 1.x can create new databases on 2.2, continue opening and operating existing 2.0 databases, and does not force an in-place file-format migration to upgrade the CLI. You can upgrade nanograph immediately and decide later whether to migrate existing databases.

When to use this

Run nanograph doctor --verbose and look at the storage format column. If any datasets show 2.0, migration is recommended. If all show 2.2, no migration is needed.

What the migration preserves and what it does not

Preserved: All node and edge records, schema, property values, and graph structure.

Not preserved:

  • CDC history — the transaction log (_tx_catalog.jsonl) and CDC event log (_cdc_log.jsonl) do not survive export/reimport. The new database starts with a fresh transaction history.
  • Dataset versions — Lance version history is reset. Time-travel to previous dataset versions is no longer possible.
  • Computed embeddings — vectors from @embed(...) fields are stripped during export and regenerated afterward. If using a real embedding provider (provider = "openai" or provider = "gemini"), this makes API calls.

Migration steps

Substitute <db> with the actual database name (e.g., omni, starwars, app). If the project has a nanograph.toml with db.default_path, the --db flags can be omitted for most commands.

Step 1: Pre-flight

Verify the current state and back up:

# Confirm migration is needed
nanograph doctor --verbose

# Record current row counts for later verification
nanograph describe --json > /tmp/pre-migration-describe.json

# Back up the entire database directory
cp -r <db>.nano <db>.nano.backup

Do not skip the backup. If anything goes wrong, cp -r <db>.nano.backup <db>.nano restores the original.

Step 2: Export

Export the full graph without embeddings. Embedding vectors are stripped because they will be regenerated cleanly in step 6.

nanograph export --db <db>.nano --format jsonl --no-embeddings > <db>-export.jsonl

Step 3: Validate the export

Compare node and edge counts against the live database:

grep -c '"type"' <db>-export.jsonl    # node count
grep -c '"edge"' <db>-export.jsonl    # edge count
nanograph describe --db <db>.nano     # compare

If the schema has been updated since the last load, some exported records may contain values that are invalid under the current schema (e.g., old enum values). Check the export against the schema and fix any mismatches with targeted jq or sed before loading. Document what was fixed.

Step 4: Create a new database

Initialize from the current schema. Use a temporary name — never overwrite the original until verification is complete.

nanograph init --db <db>-v2.nano --schema <schema_path>

Use the schema from nanograph.toml's schema.default_path, or from <db>.nano/schema.pg if the project doesn't keep a separate schema file.

Step 5: Load the exported data

nanograph load --db <db>-v2.nano --data <db>-export.jsonl --mode overwrite

If the load fails with schema validation errors, fix the export (step 3) and retry.

Step 6: Regenerate embeddings

If the schema has any @embed(...) properties, regenerate vectors:

nanograph embed --db <db>-v2.nano

This calls the embedding provider configured in nanograph.toml or .env.nano. With provider = "mock", this is instant. With provider = "openai" or provider = "gemini", this makes API calls proportional to data volume. Scope to a single type with --type <NodeType> if needed.

Step 7: Verify the new database

This is the most important step. Do not skip any check.

# Confirm storage format is 2.2
nanograph doctor --db <db>-v2.nano --verbose

# Compare row counts against pre-migration snapshot
nanograph describe --db <db>-v2.nano --json > /tmp/post-migration-describe.json

# Run integrity check
nanograph doctor --db <db>-v2.nano

Compare the row counts from /tmp/pre-migration-describe.json and /tmp/post-migration-describe.json. Every node and edge type should have the same count.

Then run smoke queries — especially any that touch search, traversal, and aggregation:

nanograph run search "test query" --db <db>-v2.nano
nanograph run --db <db>-v2.nano --query queries.gq --name <query> --param key=value

If any check fails, stop. The old database is untouched — investigate the failure before proceeding.

Step 8: Swap databases

Only after all verification passes:

mv <db>.nano <db>.nano.old
mv <db>-v2.nano <db>.nano

If nanograph.toml uses db.default_path, no config change is needed — the path has not changed.

Step 9: Final verification

nanograph doctor --verbose
nanograph describe

Confirm everything works with the swapped database. Run a few queries.

Step 10: Clean up

Once confident the migration succeeded:

rm -rf <db>.nano.backup
rm -rf <db>.nano.old
rm <db>-export.jsonl
rm /tmp/pre-migration-describe.json /tmp/post-migration-describe.json

Keep the backup for at least a day before deleting. If the migration happened in a git-tracked project and the database was committed, you can always recover from git history.

Rollback

Before step 8 (swap): the old database is still in place. Clean up the failed attempt:

rm -rf <db>-v2.nano
rm <db>-export.jsonl

After step 8 (swap): restore from backup:

rm -rf <db>.nano
mv <db>.nano.old <db>.nano

Checklist

  • doctor --verbose confirms current storage format (2.0 or 2.2)
  • Backup created (<db>.nano.backup)
  • Pre-migration row counts saved
  • Export completed with --no-embeddings
  • Export counts match live database
  • New database initialized from current schema
  • Data loaded into new database
  • Embeddings regenerated (if applicable)
  • New database shows 2.2 in doctor --verbose
  • Row counts match between old and new
  • doctor passes on new database
  • Smoke queries return expected results
  • Databases swapped
  • Final doctor and describe pass
  • Backup and old database cleaned up

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.47%
按下载量换算34

Claude

27.93%
按下载量换算27

Cursor

19.1%
按下载量换算18

Gemini CLI

8.78%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

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该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

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