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Agent Skill · 研究检索

nanograph-lance(纳米喷枪)

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。

nanograph-lance 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 64,959。

总安装 291周安装 12GitHub Stars 1下载 95核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/nanograph-lance

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/nanograph/nanograph-skills --skill nanograph-l…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

nanograph-lance 怎么安装?

在终端执行 npx skills add https://github.com/nanograph/nanograph-skills --skill nanograph-lance。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

nanograph-lance 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

nanograph-lance 需要什么权限?

权限级别是需要联网。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。

nanograph-lance 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

nanograph-lance 用的人多吗?

公开数据是累计安装 291 次,近一周安装 12 次,下载 95 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

nanograph-lance 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
nanograph-lance

纳米喷枪 · 本页

2911需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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

需要联网

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

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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