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 skills add firefly-hefeng/VESTI-SKILLS --skill vesti-memorygit clone --depth 1 https://github.com/firefly-hefeng/VESTI-SKILLSWrote 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/firefly-hefeng/vesti-skills/vesti-memory)<a href="https://agentmods.dev/skills/firefly-hefeng/vesti-skills/vesti-memory"><img src="https://agentmods.dev/badge/skills/firefly-hefeng/vesti-skills/vesti-memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/firefly-hefeng/vesti-skills/vesti-memory"><img src="https://agentmods.dev/badge/skills/firefly-hefeng/vesti-skills/vesti-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00089 | $0.01316 |
| Opus 5 | $0.00044 | $0.00658 |
| Sonnet 5 | $0.00018 | $0.00263 |
| Haiku 4.5 | $0.00009 | $0.00132 |
Grade A, and why
vesti-memory 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 11d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
VESTI Memory — 项目上下文、文件定位与渐进检索
VESTI 在本机持续采集你与各 AI coding agent 的历史会话（含工具调用、子代理、文件改动），并以只读 MCP server 暴露检索接口。
会话开始：先拉项目上下文
处于已跟踪项目时，先调一次 vesti_get_project_context 再动手，不要向用户重复索要背景：
- vesti_get_project_context(paths?) — 项目"自动上下文包"。每个路径返回：L0 状态卡（一句话/活跃文件/未决问题）+ L2 维护简报 + 最近会话列表（标题/时间/一句话）+ 合并的未决问题 + 确定性活跃文件时间线。
paths[0]传你的当前工作目录；不传时默认最近活跃的项目。- 合并/跨项目任务：一次传入多个项目路径，响应附带
cross_project——共享文件、共享主题、会话时间交叠，直接支撑"基于几个分支开合并项目"。 - 路径出现在
unmatched_paths= 该项目未被 VESTI 跟踪（hints 里附已知项目列表），此后按无记忆处理，不要再反复重试。 - 拿到上下文包后仍有缺口，再走下面的三层检索。
需要定位文件时：先查文件级记忆
- 用户问“上次 OAuth 改在哪个文件”“填写之前做过的 BP”或需要把历史工作落到当前代码时，先调用
vesti_search_files(query, topK=10)。 - 把返回的
path、projects、last_touched和 backing sessions 当作历史证据；先确认项目范围，再用当前 Agent 的文件系统工具检查路径是否仍存在并读取最新内容。 - 该工具搜索的是 VESTI 记录的历史文件触碰，不搜索当前磁盘内容。用户问“当前目录里有没有某文件”时，直接使用文件系统搜索。
- 工具列表中没有
vesti_search_files时，说明 MCP 版本较旧；退回vesti_search(文件名/主题)→vesti_timeline→vesti_get_turns，并把推断出的路径标为待核实。
需要历史细节时：三层渐进披露
按以下顺序使用（每层便宜一个数量级），不要跳过层级直接拉全文：
- vesti_search(query, topK=8) — 会话级索引。返回 session_id、标题、平台、项目路径、时间、digest 一句话、关键主题、命中片段与
confidence。- 查询用名词/文件名/决策关键词，中英文皆可（底层 trigram FTS）。
confidence:"low"的结果只作线索，不要当作事实；换关键词重试或明确告诉用户"记忆中没有找到"。
- vesti_timeline(session_id, around_turn?) — 单会话 turn 大纲（序号/时间/用户意图一行/工具数/token）。用它定位需要哪几轮，不要盲取。
- vesti_get_turns(session_id, turn_ids | range, max_chars=8000) — 取指定轮完整内容（用户/助手原文 + 工具调用摘要）。只在确定范围后调用；
truncated:true时按 seq 继续取。
只知道项目名而不知道路径时使用 vesti_project_brief(project)；知道路径时优先使用 vesti_get_project_context。
交接前
- vesti_get_handoff_context(path | session_id, user_messages=8) — 轻量交接材料，与 relay v2 对齐：项目上下文块 + 最近 N 条用户消息 + 文件锚点（确定性活跃文件时间线）+ verify_first 种子（待确认的未决问题、上次失败的步骤复查）。全部来自存储数据，无编造。
- 拿到材料后按 vesti-handoff skill 的 V2 schema 组装交接包（goal/state/decisions 由你归纳，清单类只用锚点数据）；长 transcript 重压缩交给 VESTI app 侧 relay 管线，不在 MCP 做。
工作守则
- 续作场景：
vesti_get_project_context(当前目录)→ 文件问题用vesti_search_files，决策问题用vesti_search→ 按需vesti_timeline/vesti_get_turns。引用记忆内容时给出来源（会话标题/时间），让用户可回查。 - 时效性：记忆条目带时间；同一事实新旧冲突时以更近的会话为准，并提醒用户发生过变更。
- 边界：库是只读的；查不到就如实说查不到，不要编造"记忆中"的内容。
- 未注册 MCP 时提示用户按 README 注册（stdio，Node ≥23.4，或 22.x 加
--experimental-sqlite；也可在 VESTI app 设置页"连接到 Agent"一键注册）。
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.
- 11d ago First seen · 50 lines · 89 tokens per session scan A 001fe3bd0c80
vesti-memory is a skill published in the GitHub repository firefly-hefeng/VESTI-SKILLS (177 stars, last pushed yesterday), licensed MIT. It adds 89 tokens to every session and 1,316 once invoked, about $0.0004 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 2026-08-30.
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