JianFeeeee 1b6cd57dca fix(skillmgr): 内核旧版 skill 永不更新 —— 无 stamp 不等于"用户改过"
## 缺陷(实测,非推测)

给 kernel-plugins 补上 `skill_info(name, section=...)` 的用法说明后,
改了嵌入内容,但**生产上永远不会更新**:

    /home/hologramagent/skills/kernel-plugins/   mtime 2026-10-26,无 .builtin-sha256
    嵌入版本 1.1.0   vs   磁盘版本 1.0.0

`materializeBuiltinSkills` 的判断链是:

    builtinSkillUnmodified(dir)  → 读 .builtin-sha256
                                  没有 stamp ⇒ false
    ⇒ continue("用户改过或无法确认,保留人的版本")

而 stamp 机制是 387ae94(今天早些时候)引入的。生产机上这份 skill
是**更早的内核**写的 —— 它没有 stamp,于是被永久判为"来历不明"。

后果不只是这一份文档:任何在 stamp 机制之前部署的机器,其内核自带
skill 都停在旧版。而 kernel-* 系列正是教模型怎么用工具/记忆/终端的
文档 —— 内核改进了文档,模型永远看不到。

## 修法:用 frontmatter version 区分两种"没有 stamp"

原判据把两件事混成一件:

    用户改过措辞        ⇒ 必须保留
    内核在 stamp 之前写的 ⇒ 应该升级

新增 builtinSkillOutdated(dir, embedded):只在**没有 stamp** 时被调用,
判据是磁盘 frontmatter 的 `version` 严格低于内嵌版本。

安全性分析(写在代码注释里):
    人改措辞         通常不动 version ⇒ 不低于 ⇒ 保留 ✓
    人把 version 改高                        ⇒ 保留 ✓
    内核旧版(无 stamp)                      ⇒ 升级 ✓(这正是要修的)
    解析不出版本号                            ⇒ 返回 false ⇒ 保留(保守)
误升级的唯一条件是"人把 version 改低",而那本身就意味着放弃内核版本。

原有的四条保守判据(含 NoStampIsConservative)全部保持绿。

## 判据抓到了我自己写的 bug

TestCompareVersion 立刻红了:

    compareVersion("1.0","1.0.0")=-1,期望 0

原因:缺失段留成空串 ⇒ Atoi("") 失败 ⇒ 落到字符串比较 ⇒ `"" < "0"`
⇒ 版本号少写一段的 skill 会被无谓地重复升级。改为缺失段视为 "0"。

## 变异验证

  builtinSkillOutdated 恒 return false(= 修复前行为)
      ⇒ TestMaterializeBuiltinSkills_UpgradesStaleKernelVersion 红:
        "★ 没有 stamp 的内核旧版(version 0.0.1)没被升级"

  compareVersion 缺失段改回空串
      ⇒ TestCompareVersion 红:"compareVersion(\"1.0\",\"1.0.0\")=-1,期望 0"

## 顺带:kernel-plugins v1.1.0

补一节「Reading a skill without burning context」,说明:
  - 长文档返回目录 + 开头 30 行,目录每行带 (N 行)
  - `section` 可按编号或标题子串取,大小写不敏感,容忍 `##` 前缀
  - 顶层章节返回整棵子树
  - 未命中会列出全部可用章节
  - 章节边界尊重围栏代码块

## 验证

    go build ./...            PASS
    go vet ./...              PASS
    gofmt -l                  干净
    go test ./... -count=1    62 包全过,0 FAIL
    干净检出(worktree)       build + skillmgr 全绿

(包数 63 → 62 是因 clawhubadapter 已在 a0077a2 迁出为外部插件,与本改动无关)
2026-11-04 15:50:39 +08:00

⚠️ AI-Assisted Programming Notice: Parts of this project's code, documentation, and commit history were generated or modified with AI assistance. Key changes have been human-reviewed, but please evaluate and verify before use.

HologramAgent

中文: README.md

Self-hosted agent kernel. Runs on your own hardware; plugins attach as separate processes.

make build build-cli
./build/homed -data /tmp/ha

API keys go in the web UI at http://localhost:8080. Requires Linux.

The main difference from typical agent frameworks is context handling: instead of truncating on overflow or asking the model to summarize, it scores history by relevance and sinks the low-scoring events into cold context, then distills those into a graph. Two other things differ: startup order is resolved at build time, and plugins launch as soon as their facilities are ready.

Architecture

                    homed (kernel)
        LLM orchestration · memory · retrieval
                       │
        ┌──────────────┼──────────────┐
        │              │              │
   stdio RPC       shared mem     event ring
     control       bulk data      notify
        │              │              │
        └──────────────┼──────────────┘
                       │
              plugins (separate processes)
   web UI · QQ · CLI · files · browser

The kernel process handles orchestration, memory and retrieval; HTTP, files and devices belong to plugins (internal/plugin). A crashed plugin is restarted with backoff 1s / 2s / 3s; the 4th crash within 5 minutes stops automatic restarting pending human intervention (cmd/homed/kmod_runtime.go).

Context window

internal/agent/core/context.go

Each event gets two vectors on entry: a dense vector sharing one space with media and cold context, and a TF-IDF word vector as fallback. Pruning scores history against the current input and keeps topK, with the most recent N events exempt from scoring.

N defaults to 10, bounded by budget: min(configured, ContextTokens / avg tokens per event).

That bound came from a measurement. At a 50k window with 5800 tokens per event and a 40000 budget, pinning 10 events needed 58k, more than the budget itself. Every prune met its target without freeing space; the budget was gone after two rounds and the task terminated, with recall falling from 44% to 22%. Small windows now tighten automatically; a 1M window uses the configured value.

Pruned events move to the cold-context layer together with the media blocks they hold. Block IDs stay the same; only the holding layer changes. Retrieval uses coldcontext_query: Peek to confirm, then Remove the source copy. There is no fetch-and-delete path, since deletion follows confirmation.

Three layers

internal/agent/core/context.go · internal/memory/coldcontext/ · internal/memory/graph.go

Layer Holds Reaches the model Cooling
L1 Context events + blocks directly, in the system prompt low relevance sinks
L2 Cold Context events + blocks (same shape as L1) inserted back into L1 order on recall ≤2 accesses and 72h idle, then distilled
L3 Stable triples, nodes are blocks on recall never leaves

A block has exactly one holder at any layer, with no reference counting. Images and audio are blocks in L2/L3 with their own vectors, searched in the same space as text.

Scenes

internal/memory/scene_emerge.go

A scene is built from five weighted features: channel 1.0, conversation partner 1.0, tools 0.8, topic 0.4, time of day 0.2. A scene is created on the second occurrence of the same fingerprint (minSceneEvidence = 2); one-off interactions don't create one. Join threshold 0.5, recall 0.35. Recurrence adds 1 to strength; scenes that stop recurring decay by half-life.

Two entry points run in parallel: a plugin declares the scene at an injection point (e.g. chan:qq/peer:group_1), or fingerprints cluster on their own. A declared scene derives features only from its own key and does not absorb the full fingerprint. Absorbing it would make the declared scene match nearly every later turn of that kind at close to 1.0 similarity, and the clustering path could never grow narrower scenes.

Seven stages

internal/agent/core/stages.go

Stage When Plugins can
on_input message arrives blocklist, rate limit, short-circuit reply
pre_action context ready, before LLM inject external data
post_action LLM returns text and tool calls filter, force redirect
before_toolcall before one tool runs audit, deny, rewrite args
after_toolcall after one tool runs redact, sort results
before_output final text ready format adaptation
after_output after sending statistics

The inner loop is post_action → [before_toolcall → run → after_toolcall] → post_action. It exits when the model stops calling tools, all calls are denied, or the limit is reached.

Everything else

  • Channels: web UI, CLI, QQ groups and DMs, A2A, ACP in your IDE
  • Tools: files, shell, code, browser (real Chrome), calendar, notes, RSS, music
  • Modalities: images, video, voice, screenshots, web pages

28 built-in plugins, 89 model-callable tools. External plugins live one per repo under github.com/HologramAgent.

Downloads

Releases ship three variants:

Variant Contents For
full homed + waiter + desktop GUI + systemd unit Single-machine, everything
server homed + waiter + systemd unit Servers (no desktop environment)
client waiter + desktop GUI Connecting to a remote HologramAgent
  • Linux: .deb (amd64/arm64), .rpm (x86_64), .tar.gz
  • Windows: HologramAgent_v1.2.0_{Full,Server,Client}_win64.exe (NSIS installer, includes the AGPL license page). Since v1.2.0 homed no longer supports native Windows (it relies on fd inheritance and in-segment offset dereferencing), so the installer bootstraps WSL2 and installs the Linux packages inside the distribution the same way a Linux host would.
  • Portable: homeagent-bin-<os>_<arch>.tar.gz (homed/waiter/initconfig)
  • Verification: SHA256SUMS

The macOS homed requires a native macOS build (CGO + sqlite3), so release packages ship only waiter/initconfig.

Build

make build build-cli    # Build daemon + CLI
make test               # go test ./...
make install            # Install to system

Dependencies: Go 1.25+, CGo (go-sqlite3), Linux.

homed requires Linux (it relies on fd inheritance and intra-segment offset dereferencing of the shared memory region; see cmd/homed/platform_windows.go). On Windows only waiter.exe is built and homed runs under WSL2 (see Downloads). macOS can build waiter/initconfig; homed must be built on native macOS.

License

AGPL-3.0-only. See LICENSE.

Bundled third-party components keep their own licenses; full texts ship in /usr/share/doc/hologramagent/licenses/.

Third-party components shipped with the packages

Component License Location
Chinese-CLIP ViT-B/16 (ONNX artifacts) Apache-2.0 /usr/lib/hologram/models/chinese-clip-vit-b16-onnx/
ONNX Runtime (libonnxruntime.so) MIT /usr/lib/hologram/onnxruntime/
jieba dictionary (embedded in the binary) MIT internal/memory/jiebadict/
Go dependencies (go-sqlite3, gojieba, bubbletea, …) MIT / BSD-3 / Apache-2.0 permissive, AGPL-3.0-compatible

The package metadata declares this package as AGPL-3.0-only.

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JavaScript 9.1%
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HTML 1.8%
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