JianFeeeee 67be114647 feat(skillmgr): skill_info 支持按章节读取 —— 长 SKILL 不再一次吞一万 token
## 问题

skill_info 此前**整份返回** SKILL.md。生产最坏一例:

    commondev-discipline v2.3.0 → 983 行 / 53810 字节 / ≈10600 token

模型为查「怎么开 PR」(§14,21 行)必须把 hmap 插件开发纪律(320 行)
和红牌一起读进上下文。一次误读一万 token,还挤掉了真正要用的历史。

拆 skill(一个插件声明多个)能解决"跨文档绑定",但解决不了
"单文档内部按需读" —— 本提交补的就是后者。

## 改法

skill_info(name, section=...) 只返回匹配的章节。不传 section 时:

  短文档(<=200 行)  直接返回全文(加目录只是噪音)
  长文档(>200 行)   返回「目录 + 开头 30 行」,模型据此决定读哪节

目录每行都标 `(N 行)` —— 行数不是装饰,它是模型在"一次吞一万 token"
和"先读目录"之间做选择的唯一依据。

## 三个实现要点

① **切分必须感知围栏代码块**

   SKILL.md 里有大量 shell 注释:

       ```bash
       # 期望 200;401/403 = 凭据无效
       curl -s ...
       ```

   硬按 `#` 切会把一条 curl 示例拆成两半 —— 模型拿到的是无法执行的
   碎片,比不给还糟。parseSections 跟踪 ``` / ~~~ 围栏,块内不切。

② **返回行号区间,不复制字符串**

   章节内容的唯一事实来源仍是文件本身,目录与正文用同一套边界计算,
   不会漂移成两份。

③ **点名顶层章节给整棵子树**

   问 `## Setup` 时要的是 §11–§16 全部,不是只有标题那一行。
   边界 = 下一个**同级或更浅**的标题。

## 判据(均经变异验证)

  TestSectionsRebuildEqualsOriginal    ★ 主判据
      所有区间拼接必须逐字节等于原文
      一次同时抓漏行 / 重行 / 错位三类缺陷
      变异:end 改 heads[i+1].line-1 ⇒ "原文 16 行,拼回 13 行"(实测红)

  TestSectionsShellCommentIsNotHeading
      变异:删掉 fence 判断 ⇒ "代码块内的 shell 注释被当成标题:
      \"期望 200;401/403 = 凭据无效\"(行 4)"(实测红)

  TestResolveSectionReturnsWholeSubtree
      变异:`<=` 改 `<` ⇒ "Setup 越界吃进了 Usage 的内容"(实测红)

  TestResolveSectionToleratesHashPrefix
      模型常把 `## Setup: git 操作` 整行传进来,必须容错

  TestRenderTOCListsLevels
      目录只列到 maxLevel,且必须带行数

## 判据本身最容易写废的地方(已避开)

① 用人造短文档测切分 —— 那种文档没有 shell 注释、没有嵌套层级、
   没有围栏代码块,测过了不代表能切真实文件。
   主判据**用生产那份 983 行的真实文件**。
② 只断言"切出 N 个章节" —— 数量对不代表边界对。

## 真实数据验证

    文档 983 行 → 切出 46 个章节(## 顶层 7 个)
    区间拼接 == 原文(逐字节)
    section="审批"    →  21 行(§16)
    section="Setup"   → 123 行(含 §11–§16 全部子节)
    section="hmap"    → 320 行

## 验证

    go build ./...            PASS
    go vet ./...              PASS
    gofmt -l                  干净
    go test ./... -count=1    63 包全过,0 FAIL
    干净检出(worktree)       build + skillmgr 全绿
2026-11-04 15:06:04 +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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