## 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
⚠️ 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 v1.2.0homedno 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.
homedrequires Linux (it relies on fd inheritance and intra-segment offset dereferencing of the shared memory region; seecmd/homed/platform_windows.go). On Windows onlywaiter.exeis built andhomedruns under WSL2 (see Downloads). macOS can buildwaiter/initconfig;homedmust 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.