JianFeeeee a8ce5ece30 feat(agentcli): 补 skill 文档 —— 终端 vs 一次性命令的边界
## 为什么补这个

真实生产事故:agent 把 terminal_create 当成一次性命令执行器用。

    terminal_create(command='cd /usr/lib/node_modules/@deepseek-ai && echo "=== 已安装的 dsh 包 ===" && ls -d */ | head -60 && …')
    → read /dev/ptmx: input/output error
    → 再开 term_7、term_8、term_9 …

三层成因:

  ① cmd 插件 auto_start=false ⇒ 工具表里没有 cmd_run,需先 plugin_start cmd
  ② terminal_create 的 command 参数描述写着"要执行的命令",语义上像"跑一条命令"
  ③ 没有文档说清"一次性命令该用 cmd_run、终端是给要持续输入的程序用的"

①③ 已分别修掉(cmd-tools skill + 本文件)。② 是设计上的引力:
参数名叫 command、描述说"要执行的命令",模型自然会塞复合命令进去。
文档要正面抵消。

## 实现方式

按 seq 的既有模式:内置插件实现 plugin.SkillProvider,
由内核统一写盘/幂等/事件/停用清理(internal/plugin/builtin_skill.go)。

  · 新增 internal/plugins/agentcli/skill.go
  · BuiltinSkills() 交出正文,生命周期与插件同生共死
    (禁用插件后 skill 还留着 = 模型看得到说明、点进去工具却不存在)

## 文档的核心:可操作判据,不是抽象词

一句判据:**跑完就结束 → cmd_run。要一直活着、要看屏幕、要按键 → terminal。**

关键的三条(缺任何一条模型都会退回旧行为):

  · 点明 cmd 插件要 plugin_start(否则模型找不到 cmd_run 就用终端)
  · 点明复合命令(cd && echo && ls)属于一次性,不是"所以要用终端"
  · 解释 /dev/ptmx 那个真实报错的含义(命令跑完终端已退出),
    否则模型会以为终端坏了

索引行(模型每轮唯一能看到的一行)也带边界:
"交互式终端(vim/ssh/top/长驻进程)。一次性的 shell 命令不要用终端,用 cmd_run。"

## 判据(均经变异验证)

  TestSkillProviderContract               编译期断言 SkillProvider + 正文非空
  TestSkillSeparatesTerminalFromOneShot   ★ 六条必备内容缺一即红
  TestSkillIndexLineStatesBoundary        索引行必须指出替代方案
  TestSkillBodyFirstParagraphStandalone   正文首段(真正注入提示词的那段)能独立看懂

变异验证(实测):把整份文档换成"终端可以执行各种命令"的弱化版本
⇒ 3 条同时红。

★ 第一版变异是"只改一句话",判据没红 —— 因为 cmd_run 在文中另有 13 处。
  那次变异无效,不算验证。改成整份替换后才是真验证。

## 端到端验证(独立实例 + 假 LLM 捕获真实请求)

  [plugin] agentcli: installed skill "agentcli-terminal" -> .../skills/agentcli-terminal
  [clawhubadapter] skill agentcli-terminal detected, handed off to skillmgr

实际注入到 system prompt 的 [Skills] 段:

    agentcli-terminal v1.0.0 - 终端给**要持续输入**的程序用(vim、ssh、top、REPL、
    要按键确认的安装器);一次性的 shell 命令用 **cmd_run**(cmd 插件,需…

## 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(git worktree)     build + 判据全绿
  独立实例实测                 真机装盘 + 真机注入提示词(见上)
2026-11-04 13:22:07 +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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