cargo-context

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Cargo CLI — Context

The context is a git-backed repository of typed markdown/MDX files that captures a workspace's GTM knowledge (company narrative, ICPs, personas, plays, proof, objections, etc.) and is read/written by both humans and agents. The

cargo-ai context

domain has two subdomains you'll use:

runtime — browse, read, write, edit, and execute against the workspace's runtime sandbox (a checked-out copy of the context repo).
```
write
```
/
```
edit
```
are pushed to the default branch;
```
execute
```
runs are not pushed.
graph — build/load the knowledge graph derived from every markdown/MDX file in the context repo.

The canonical example of a context repository is
getcargohq/cargo-workspaces
. Read its
README.md
to understand the domain layout and file conventions before writing new entries. For uploading runtime-independent files (CSVs, PDFs) used in batch runs, use
cargo-workspace-management
(
cargo-ai workspaceManagement file upload
) instead. For RAG file attachments to agents, use
cargo-ai
(
cargo-ai content file upload
).

See
references/conventions.md
for the full context repo structure and per-domain templates. See
references/response-shapes.md
for the JSON shapes returned by each
cargo-ai context
command. See
references/troubleshooting.md
for common errors and how to fix them. See
references/examples/authoring.md
for end-to-end add / edit / delete recipes. See
references/examples/lifecycle.md
for the bootstrap + refresh-from-calls playbook. See
references/examples/graph-queries.md
for inspecting the knowledge graph.

Context 是一个由git托管的仓库，存储类型化的markdown/MDX文件，用于记录工作区的GTM知识（公司叙事、理想客户画像ICPs、用户角色、策略方案、案例证明、异议处理等），支持人类和Agent共同读写。

cargo-ai context

包含两个你会用到的子模块：

runtime — 浏览、读取、写入、编辑工作区的运行时沙箱（即context仓库的检出副本），并在其中执行命令。
```
write
```
/
```
edit
```
操作会推送到默认分支；
```
execute
```
执行的操作不会推送。
graph — 构建/加载从context仓库中所有markdown/MDX文件衍生而来的知识图谱。

context仓库的标准示例为
getcargohq/cargo-workspaces
。在编写新条目之前，请阅读其
README.md
以了解领域布局和文件规范。若要上传批量运行中使用的独立于运行时的文件（如CSV、PDF），请使用
cargo-workspace-management
（
cargo-ai workspaceManagement file upload
）。若要为Agent上传RAG附件文件，请使用
cargo-ai
（
cargo-ai content file upload
）。

完整的context仓库结构和各领域模板请查看
references/conventions.md
。每个
cargo-ai context
命令返回的JSON格式请查看
references/response-shapes.md
。常见错误及修复方法请查看
references/troubleshooting.md
。端到端的添加/编辑/删除操作示例请查看
references/examples/authoring.md
。初始化+从通话记录刷新的操作指南请查看
references/examples/lifecycle.md
。知识图谱查询示例请查看
references/examples/graph-queries.md
。

Prerequisites

前置条件

See

../cargo/references/prerequisites.md

for install, login (

--oauth

--token

), JSON output conventions, and error shapes. Verify the session with

cargo-ai whoami

before running any of the commands below —

runtime write

and

runtime edit

push commits to the workspace's context repo, so confirming

workspace.name

first is non-negotiable.

安装、登录（

--oauth

--token

）、JSON输出规范及错误格式请查看

../cargo/references/prerequisites.md

。在运行以下任何命令之前，请使用

cargo-ai whoami

验证会话 —

runtime write

和

runtime edit

会向工作区的context仓库推送提交，因此必须先确认

workspace.name

。

Discover the context first

先了解当前context内容

Before editing anything, see what's in the context repo:

bash

cargo-ai context runtime browse                 # list entries at the runtime sandbox root
cargo-ai context graph get                      # full knowledge graph derived from the repo's md/mdx files

在编辑任何内容之前，请先查看context仓库中的现有内容：

bash

cargo-ai context runtime browse                 # 列出运行时沙箱根目录下的条目
cargo-ai context graph get                      # 获取从仓库md/mdx文件衍生的完整知识图谱

Quick reference

快速参考

bash

undefined

bash

undefined

Runtime sandbox (checked-out copy of the context repo)

运行时沙箱（context仓库的检出副本）

cargo-ai context runtime browse [--path <path>] cargo-ai context runtime read --path <path> [--start-line <n>] [--end-line <n>] cargo-ai context runtime write --path <path> --content <content> [--commit-message <message>] cargo-ai context runtime edit --path <path> --old-string <old> --new-string <new> [--commit-message <message>] cargo-ai context runtime execute --command <command> [--args <json>]

Knowledge graph

知识图谱

cargo-ai context graph get

undefined

cargo-ai context graph get

undefined

Runtime sandbox

运行时沙箱

The runtime sandbox is a checked-out, executable copy of the context repository. It's the surface you use to read and modify context files, and to run commands against them.

Two important behaviors to remember:

write
and
edit
push to the default branch of the context repo. They are not local-only.
execute
does not push. Changes made to files by a shell command run via
```
execute
```
stay in the sandbox and are discarded — use
```
execute
```
for builds, tests, or inspection, not for committing edits.

Uploaded content files are available read-only under
.files/
. The workspace's

content file

uploads (PDFs, CSVs, text — see

cargo-content

) appear in the sandbox under a

.files/

directory, so a command run via

execute

(or

read

browse

) can consume them — e.g.

cargo-ai context runtime execute --command ls --args '["-1",".files"]'

. It sits outside the committed context tree: the sandbox's auto-commit skips it, so nothing under

.files/

is ever pushed to the context repo, and you can't add or change content files from here (use

cargo-ai content file …

instead).

Because writes push immediately, confirm the target workspace before the first
write
/
edit
:

bash

cargo-ai whoami   # → workspace.uuid, workspace.name

Read the workspace name back to the user. If the session is for a specific client, make sure

workspace.name

matches before authoring anything — there is no dry-run mode. If

workspace.name

is generic or ambiguous (e.g. "Main", "Test", a person's name, an internal codename), don't guess — ask the user for the company name and canonical domain (

example.com

) and confirm both before the first write. If you logged in without pinning a workspace, re-run

cargo-ai login --oauth --workspace-uuid <uuid>

(or

--token <workspace-scoped-token>

for non-interactive use).

Edits derived from sales-call analysis should be applied one at a time with human review, not batched. Looping an agent over many calls tends to overweight the loudest signal and miss nuance — see

references/examples/lifecycle.md

for the call-refresh playbook.

运行时沙箱是context仓库的一个已检出、可执行的副本。你可以通过它读取和修改context文件，并在其中运行命令。

请记住两个重要行为：

write
和
edit
会推送到context仓库的默认分支，它们不是仅本地生效的操作。
execute
操作不会推送。通过
```
execute
```
运行shell命令对文件做出的更改仅保留在沙箱中，最终会被丢弃 — 请将
```
execute
```
用于构建、测试或检查操作，而非提交编辑内容。

上传的内容文件在
.files/
目录下为只读状态。工作区的

content file

上传文件（PDF、CSV、文本 — 详见

cargo-content

）会出现在沙箱的

.files/

目录中，因此通过

execute

（或

read

browse

）运行的命令可以读取这些文件 — 例如

cargo-ai context runtime execute --command ls --args '["-1",".files"]'

。该目录位于已提交的context树之外：沙箱的自动提交会跳过它，因此

.files/

下的任何内容都不会推送到context仓库，你也无法在此添加或修改内容文件（请改用

cargo-ai content file …

）。

由于写入操作会立即推送，在首次执行
write
/
edit
之前请确认目标工作区：

bash

cargo-ai whoami   # → workspace.uuid, workspace.name

将工作区名称告知用户。如果会话针对特定客户，请确保

workspace.name

匹配后再编写任何内容 — 本工具没有试运行模式。如果

workspace.name

通用或模糊（例如"Main"、"Test"、人名、内部代号），请勿猜测 — 请询问用户公司名称和规范域名（如

example.com

），确认两者匹配后再执行首次写入。如果登录时未绑定工作区，请重新运行

cargo-ai login --oauth --workspace-uuid <uuid>

（非交互式使用请用

--token <workspace-scoped-token>

）。

从销售通话分析得出的编辑内容应分批提交并由人工逐一审核，而非批量处理。让Agent循环处理多个通话记录往往会过度放大最明显的信号，忽略细节 — 请查看

references/examples/lifecycle.md

中的通话记录刷新指南。

Browse and read

浏览与读取

bash

undefined

bash

undefined

List entries at the root of the runtime sandbox

列出运行时沙箱根目录下的条目

cargo-ai context runtime browse

List entries under a subpath (e.g. a domain folder like persona/ or play/)

列出子路径下的条目（例如persona/或play/等领域文件夹）

cargo-ai context runtime browse --path persona

Read a full file

读取完整文件

cargo-ai context runtime read --path persona/vp-sales-mid-market.md

Read only a line range (1-indexed, inclusive on both ends)

仅读取指定行范围（从1开始计数，包含起止行）

cargo-ai context runtime read --path play/inbound-trial-to-paid.md --start-line 1 --end-line 40

undefined

cargo-ai context runtime read --path play/inbound-trial-to-paid.md --start-line 1 --end-line 40

undefined

Write a new file

写入新文件

write

creates (or overwrites) a file and pushes a commit to the default branch.

Begin every

.md

.mdx

file with a YAML frontmatter block setting

title

and

description

. Frontmatter is not validated — a file with missing, empty, or malformed frontmatter is still written and committed; it just indexes poorly in the graph (a missing

title

falls back to the filename, the node summary to the first paragraph).

write

can still fail for other reasons —

repositoryNotFound

syncConflict

syncFailed

failedToWrite

, or

deniedPath

(e.g. writing under

.files/

); see

references/response-shapes.md

bash

cargo-ai context runtime write \
  --path persona/vp-sales-mid-market.md \
  --content "$(cat <<'EOF'
---
title: VP of Sales, mid-market
description: Owns pipeline, quota, and rep productivity at a 200–2,000-person company.
---

write

用于创建（或覆盖）文件，并向默认分支推送提交。

每个

.md

.mdx

文件开头都应包含一个YAML前置块，设置

title

和

description

。前置块不做验证 — 缺少、为空或格式错误的前置块仍会被写入并提交，但会导致其在图谱中的索引效果不佳（缺少

title

会回退使用文件名，节点摘要会回退使用第一段内容）。

write

仍可能因其他原因失败：

repositoryNotFound

、

syncConflict

、

syncFailed

、

failedToWrite

或

deniedPath

（例如写入

.files/

目录下）；详情请查看

references/response-shapes.md

。

bash

cargo-ai context runtime write \
  --path persona/vp-sales-mid-market.md \
  --content "$(cat <<'EOF'
---
title: VP of Sales, mid-market
description: Owns pipeline, quota, and rep productivity at a 200–2,000-person company.
---

Role

Title: VP of Sales
Seniority: Executive
Function: Revenue
Reports to: CRO or CEO

Title: VP of Sales
Seniority: Executive
Function: Revenue
Reports to: CRO or CEO

KPIs

New ARR, win rate, pipeline coverage, rep ramp time

New ARR, win rate, pipeline coverage, rep ramp time

Pains

Pipeline gaps, slow ramp, low rep activity, forecasting drift

Pipeline gaps, slow ramp, low rep activity, forecasting drift

Motivations

Hit the number, build a repeatable motion, get visibility

Hit the number, build a repeatable motion, get visibility

Day-to-day

Forecast calls, deal reviews, pipeline reviews, 1:1s with frontline managers.

Preferred channels

medium/linkedin-outbound
medium/exec-warm-intro

medium/linkedin-outbound
medium/exec-warm-intro

Common objections

objection/we-already-have-an-ai-sdr

objection/we-already-have-an-ai-sdr

How we land

Lead with pipeline-coverage math, not features. EOF )"
--commit-message "Add VP of Sales mid-market persona"

undefined

Lead with pipeline-coverage math, not features. EOF )"
--commit-message "Add VP of Sales mid-market persona"

undefined

Edit an existing file

编辑现有文件

edit

replaces a single exact substring.

--old-string

must occur exactly once in the file; pass an empty

--new-string

to delete the match.

edit

does not validate frontmatter — an edit that strips or empties

title

description

still applies, so keep the block intact to keep the node discoverable.

edit

can fail for other reasons, though:

stringNotFound

stringNotUnique

(the

--old-string

match),

fileNotFound

noOp

(new string equals old),

syncConflict

syncFailed

failedToEdit

, or

deniedPath

bash

undefined

edit

用于替换单个精确子字符串。

--old-string

在文件中必须恰好出现一次；传入空的

--new-string

即可删除匹配内容。

edit

不验证前置块 — 若编辑操作删除或清空

title

description

，仍会生效，因此请保持前置块完整以确保节点可被发现。

edit

仍可能因其他原因失败：

stringNotFound

stringNotUnique

（

--old-string

匹配问题）、

fileNotFound

、

noOp

（新字符串与旧字符串相同）、

syncConflict

syncFailed

、

failedToEdit

或

deniedPath

。

bash

undefined

Replace one specific sentence

替换特定句子

cargo-ai context runtime edit
--path global/positioning.md
--old-string "We help RevOps automate workflows."
--new-string "We help RevOps run AI-native GTM motions."
--commit-message "Refresh positioning one-liner"

Delete a line (pass empty --new-string)

删除一行（传入空的--new-string）

cargo-ai context runtime edit
--path persona/vp-sales-mid-market.md
--old-string "\n- Outdated stat: 4.2x pipeline\n"
--new-string ""


For larger restructures, prefer `write` (full-file overwrite) over many sequential `edit` calls.

cargo-ai context runtime edit
--path persona/vp-sales-mid-market.md
--old-string "\n- Outdated stat: 4.2x pipeline\n"
--new-string ""


对于较大规模的重构，建议使用`write`（全文件覆盖）而非多次连续的`edit`调用。

Execute a command in the sandbox

在沙箱中执行命令

execute

runs a shell command in the sandbox. Useful for inspecting structure or running checks; changes are not pushed.

bash

undefined

execute

用于在沙箱中运行shell命令。适用于检查结构或运行校验；更改不会被推送。

bash

undefined

Find every file that cross-references a specific slug

查找所有引用特定slug的文件

cargo-ai context runtime execute
--command grep
--args '["-r","-l","persona/vp-sales-mid-market","."]'

Count entries per domain

统计各领域的条目数量

cargo-ai context runtime execute --command ls --args '["-1","persona"]'

Run a one-shot script (no quotes/escaping needed inside --command beyond JSON for args)

运行单次脚本（--command内无需额外转义，args遵循JSON格式即可）

cargo-ai context runtime execute --command pwd


`--args` is a JSON array of string arguments. Omit it for a no-arg command.

cargo-ai context runtime execute --command pwd


`--args`是字符串参数的JSON数组。无参数命令可省略该选项。

Context repository structure and conventions

Context仓库结构与规范

The Cargo context repo is a typed knowledge base. The canonical example — and the source of the conventions below — is

getcargohq/cargo-workspaces

; read its

README.md

and

_template.md

files in each domain before writing new entries. For the full domain reference, see

references/conventions.md

Cargo context仓库是一个类型化知识库。以下规范的标准示例为

getcargohq/cargo-workspaces

；在编写新条目之前，请阅读其

README.md

和各领域下的

_template.md

文件。完整的领域参考请查看

references/conventions.md

。

Domains

领域分类

Domain	Purpose
`global/`	Company-level context: mission, voice, positioning, narrative, pricing
`icp/`	Ideal Customer Profile segments
`persona/`	Buyer personas (roles inside an ICP)
`jtbd/`	Jobs-to-be-done framings
`alternative/`	Competitors, substitutes, status quo
`client/`	Customer profiles, case studies, reference accounts
`insight/`	Market insights and observations
`medium/`	Channel playbooks (email, LinkedIn, cold call, etc.)
`objection/`	Objections + responses + proof
`play/`	GTM plays (signal → audience → channel → sequence → outcome)
`proof/`	Atomic proof points (metrics, quotes, case data)
`signal/`	Buying signals and intent triggers

领域	用途
`global/`	公司级上下文：使命、品牌调性、定位、叙事、定价
`icp/`	理想客户画像细分
`persona/`	购买者角色（ICP内的具体职位）
`jtbd/`	待完成工作框架
`alternative/`	竞争对手、替代方案、现状
`client/`	客户档案、案例研究、参考客户
`insight/`	市场洞察与观察
`medium/`	渠道操作手册（邮件、LinkedIn、冷呼叫等）
`objection/`	异议处理+回复+案例证明
`play/`	GTM策略方案（信号→受众→渠道→流程→结果）
`proof/`	原子化证明点（指标、引用、案例数据）
`signal/`	购买信号与意向触发因素

File conventions

文件规范

Filename:
```
kebab-case.md
```
(e.g.
```
vp-sales-mid-market.md
```
).
Frontmatter: start every
```
.md
```
/
```
.mdx
```
file with YAML frontmatter setting
```
title
```
and
```
description
```
. This is a strong convention, not enforced — a write with missing, empty, or malformed frontmatter is still created and committed; it just indexes poorly. The graph reads
```
title
```
(fallback: filename) and
```
summary
```
(fallback: the file's first paragraph); it does not read
```
description
```
, so add a
```
summary:
```
if you want to control the node summary. See Source references and graph edges.
Cross-references: use the
```
domain/slug
```
form, no
.md
extension (e.g.
```
persona/vp-sales-mid-market
```
). To register as a graph edge a reference must use one of the three link forms below — a bare
```
domain/slug
```
(or file path) in plain prose creates no edge.
Templates: each domain ships an
```
_template.md
```
. Read it (
```
cargo-ai context runtime read --path persona/_template.md
```
) before authoring a new entry.
```
_template.*
```
files are excluded from the graph — never reference them.

文件名： 使用
```
kebab-case.md
```
格式（例如
```
vp-sales-mid-market.md
```
）。
前置块： 每个
```
.md
```
/
```
.mdx
```
文件开头都应包含YAML前置块，设置
```
title
```
和
```
description
```
。这是一个强规范而非强制要求 — 缺少、为空或格式错误的前置块仍会被创建并提交，但会导致索引效果不佳。图谱会读取
```
title
```
（回退为文件名）和
```
summary
```
（回退为文件第一段）；不会读取
description
，因此若要控制节点摘要，请添加
```
summary:
```
字段。详见源引用与图谱边。
交叉引用： 使用
```
domain/slug
```
格式，无需
.md
扩展名（例如
```
persona/vp-sales-mid-market
```
）。若要将注册为图谱边，引用必须使用以下三种链接形式之一 — 纯文本中的裸
```
domain/slug
```
（或文件路径）不会创建边。
模板： 每个领域都提供
```
_template.md
```
文件。在编写新条目之前，请先读取该文件（
```
cargo-ai context runtime read --path persona/_template.md
```
）。
```
_template.*
```
文件会被排除在图谱之外 — 请勿引用它们。

Source references and graph edges

源引用与图谱边

The knowledge graph is built from every

.md

.mdx

.yaml

, and

.yml

file in the repo (any folder; only

.git/

is excluded). Each file is a node, but edges are created only from three forms — anything else is invisible to the graph:

Frontmatter
references:
list (preferred for source citations — keeps prose clean):

yaml

---
title: AgoraPulse expansion thesis
description: Why AgoraPulse is ready for a multi-thread expansion play.
references:
  - outputs/sales-notes/2026-06-05-agorapulse-build-session-1-outcomes.md
---

A Markdown link in the body — standard
```
[label]
```
followed immediately by
```
(path)
```
syntax, where the target is the file path, e.g. an anchor linking to
```
outputs/sales-notes/2026-06-05-agorapulse-build-session-1-outcomes.md
```
.

Wikilinks in the body (extension optional):

[[outputs/sales-notes/2026-06-05-agorapulse-build-session-1-outcomes]]

Key constraints:

Never cite a source as a bare path in prose (e.g. a
```
Source:
```
line that just mentions
```
outputs/sales-notes/foo.md
```
as text) — it is not parsed and creates no edge.
Prefer root-relative paths (resolved from the repo root first, then relative to the citing file) so links work regardless of where the document lives.
Extensions are optional — the resolver auto-tries
```
.md
```
,
```
.mdx
```
,
```
.yaml
```
,
```
.yml
```
in that order. Including the extension is fine.
The target must exist or the edge is broken (a dead link in the graph UI). Verify with
```
runtime browse
```
before citing.
For docs with a Source/Evidence section, cite the files in frontmatter
```
references:
```
; use inline markdown links when the citation needs surrounding prose. Full rules:
```
references/conventions.md
```
.

知识图谱由仓库中所有

.md

、

.mdx

、

.yaml

和

.yml

文件构建（任何文件夹；仅

.git/

会被排除）。每个文件都是一个节点，但仅三种形式会创建边 — 其他形式对图谱不可见：

前置块
references:
列表（推荐用于源引用 — 保持文本简洁）：

yaml

---
title: AgoraPulse expansion thesis
description: Why AgoraPulse is ready for a multi-thread expansion play.
references:
  - outputs/sales-notes/2026-06-05-agorapulse-build-session-1-outcomes.md
---

正文中的Markdown链接 — 标准
```
[label]
```
后紧跟
```
(path)
```
语法，其中目标为文件路径，例如链接到
```
outputs/sales-notes/2026-06-05-agorapulse-build-session-1-outcomes.md
```
的锚点。

正文中的维基链接（扩展名可选）：

[[outputs/sales-notes/2026-06-05-agorapulse-build-session-1-outcomes]]

。

关键约束：

请勿在纯文本中以裸路径形式引用源（例如
```
Source:
```
行仅提及
```
outputs/sales-notes/foo.md
```
文本） — 不会被解析，也不会创建边。
优先使用根相对路径（先从仓库根目录解析，再相对于引用文件），这样无论文档位于何处，链接都能正常工作。
扩展名可选 — 解析器会自动按
```
.md
```
、
```
.mdx
```
、
```
.yaml
```
、
```
.yml
```
的顺序尝试。包含扩展名也可。
目标必须存在，否则边会失效（图谱UI中的死链）。引用前请用
```
runtime browse
```
验证。
对于包含Source/Evidence部分的文档，请在前块
```
references:
```
中引用文件；当引用需要上下文文本时，使用内联Markdown链接。完整规则请查看
```
references/conventions.md
```
。

Workflow: add a new entry

工作流：添加新条目

Confirm the target domain and copy its template:

bash

cargo-ai context runtime read --path persona/_template.md

```
write
```
a new file at
```
<domain>/<slug>.md
```
with
```
title
```
+
```
description
```
and the body sections filled in.
Add cross-refs (
```
domain/slug
```
) where useful — keep them bidirectional when it makes sense.
Rebuild the knowledge graph to verify the new entry and its links:
bash
```
cargo-ai context graph get
```

For full per-domain templates and worked examples, see

references/conventions.md

and

references/examples/authoring.md

确认目标领域并复制其模板：

bash

cargo-ai context runtime read --path persona/_template.md

使用
```
write
```
在
```
<domain>/<slug>.md
```
路径下创建新文件，填写
```
title
```
+
```
description
```
及正文部分。
在合适的位置添加交叉引用（
```
domain/slug
```
） — 合理情况下请保持双向引用。
重建知识图谱以验证新条目及其链接：
bash
```
cargo-ai context graph get
```

完整的各领域模板和示例请查看

references/conventions.md

和

references/examples/authoring.md

。

Workflow: bootstrap and refresh

工作流：初始化与刷新

To stand up a new workspace's context repo from scratch, or to refresh an existing one on a cadence, follow the two-phase lifecycle in

references/examples/lifecycle.md

Bootstrap (one-time): seed
```
global/
```
,
```
persona/
```
,
```
client/
```
,
```
proof/
```
,
```
objection/
```
,
```
signal/
```
from public sources, then open a fresh agent session against the seeded repo. For the prescriptive, automatable version (domain in → files out, idempotent, with credit budget), use
```
references/examples/bootstrap-from-domain.md
```
.
Refresh (every 2–4 weeks): pull the last ~3 months of sales-call transcripts → analyze one at a time, human-in-the-loop → apply a repetition threshold before promoting any claim to context → validate by generating sequence permutations → diff the graph before/after and retire stale entries.

The repetition threshold (how many calls a claim must appear in before it lands in context) is documented in

references/conventions.md

若要从零开始搭建新工作区的context仓库，或定期刷新现有仓库，请遵循

references/examples/lifecycle.md

中的两阶段生命周期：

初始化（一次性）： 从公共来源导入
```
global/
```
、
```
persona/
```
、
```
client/
```
、
```
proof/
```
、
```
objection/
```
、
```
signal/
```
的内容，然后针对已导入的仓库启动新的Agent会话。如需可自动化的规范版本（输入领域→输出文件，幂等，包含信用预算），请使用
```
references/examples/bootstrap-from-domain.md
```
。
刷新（每2-4周一次）： 拉取过去约3个月的销售通话记录→逐一分析，人工参与循环→在将任何结论加入context之前应用重复阈值→通过生成序列排列验证→对比刷新前后的图谱并淘汰过时条目。

重复阈值（结论需出现在多少个通话记录中才能加入context）请查看

references/conventions.md

。

Knowledge graph

知识图谱

context graph get

builds (or loads from cache) the knowledge graph over every markdown/MDX file in the context repo. Use it to:

Audit cross-references between domains (e.g. find personas that link to plays with no proof attached).
Discover what already exists before writing a new entry (avoid duplicates).
Power downstream agents that need the typed structure of the workspace's context.

bash

cargo-ai context graph get

The response includes the parsed frontmatter and outbound

domain/slug

references for each node — pipe it through

jq

to slice it. See

references/examples/graph-queries.md

for ready-to-run queries.

context graph get

会构建（或从缓存加载）基于context仓库中所有markdown/MDX文件的知识图谱。可用于：

审核各领域之间的交叉引用（例如查找链接到无证明方案的角色）。
在编写新条目之前发现现有内容（避免重复）。
为需要工作区上下文类型结构的下游Agent提供支持。

bash

cargo-ai context graph get

响应包含每个节点的解析前置块和出站

domain/slug

引用 — 可通过

jq

进行切片处理。现成的查询示例请查看

references/examples/graph-queries.md

。

Help

帮助

Every command supports

--help

bash

cargo-ai context --help
cargo-ai context runtime browse --help
cargo-ai context runtime read --help
cargo-ai context runtime write --help
cargo-ai context runtime edit --help
cargo-ai context runtime execute --help
cargo-ai context graph get --help

每个命令都支持

--help

选项：

bash

cargo-ai context --help
cargo-ai context runtime browse --help
cargo-ai context runtime read --help
cargo-ai context runtime write --help
cargo-ai context runtime edit --help
cargo-ai context runtime execute --help
cargo-ai context graph get --help