Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/stringke/std-agent/add-targetnpx skills add StringKe/std-agent --skill add-targetgit clone --depth 1 https://github.com/StringKe/std-agentWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00071 | $0.01600 |
| Opus 5 | $0.00036 | $0.00800 |
| Sonnet 5 | $0.00014 | $0.00320 |
| Haiku 4.5 | $0.00007 | $0.00160 |
Grade A, and why
add-target scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Maintain targets
目标:ValidTargets 与仍在维护、且有项目级配置面的 AI CLI 对齐。新平台按官方能力接入 5 种 type;已有平台只改有证据的差异;官方停更或配置面消失则移除。不破坏共享输出。
开始前读取 .stdai/standards/references/transformer-design.md、docs/format-spec.md、docs/conversion-rules.md,以及相近 target 的 docs/targets/<name>.md 与 transformer。
- 用户点名工具:只处理该 target(含接入、更新或移除)。
- 未点名:必须同时做三件事,先出差异表再改代码。无证据变化不要重写。
- 发现未注册的新平台
- 复核每个
ValidTargets的官方规范 - 判定停更项并准备移除
未要求时不 commit、不发版。移除是 breaking change:先列出官方证据和受影响路径,确认后再删代码。
发现新平台
对照 internal/config.ValidTargets 搜索尚未接入的 AI coding CLI / IDE agent。用 exa 查产品与新闻,用 context7 和官方站点查项目级配置,用 gh_grep 核仓库实现。社区文章只作明确标注的补充。
接入门槛:有文档化的项目级 rules/skills/commands/agents 配置,且 stdagent 能写出该工具会发现的文件。只有用户级 ~/ 配置或纯聊天 UI 的列入 skip,不要接入。
每个候选记 add / skip / watch,并带来源 URL。
调研现有 target
官方文档和官方仓库是协议依据。每条结论带完整来源 URL。
用当前官方页面对照 adapter 与 docs/targets/<name>.md。逐项标 native / degrade / skip / UNKNOWN:
| 维度 | 要确认的事实 |
|---|---|
| 存活 | 产品是否仍发布;仓库是否 archived;是否改名/被后继产品取代 |
| 发现路径 | 项目根文件、目录、加载顺序、嵌套、本地覆盖文件 |
| rules | 原生路径、path matcher、always-on、是否 inline 到根文件 |
| skills | 是否 Agent Skills;目录;frontmatter 白名单 |
| commands | 原生 prompts/commands/workflows,或降为 skill 子目录 |
| references | 原生目录或 fallback |
| subagents | 原生 agents 目录与字段,或 fallback |
| MCP | 项目级文件与顶层 key;用户配置合并则不要覆盖 |
| 共享路径 | 是否消费 AGENTS.md / .agents/;多 target 同路径必须字节一致 |
| 限额 | 单文件、根文件、总量的 soft/hard;超限是截断、拒载还是淘汰 |
| 不写 | hooks、用户 settings、全局 ~/ 配置、crush.json / kilo.jsonc 这类 JSONMerge 用户文件 |
硬限制只记录有官方证据的值,并同步 internal/budget.Limits。没有文档化上限就写 UNKNOWN,不要发明数字。
停更与移除
满足任一官方证据则移除(或先迁到后继 target):
- 官方宣布停更、sunset 或产品下线
- 官方仓库 archived,且没有沿用同一项目配置的后继产品
- 项目级配置面消失,stdagent 再也写不出该工具会发现的文件
- 产品改名或被后继取代:优先把现有 target 迁到后继约定,不要留僵尸名
不要因为发布间隔长、用户少、文档暂时 404 就移除。证据不足标 UNKNOWN 并保留。
改名迁移不算静默删除:保留用户仍在用的路径,直到后继 adapter 能写出等价文件。
从 ValidTargets 删除会使旧 config.toml 校验失败,commit 使用 ! 与 BREAKING CHANGE。共享路径(如 AGENTS.md)只要还有其他 producer 就保留。本仓库先从 .stdai/config.toml 去掉该 target,再 go run ./cmd/stdagent sync --no-pull --strict 让 prune 清掉其独占生成物,然后删代码。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 91 lines · 71 tokens per session scan A 5f82c06eb1c5
add-target is a skill published in the GitHub repository StringKe/std-agent (8 stars, last pushed 11d ago), licensed MIT. It adds 71 tokens to every session and 1,600 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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