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/double-coding-lab/flow2spec/f2s-kb-rmnpx skills add double-coding-lab/Flow2Spec --skill f2s-kb-rmgit clone --depth 1 https://github.com/double-coding-lab/Flow2SpecWhat 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 | $0.00040 | $0.00908 |
| Opus 5 | $0.00020 | $0.00454 |
| Sonnet 5 | $0.00008 | $0.00182 |
| Haiku 4.5 | $0.00004 | $0.00091 |
Grade A, and why
f2s-kb-rm 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 2d ago.
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.
What it actually says
执行口径:仅维护
.Knowledge,不改配置根rules/skills。
编排(主 / 子 agent)
- 两字段(
subAgent/switchAgentVerification)语义以统一入口为唯一事实源:Cursor/Claude 读配置根rules/f2s-flow2spec-unified-entry.*;Codex 读.codex/topics/f2s-flow2spec-unified-entry.md(与上同源,flow2spec init镜像)。不在此复述。 - 默认主 agent 全流程执行(单点删除拆子收益低)。
- 拆子阈值:仅当
subAgent=true且批量删除一次 ≥ 5 主题时,才拆子执行删除与清引用。 - 主必控:范围确认、
fallbackTopic重指。 - 写权硬约束:
manifest-routing.json与.Knowledge/index.md恒由主 agent 落盘。 - 验证:默认落盘侧自验;本 SKILL 不绑定交叉校验。
删除文档对应的项目上下文
输入
- 一个参数:
.Knowledge/stock-docs/<文件名>.md路径,或可匹配文件名片段。
执行步骤
- 读取
.Knowledge/index.md,匹配目标文档相关主题。 - 删除对应
.Knowledge/topics/<topic>.md文件。 - 从
.Knowledge/index.md移除匹配项并写回。 - 更新路由清单:
.Knowledge/manifest-routing.json:移除失效topicPaths、taskToTopicRules、topicDependencies、topicMetadata引用- 对应
matchers/<matcherId>.json:移除失效规则或includeAny词条(与已删task/matcherId对齐) - 若删除了
fallbackTopic,必须指定新的兜底主题 - 创作侧准则:本步会调整
topicDependencies(删除被依赖主题或孤儿边),须先 Readrules/f2s-topic-authoring.*全文(Cursor/Claude:rules/f2s-topic-authoring.mdc;Codex:.codex/topics/f2s-topic-authoring.md),核对 DAG 与最小化约束后再落盘。
输出摘要(必须)
- 已删除的 topic 文件列表
.Knowledge/index.md删除的条目- 路由清单调整的字段
- 未执行项(若有)
复杂场景示例
用户输入文件名片段「回调」,匹配到 2 个主题文档。
- 先列出两个候选并要求用户确认删除范围,避免误删。
- 删除后同步清理路由清单失效引用;若删到了
fallbackTopic,必须先指定新的兜底主题再落盘。 - 最终摘要中写清:删除了哪些 topic、保留了哪些 topic、为什么。
约束
- 匹配多义时先询问用户确认。
- 仅删除命中主题,不影响其它主题。
manifest-routing.json与.Knowledge/index.md恒由主 agent 落盘(写权硬约束);范围确认与fallbackTopic重指不可下放给子 agent。
完成后自检
- 被删 topic 是否仍被
manifest引用(必须为否)。 index是否仍存在失效主题路径(必须为否)。topicMetadata是否仍引用已删除 topic(必须为否)。fallbackTopic是否仍有效。- 未在低于拆子阈值(< 5 主题)时强行拆子;manifest / index 由主单点落盘。
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.
- 2d ago First seen · 62 lines · 40 tokens per session scan A d7120b62393f
f2s-kb-rm is a skill published in the GitHub repository double-coding-lab/Flow2Spec (47 stars, last pushed 5d ago), licensed MIT. It adds 40 tokens to every session and 908 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…