01 / THE SIGNAL

我们发现了什么

The open-source control plane for AI agents — skill routing, context governance, trustworthy execution, evidence, security, and multi-agent orchestration.. Title: GitHub - casioreview20-glit

  • 来源:GitHub(发现于 2026-07-27)
  • 证据等级:D · 发现产品或需求信号,暂未获得可核验的商业证据。
  • 商业模式:待核验
  • 主题:独立产品
  • 初筛评分:17.7/100 · 收录 1 次
#独立开发#待验证#开源
02 / SOURCE & EVIDENCE

证据,比故事更重要。

发现产品或需求信号,暂未获得可核验的商业证据。

规则清洗与初筛,未经人工商业核验。原文语境、实际客户和付费情况仍需自行验证。

引用与数字披露

来源类型(原作者自述/第三方测算/媒体转引)需采集端标注,本版尚未落字段。

短句引用
作者
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抓取日期
来源类型
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数字口径
币种
未标注
口径
未标注
披露主体
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上下文核对:来源原文含限定词projection,中文摘要未逐字保留 —— 引用或跨期比较前请回原文核对,别把估算读成已实现。

中文辅助译文(节选·触顶截断)

ForgeOS

面向 AI 智能体的技能智能操作系统与信任控制平面。

ForgeOS 决定可以运行哪些技能、哪些上下文可以进入、哪些步骤必须是确定性的,以及哪些证据足以接受完成。

5 分钟路径 · 工作原理 · 生态对比 · 面向开发者 · 面向专家 · Tiếng Việt · 22 种语言翻译


ForgeOS 存在的原因

智能体不会因为拥有更多的提示、更多的工具或更长的上下文窗口就变得可靠。

它在系统能够回答以下六个问题时才会变得可靠:

  1. 需要的具体结果是什么?
  2. 哪种技术是合适的——而哪些类似的技术在此处是错误的?
  3. 该工作单元所需的最小上下文是什么?
  4. 哪些步骤必须是确定性的,而不是委派给模型?
  5. 哪些独立证据可以证明输出?
  6. 同一工作流在失败后能否恢复、继续并审计自身?

ForgeOS v0.6 将这些问题转化为运行时:

已确认的意图
→ 结果 + 技术检索
→ 硬性策略与反触发过滤器
→ 最小 RoutePlan DAG
→ 每个工作单元隔离的 ContextPack
→ 确定性 / 智能体 / 反思执行图
→ 锚定输出 + 覆盖账本
→ 可信回执 + 证据门控
→ 发布、回滚、恢复与学习隔离

它不是提示词集合。它是围绕技能、规则、钩子、智能体、工具、上下文、证据和学习的控制平面。


v0.6.1 中真实存在的内容

表面已验证实现
旧版类型化结果脚手架1,024
Deep Skill Contract v2 技术128
L0 编排 / 信任 / 上下文技术32
L1 跨领域工程技术96
独立评估器绑定128
技能目录250(146 核心,104 领域)
稳定的程序化提供器33
候选程序化提供器242
内置技能 + 知识映射1,299
代码审查智能一致性用例12
智能体表面对抗用例20/20
稳定提供器物化33/33
路由器 Precision@1 / @393.75% / 100%
路由器 Recall@6100%
不安全路由激活0%

[!IMPORTANT] 这 1,024 个旧版节点是结果脚手架,并非 1,024 个生产级程序化技能。v0.6 包含 128 个深度技术合约。33 个程序化提供器因兼容性而保留在声明的稳定路由通道中,但最终认证审计发现 0/128 证据合格的稳定项,以及 0 项通过 Revision 2 完成定义认证。其余证据需要留出、配套多模型、压力测试、独立审查和生产回执。

内核清单: 32 项 L0 技术 + 96 项 L1 技术 = 128 项深度内核技术。

目录路由状态: 33 个声明为稳定通道的程序化提供器以及 242 个候选。正式认证证据: 0 项稳定合格,0 项认证。请参阅 最终认证审计。

发布审计有意将这些声明保持为假:

1,024 个生产级程序化技能 假
完整的 PostgreSQL 生命周期高可用 假
通用 microVM 沙箱 假
专家标注的 200-PR 审查基准 假
10,000 次配对评估运行 假

ForgeOS v0.6 不声称具备通用生产完整性,也不声称拥有 1,024 个生产级程序化技能。

请参阅 v0.6 声明边界。


通用通道,治理执行

不可变的 通用通道注册表 将工作路由 跨战略与研究、产品 UX/UI、视觉媒体与 3D、软件、 数据与 AI、智能体、安全、云运维、商务、教育、 硬件 / 制造以及机器人 / 物理 AI。通道是一种诚实的路由 与证据合约——并非声称 ForgeOS 可以自主运行工厂、 机器人、支付流程或生产环境。

物理世界的工作仍需人类批准。高风险第三方执行 需要兼容的已配置远程提供器;本地代理从不 伪装为 microVM。请参阅 远程 MicroVM 沙箱。


五分钟路径

当您希望获得价值而无需先学习信任内核时,请使用此路径。

1. 安装

npm install
npm test
node src/cli/forge.mjs init

已安装的包:

npx forgeos init
forge doctor

forge init 创建一个安全的本地 SQLite-WAL 配置文件。其 API 密钥被写入一个 0600 权限的文件中,且永远不会被打印出来。

2. 找到合适的技术

forge skills search "react rerender"
forge skills inspect reducing-react-render-thrashing
forge route --query "compile the minimum context for a large monorepo"

3. 检查 v0.6

forge v06 status
forge profile plan coding --target codex
forge security scan --file agent-surface.json

4. 检查高风险执行边界

node src/cli/forge.mjs sandbox status --json

在没有配置远程提供器的情况下,此命令会故意返回 unavailable 和退出码 2;ForgeOS 从不将本地代理表示为 microVM。有关签名提供器合约,请参阅 远程 MicroVM 沙箱。

5. 启动本地控制平面

npm start
# 仪表板:http://127.0.0.1:8787/dashboard
# MCP:http://127.0.0.1:8787/mcp
# A2A:http://127.0.0.1:8787/a2a

深度操作员路径

在将 ForgeOS 嵌入 Codex、Claude Code、ChatGPT、开源智能体、CI 或内部平台时,请使用此路径。

技能智能路由器

路由器执行两阶段检索,而非匹配技能名称:

意图 / 失败门控
→ 结果检索
→ 直接技术触发检索
→ 反触发排除
→ 信任、租户、成熟度、工具、许可证、新鲜度过滤器
→ 效用度量重排序
→ 最小技术 DAG
→ 提供器解析
→ 冻结的 RoutePlan

每个被选中和被拒绝的技术都有一个理由。硬性阻断始终胜过评分。

全局上下文内核 v2

ForgeOS 对完整请求进行预算管理:

系统 · 任务 · 已选技能段落 · 代码符号 · 制品
· 记忆 · 工具输出 · 引用 · 惰性工具模式
· 输出预留 · 安全预留

它提供:

  • 一个由解析器和物化器共享的令牌计费接口;
  • 段落级技能加载;
  • 每个工作单元隔离的上下文;
  • 惰性工具模式物化;
  • 语义 ABI 符号 ID 与陈旧哈希拒绝;
  • 制品差异投影;
  • 受作用域限制、带过期的本能注入;
  • 内容寻址的原始日志与提炼后的失败区间;
  • 每个未包含源的缺失清单。

确定性技能织构

一个 v0.6 技术被编译为可执行图:

确定性节点
范围选择 · 打包 · 规则解析 · 锚定 · 证据

智能体节点
调查 · 假设 · 领域判断

反思节点
矛盾 · 假阳性过滤 · 可执行性

控制节点
并行汇合 · 覆盖门控 · 重试 · 回滚

SQLite 覆盖账本使用租约、心跳、隔离令牌和可信回执。被回收的工作器无法将工作单元标记为完成。

代码审查智能垂直切片

首个完整的垂直切片端到端地证明了架构:

完整范围
→ 关系感知工作单元
→ 上下文规则选择
→ 隔离的智能体分析
→ 行 / 哈希锚点
→ 编辑后重定位
→ 独立反思
→ 覆盖回执

打包的 12 用例语料库是一个确定性一致性基准。它不被宣传为专家标注的 200-PR 基准。

持续学习——无需自动自我污染

观察到的模式成为受限的本能,而非稳定技能:

可信运行回执
→ 观察到的本能
→ 租户 / 项目 / 装备隔离 + TTL
→ 兼容的本能集群
→ 候选演进提议
→ 独立评估
→ 人类提升或回滚

生产者无法提升自身习得的行为。

装备运行时 v2

ForgeOS 区分四种表面:

表面用途
规则必须始终应用的简短不变量
钩子绑定到事件的确定性动作
技能需要判断的条件性程序
智能体角色独立的上下文、工具、模型或权限

中性事件包括 before.tool.execute、after.file.write、verification.checkpoint、session.compact 和 session.ended。宿主适配器必须标记不支持的功能,而不是声称虚假的等价性。

配置文件:

minimal · coding · creative · research · regulated
local-small · enterprise

智能体表面安全

安全引擎扫描智能体系统本身:

  • 指令和提示边界违规;
  • 钩子和包生命周期脚本;
  • MCP 描述、权限和工具可达性;
  • 命令允许列表;
  • 密钥 / 环境引用;
  • 密钥到出站的权限路径;
  • 管道到 shell 和广泛的通配符能力;
  • 安装前的配置文件权限差异。

其对抗语料库当前通过 20/20 用例。

代理式本地执行

本地运行器为普通命令提供真正的安全边界:

  • 无 shell 插值;
  • 命令和环境允许列表;
  • 工作区和符号链接容纳;
  • 超时和进程组终止;
  • 有界 stdout / stderr;
  • 内容寻址的执行回执。

它不是通用的网络拒绝型 microVM 沙箱。高风险第三方执行仍需要外部容器或 microVM 隔离层。

ForgeOS 现在拥有一个故障闭合的远程 microVM 控制平面适配器:它仅提交到已配置的 HTTPS 提供器,并且仅接受请求绑定、由 Ed25519 签名的回执,以及明确的网络和密钥隔离证据。它从不回退到本地代理。该适配器不是托管的 microVM 服务;兼容的生产提供器仍是外部部署要求。请参阅 远程 MicroVM 沙箱。


ForgeOS 工作原理

ForgeOS 将两个产品合并到一个运行时中:

  1. 技能智能层:检索技术、拒绝不安全的近似匹配、仅编译所需的技能段落,并构建冻结的执行计划。
  2. AI 控制平面:管理项目、制品、证据、审批、租约、恢复、联邦和发布门控。
已确认的意图或失败门控
→ 结果和直接技术检索
→ 反触发、租户、信任、工具、许可证和新鲜度过滤器
→ 最小冻结 RoutePlan DAG
→ 每个工作单元隔离的 ContextPack
→ 确定性 / 智能体 / 反思执行图
→ 锚定输出和隔离的覆盖账本
→ 可信回执和保障感知门控
→ 发布、恢复、回滚或学习隔离

十个协作系统

系统控制内容
技能智能路由器结果检索、技术评分、反触发、硬性策略、提供器选择,以及可解释的 RoutePlan
全局上下文内核 v2跨策略、任务、技能段落、符号、制品、记忆、工具输出、引用和输出预留的统一令牌预算
确定性技能织构包含确定性节点、智能体节点、反思节点、审批、锚点和停止条件的混合图
覆盖账本工作单元所有权、租约、隔离令牌、完成覆盖、陈旧工作器拒绝和可恢复性
信任内核证据新鲜度、制品谱系、审批权限、保障级别和发布决策
智能体表面安全提示注入模式、危险的包脚本、密钥到出站路径、权限和适配器能力诚实性
代理式本地执行无 shell

……(正文超出本站单页篇幅上限,此处截断;完整表述请见下方原文入口。)

译文由上游机器翻译生成,可能有误;判断请以英文原文为准。

英文原文(来源本站未改写)

ForgeOS

Skill Intelligence OS and Trust Control Plane for AI agents.

ForgeOS decides which skill may run , which context may enter , which steps must be deterministic , and which evidence is strong enough to accept completion .

5-minute path · How it works · Ecosystem comparison · For developers · For experts · Tiếng Việt · 22 translations


Why ForgeOS exists

An agent does not become reliable because it has more prompts, more tools, or a longer context window.

It becomes reliable when the system can answer six questions:

  1. What exact outcome is required?
  2. Which technique is appropriate—and which similar techniques are wrong here?
  3. What is the smallest context needed for this work unit?
  4. Which steps must be deterministic rather than delegated to a model?
  5. What independent evidence proves the output?
  6. Can the same workflow recover, resume, and audit itself after failure?

ForgeOS v0.6 turns those questions into a runtime:

confirmed intent
→ outcome + technique retrieval
→ hard policy and anti-trigger filters
→ minimum RoutePlan DAG
→ isolated ContextPack per work unit
→ deterministic / agent / reflection execution graph
→ anchored outputs + coverage ledger
→ trusted receipts + evidence gates
→ release, rollback, recovery, and learning quarantine

It is not a prompt collection. It is the control plane around skills, rules, hooks, agents, tools, context, evidence, and learning.


What is real in v0.6.1

SurfaceVerified implementation
Legacy typed outcome scaffolds1,024
Deep Skill Contract v2 techniques128
L0 orchestration/trust/context techniques32
L1 cross-domain engineering techniques96
Independent evaluator bindings128
Skill catalog250 (146 core, 104 domain)
Stable procedural providers33
Candidate procedural providers242
Built-in skill + knowledge mappings1,299
Code Review Intelligence conformance cases12
Agent-surface adversarial cases20/20
Stable provider materialization33/33
Router Precision@1 / @393.75% / 100%
Router Recall@6100%
Unsafe route activation0%

[!IMPORTANT] The 1,024 legacy nodes are outcome scaffolds, not 1,024 production-grade procedural skills. v0.6 contains 128 deep technique contracts. Thirty-three procedural providers remain in the declared stable routing channel for compatibility, but the final certification audit finds 0/128 evidence-qualified stable and 0 certified under the Revision 2 Definition of Done. The remaining evidence requires holdout, paired multi-model, pressure, independent review, and production receipts.

Kernel inventory: 32 L0 techniques + 96 L1 techniques = 128 deep kernel techniques.

Catalog routing states: 33 declared stable-channel procedural providers and 242 candidates. Formal certification evidence: 0 stable-qualified, 0 certified. See Final Certification Audit.

The release audit intentionally keeps these claims false:

1,024 production-grade procedural skills false
full PostgreSQL lifecycle HA false
universal microVM sandbox false
expert-labeled 200-PR review benchmark false
10,000 paired evaluation runs false

ForgeOS v0.6 does not claim universal production completeness or 1,024 production-grade procedural skills.

See Claims Boundary v0.6.


Universal lanes, governed execution

The immutable Universal Lane Registry routes work across strategy and research, product UX/UI, visual media and 3D, software, data and AI, agents, security, cloud operations, commerce, education, hardware/manufacturing, and robotics/physical AI. A lane is a truthful routing and evidence contract—not a claim that ForgeOS can autonomously operate a factory, robot, payment flow, or production environment.

Physical-world work remains human-approved. High-risk third-party execution requires a compatible configured remote provider; the local broker never pretends to be a microVM. See Remote MicroVM Sandbox.


Five-minute path

Use this path when you want value without learning the Trust Kernel first.

1. Install

npm install
npm test
node src/cli/forge.mjs init

Installed package:

npx forgeos init
forge doctor

forge init creates a safe local SQLite-WAL profile. Its API key is written to a 0600 file and is never printed.

2. Find the right technique

forge skills search "react rerender"
forge skills inspect reducing-react-render-thrashing
forge route --query "compile the minimum context for a large monorepo"

3. Inspect v0.6

forge v06 status
forge profile plan coding --target codex
forge security scan --file agent-surface.json

4. Check the high-risk execution boundary

node src/cli/forge.mjs sandbox status --json

Without a configured remote provider this intentionally returns unavailable and exit code 2; ForgeOS never represents the local broker as a microVM. See Remote MicroVM Sandbox for the signed provider contract.

5. Start the local control plane

npm start
# Dashboard: http://127.0.0.1:8787/dashboard
# MCP: http://127.0.0.1:8787/mcp
# A2A: http://127.0.0.1:8787/a2a

Deep operator path

Use this path when embedding ForgeOS into Codex, Claude Code, ChatGPT, an open-source agent, CI, or an internal platform.

Skill Intelligence Router

The router performs two-stage retrieval instead of matching a skill name:

intent / failed gate
→ outcome retrieval
→ direct technique-trigger retrieval
→ anti-trigger exclusion
→ trust, tenant, maturity, tool, license, freshness filters
→ measured-utility reranking
→ minimum technique DAG
→ provider resolution
→ frozen RoutePlan

Every selected and rejected technique has a reason. Hard blockers always beat score.

Global Context Kernel v2

ForgeOS budgets the complete request:

system · task · selected skill sections · code symbols · artifacts
· memory · tool output · references · lazy tool schemas
· output reserve · safety reserve

It provides:

  • one token-accounting interface shared by resolver and materializer;
  • section-level skill loading;
  • isolated context per work unit;
  • lazy tool-schema materialization;
  • Semantic ABI symbol IDs and stale-hash rejection;
  • artifact delta projection;
  • scoped, expiring instinct injection;
  • content-addressed raw logs with distilled failure ranges;
  • an omission manifest for every source not included.

Deterministic Skill Fabric

A v0.6 technique is compiled into an executable graph:

Deterministic nodes
scope-selection · bundling · rule-resolution · anchoring · evidence

Agent nodes
investigation · hypothesis · domain judgment

Reflection nodes
contradiction · false-positive filter · actionability

Control nodes
parallel join · coverage gate · retry · rollback

The SQLite coverage ledger uses leases, heartbeat, fencing, and trusted receipts. A reclaimed worker cannot mark a work unit complete.

Code Review Intelligence vertical slice

The first complete vertical slice proves the architecture end to end:

complete scope
→ relation-aware work units
→ contextual rule selection
→ isolated agent analysis
→ line/hash anchors
→ relocation after edits
→ independent reflection
→ coverage receipt

The bundled 12-case corpus is a deterministic conformance benchmark. It is not advertised as an expert-labeled 200-PR benchmark.

Continuous Learning—without automatic self-poisoning

Observed patterns become scoped instincts, not stable skills:

trusted run receipts
→ observed instinct
→ tenant/project/harness isolation + TTL
→ compatible instinct cluster
→ candidate evolution proposal
→ independent evaluation
→ human promotion or rollback

The producer cannot promote its own learned behavior.

Harness Runtime v2

ForgeOS distinguishes four surfaces:

SurfaceUse it for
RuleShort invariant that must always apply
HookDeterministic action bound to an event
SkillConditional procedure requiring judgment
Agent roleSeparate context, tools, model, or authority

Neutral events include before.tool.execute, after.file.write, verification.checkpoint, session.compact, and session.ended. Host adapters must mark unsupported features instead of claiming false parity.

Profiles:

minimal · coding · creative · research · regulated
local-small · enterprise

Agent Surface Security

The security engine scans the agent system itself:

  • instruction and prompt-boundary violations;
  • hooks and package lifecycle scripts;
  • MCP descriptions, permissions, and tool reachability;
  • command allowlists;
  • secret/environment references;
  • secret-to-egress permission paths;
  • pipe-to-shell and broad wildcard capability;
  • profile permission diffs before installation.

Its adversarial corpus currently passes 20/20 cases.

Brokered local execution

The local runner provides a real safety boundary for normal commands:

  • no shell interpolation;
  • command and environment allowlists;
  • workspace and symlink containment;
  • timeout and process-group termination;
  • bounded stdout/stderr;
  • content-addressed execution receipt.

It is not a universal network-denying microVM sandbox. High-risk third-party execution still requires an external container or microVM isolation layer.

ForgeOS now has a fail-closed remote microVM control-plane adapter: it submits only to a configured HTTPS provider and accepts only request-bound, Ed25519-signed receipts with explicit network and secret isolation evidence. It never falls back to the local broker. The adapter is not a hosted microVM service; a compatible production provider remains an external deployment requirement. See Remote MicroVM Sandbox.


How ForgeOS works

ForgeOS combines two products in one runtime:

  1. A Skill Intelligence layer that retrieves techniques, rejects unsafe near-matches, compiles only the required skill sections, and builds a frozen execution plan.
  2. An AI control plane that manages projects, artifacts, evidence, approvals, leases, recovery, federation, and release gates.
confirmed intent or failed gate
→ outcome and direct-technique retrieval
→ anti-trigger, tenant, trust, tool, license, and freshness filters
→ minimum frozen RoutePlan DAG
→ isolated ContextPack per work unit
→ deterministic / agent / reflection Execution Graph
→ anchored outputs and fenced Coverage Ledger
→ trusted receipts and assurance-aware gates
→ release, recovery, rollback, or learning quarantine

Ten cooperating systems

SystemWhat it controls
Skill Intelligence RouterOutcome retrieval, technique scoring, anti-triggers, hard policy, provider selection, and explainable RoutePlans
Global Context Kernel v2One total token budget across policy, task, skill sections, symbols, artifacts, memory, tool output, references, and output reserve
Deterministic Skill FabricHybrid graphs containing deterministic nodes, agent nodes, reflection nodes, approvals, anchors, and stop conditions
Coverage LedgerWork-unit ownership, leases, fencing tokens, completion coverage, stale-worker rejection, and resumability
Trust KernelEvidence freshness, artifact lineage, approval authority, assurance levels, and release decisions
Agent Surface SecurityPrompt-injection patterns, dangerous package scripts, secret-to-egress paths, permissions, and adapter capability honesty
Brokered Local ExecutionShell-free comma

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出处https://github.com/casioreview20-glitch/forge-os抓取日期 · 采集源 GitHub

03 / EVIDENCE GAPS

这条还缺什么证据?

下面每条都由本条已有字段推出(等级、理由、商业模式、来源次数、是否演示), 本站不生成推测性结论;通用验证方法放在方法论页。

  • 可核验的收入或付费证据查官网定价页与付费口径;第三方数据源(如 GetLatka)只作旁证,需标注来源与时点。
  • 商业模式未定确认按席位/按用量/授权还是开源托管版收费;开源项目另查 LICENSE 与是否存在付费版。
  • 只有单一来源找一手站点或其他渠道是否重复出现同一产品;社区热帖数量不等于商业进展。

通用验证清单(谁有这个问题/谁愿意付费/一个人能交付哪一小步)见我们的筛选方法。

04 / SIGNAL HISTORY

发现时间线