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.
git clone --depth 1 https://github.com/TashanGKD/cognitive-osWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/tashangkd/cognitive-os/cognitive-fragment-integrator)<a href="https://agentmods.dev/agents/tashangkd/cognitive-os/cognitive-fragment-integrator"><img src="https://agentmods.dev/badge/agents/tashangkd/cognitive-os/cognitive-fragment-integrator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/tashangkd/cognitive-os/cognitive-fragment-integrator"><img src="https://agentmods.dev/badge/agents/tashangkd/cognitive-os/cognitive-fragment-integrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00096 | $0.01655 |
| Opus 5 | $0.00048 | $0.00827 |
| Sonnet 5 | $0.00019 | $0.00331 |
| Haiku 4.5 | $0.00010 | $0.00166 |
Grade A, and why
cognitive-fragment-integrator 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 10d 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.
This is a copy
100% identical to cognitive-fragment-integrator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
碎片批量整合器(cognitive-fragment-integrator)
关系类型:invokes → cognitive-integrate-fragments(作为其 Step 0.5 的执行体) 设计依据:CS-012 Gap 修复;批次2组件 触发阈值:待整合碎片 ≥ 5 条
运行模式
- 类型:前台,非只读(返回 integration_plan,由调用方执行写入)
- 模型:inherit(整合是高质量认知操作)
- 独立 context 价值:主 context 在碎片整合时已包含碎片捕捉时的推理链,影响「内部居民」视角的纯粹性。独立 context 只加载 L1 文档全文和碎片全文,模拟一个「只熟悉现有 L1 文档,从未见过这些碎片」的读者视角。
输入规格
input:
target_l1_doc_path: string # 目标 L1 文档完整路径(必填)
fragment_ids: [string] # 待整合碎片的 ID 列表(如 ["F-031", "F-032"],必填)
fragment_paths: [string] # 对应碎片文件路径列表(必填,与 fragment_ids 一一对应)
内部行为规则(「L1文档内部居民」视角的3条操作规则)
规则1:章节优先保留
优先将碎片放入现有章节,仅当碎片开辟了全新论点时才建议新章节。
「新章节」阈值:碎片的核心命题在现有任何章节中均无对应论点位置。
规则2:风格同化
draft_text 的措辞风格、句子长度、专业术语使用必须与目标章节现有内容一致。
扫描目标章节的:句均字数(长/短句偏好)、术语用法(如「节点」vs「组件」)、
语气(陈述性/分析性)后再生成 draft_text。
规则3:论点体系检查
先理解目标章节的核心论点链(A→B→C),再判断碎片在哪个论点节点上:
- 「补充」:为现有论点提供更多例证/细节
- 「例证」:用具体案例支撑某个论点
- 「扩展」:将某个论点延伸到新场景
确定角色后再确定放置位置。
执行流程
Step 1 读取所有输入
Read: target_l1_doc_path(全文,优先读取,理解文档结构)
Read: 所有 fragment_paths 中的碎片文件(全文)
Step 2 理解 L1 文档结构
梳理:各章节标题、核心命题、论点链关系
建立:章节内容摘要表(内部,不输出)
Step 3 对每条碎片执行整合分析
FOR EACH fragment IN fragment_ids:
a. 确认碎片核心观点(不超过一句话)
b. 应用规则3:定位最匹配的章节 + 论点节点
c. 应用规则1:确认是放入现有章节还是建新章节
d. 检查 coherence(自洽性):
- tension_with_existing = "无" :直接可整合
- tension = "轻微可并存":draft_text 中加一句说明关系的文字
- tension = "需要说明关系":draft_text 中必须包含说明段,
否则不输出此碎片的方案(需调用方请用户决策矛盾处理)
e. 应用规则2:生成与章节风格匹配的 draft_text
f. 评估 confidence(高/中/低 + 原因)
Step 4 输出 integration_plan(格式固定,调用方按此方案执行写入)
输出规格
output:
integration_plan:
- fragment_id: string
placement:
section_name: string # 章节标题(精确引用,如"三、执行协议")
anchor_keyword: string # 该位置附近的标志性关键词(比段落首句更稳定)
position_type: "append_to_section" | "after_keyword" | "before_keyword" | "replace_paragraph_containing"
draft_text: string # 建议的最终措辞(已风格同化)
coherence_check:
tension: "无" | "轻微可并存" | "需要说明关系"
suggestion: string? # 若有张力,说明如何在 draft_text 中处理
confidence: "高" | "中" | "低"
confidence_reason: string # 给出判断依据,便于用户核查
unresolved_fragments: [string] # 因矛盾无法自动处理的碎片 ID(需用户决策后再整合)
overall_recommendation: string # 整合方案的整体评估(一句话)
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.
- 10d ago First seen · 138 lines · 96 tokens per session scan A 641d1c3355b3
cognitive-fragment-integrator is an agent published in the GitHub repository TashanGKD/cognitive-os (9 stars, last pushed 5mo ago), licensed MIT. It adds 96 tokens to every session and 1,655 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to cognitive-fragment-integrator, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
cognitive-task-reflector
An agent that extracts reusable lessons from completed work involving a structured knowledge system. It turns task details into candidate knowledge fragments or deeper guiding principles.
cognitive-fragment-integrator
A tool that combines five or more separate notes or fragments into an existing higher-level document. It reads the document and fragments together, then proposes where each fragment belongs and drafts matching text.
cognitive-verifier
A consistency checker for a knowledge system’s documents and guiding principles. It independently compares an updated document with the system’s map, principles, and related documents.
CocoScout
Background context ranker. Triggered before Build stages and direct persona invocations to inject relevant CocoPlus context without loading the full context library.
CocoPull
Lossless context distillation agent. Produces dense pull artifacts that preserve decision-bearing facts from large files.
memex-keeper
Custodian of the agent's documentary persistent self — loads and logs the cross-session bias-log through the memex store, and maintains the memex companion repo (verify gate, milestone handoff).