knowledge-map

A project index that points developers to important documents, modules, tests, scripts, and configuration files.

In plain words
What is it for?
Use it to find project guidance, PDF-processing code, benchmarks, tests, and configuration settings.
Why use it?
It reduces the time spent searching a large codebase and keeps the index aligned with documentation changes.

Agent

Install

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.

agentmods
npx agentmods add agents/threefish-ai/negentropy/knowledge-map
Clone the repo
git clone --depth 1 https://github.com/ThreeFish-AI/negentropy
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 572 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00572
Opus 5 $0.00000 $0.00286
Sonnet 5 $0.00000 $0.00114
Haiku 4.5 $0.00000 $0.00057

Measured 2d ago against content hash 9fcb6a15dff3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

knowledge-map 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.

docs/reference/perceives/agents/knowledge-map.md · 48 lines

What it actually says

Knowledge Map(知识索引)

本项目所有文档与关键模块的索引入口,按主题分组。文档目录变更时必须 同步更新本表(AGENTS.md 「Knowledge Map」条款)。

协作约定

PDF Pipeline

  • pdf-engine-selection.md — Adaptive Engine Selection 决策图(PR #163):DocumentCharacteristics 驱动的 Stage 短路 与 tool 重排。
  • apple-silicon-tuning.md — Apple M 系列 GPU 调优指南:设备探测、代次缩放、Docling/MinerU/Marker 各引擎的 MPS 策略、 PyMuPDF 多页并行。
  • ../framework.md — PDF/Webpage 双 Pipeline 整体架构、 10 Stage 流程、5 级引擎降级链。
  • ../user-guide.md — MCP 工具使用指南。
  • ../issue.md — 历史 Issue 摘要与教训。

基准与脚本

测试入口

  • 单元:tests/unit/(含 test_engine_selector.pytest_chip_generation.pytest_pymupdf_parallel.pytest_engine_init_kwargs.py 等)
  • 集成:tests/integration/
  • 运行:scripts/test/run-tests.sh

配置

Changes

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.

  1. 2d ago First seen · 48 lines · 0 tokens per session scan A 9fcb6a15dff3

Subscribe to this mod's changes

knowledge-map is an agent published in the GitHub repository ThreeFish-AI/negentropy (10 stars, last pushed 2d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 572 tokens. 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.

Related

Other agents, from other repositories

README

This folder consolidates, in a single location, the SoT for the project's five multi-agent roles plus the Universal Cycle skeleton. It is modeled on the role system of a Korean newspaper, and each role is self-contained in its capability boundary, I/O contract, and prompt template.

alfadur7/llm-wiki-newsroom · 0 tokens

columnist

Authors L2-2 full hub expansion + L2-2 timeline narrative + all L2-3·L2-4 content (cluster overview·theme contradiction·synthesis·trail·root overview·root contradiction). Deep cross-source sequential reading + synthesis. Performs the GROUND·APPLY·ADAPT cycle stages together. No direct external WebSearch.

alfadur7/llm-wiki-newsroom · 76 tokens

reporter

Owner of L2-1 source · L2-2 stub authoring and broad external exploration. raw input (.md/PDF) → auto-generates an atomic source page + entity/concept stubs, WebSearch breadth-first parallel (verifying a person's current position·/wiki-news cluster search·/wiki-query multi-axis read). For the cycle stages, performs…

alfadur7/llm-wiki-newsroom · 103 tokens

code-reviewer

Adversarial multi-dimensional code review — security, performance, correctness, spec compliance, maintainability. Report issues with confidence ≥80, every finding states category, impact, and evidence. Runs after component-builder in BUILD workflows.

romiluz13/cc10x · 49 tokens

planner

Create a saved execution plan or decision RFC when implementation work needs an agreement-first artifact before execution.

romiluz13/cc10x · 21 tokens

triage-agent

Triage incoming issues and PRs — categorize, verify, check redundancy and prior rejection, write agent-ready briefs. Read-only. Routes: TRIAGE workflow.

romiluz13/cc10x · 36 tokens