Pigou Wu / AI + Education

Pigou Wu

想法中优先级javascript

cc-glm-dynamic-shim

Anthropic Messages shim for using GLM Coding Plan models with cc dynamic workflows

Progress
41%
进度41%
updated / 2026-06-28
Signal Brief
Cool
State

仍在成形 / 41%。

Risk

no code insight

Next

wiki gap / No LLM code insight snapshot has been generated yet. Run pnpm sync:wiki -- --with-llm.

Verdict
Cool

weak signal

Recent
  • GitHub / 12 天前 / PIGU-PPPgu/cc-glm-dynamic-shim
  • wiki / 今天 / 15 files
Source
source
local signals
progress
41%
health
48
open tasks
0
repo pushed
2026-06-15
wiki snapshot
2026-06-27
Goals
3
  • 1整理项目定位
  • 2补充 README 摘要
  • 3确认展示边界
Next
2
  • 01Generate LLM code insights for the most important source files.
  • 02Add one approved public screenshot or product surface image.
Links
1
Health
watch
health score
48
product41

AI progress / project progress

momentum42

updates / doing / logs / open tasks

clarity54

README / media / wiki / code

evidence36

shipped / snapshot / media / confidence

risk93

waiting / stale / AI risks

Watch
  • no code insight
  • no public media
Priority
algorithm / sync-worker-priority-signal-engine / medium
Now
中优先级
AI
低优先级
Score
36

综合分 36/100,建议为 low。这个值来自任务紧急度、近期动量、战略相关性、成熟/影响证据和停滞惩罚;它只是候选建议,只有 apply 后才会改项目 priority。

generated / 2026-06-28
urgency0/100

Open P0/P1 tasks and active doing items.

momentum34/100

Recent repository pushes, project updates, and log activity.

strategic_fit28/100

Current lifecycle state plus Pigou OS/workflow/life-state relevance.

maturity_or_impact13/100

Progress, shipped/use signals, screenshots, and repository scale.

staleness_penalty0/100

Paused, archived, or stale projects should cool down unless other signals are strong.

依据 / 5
  • no open linked tasks
  • repo files: 15
  • last push: 2026-06-15
  • strategic OS/workflow signal in project text
  • progress evaluation: 41% idea
Progress AI
algorithm / sync-worker-signal-engine / medium

GitHub sync processed; Pigou OS refreshed the project wiki, progress, status, and sync log signals from repository evidence.

The sync pipeline applied local repository, wiki, task, image, and log signals after refreshing GitHub data.

generated / 2026-06-28
product_readiness5/25

Project status, launch wording, and product surface signals.

technical_completeness10/20

Repository file count, framework, entrypoint, module, and code insight signals.

usage_validation4/25

Launch/use wording, screenshots, and visible product proof.

documentation_knowledge11/15

Project notes, README summary, and wiki/code insight completeness.

momentum8/15

Recent push, linked work items, and project log activity.

Evidence / 2
  • repo files: 15
  • last push: 2026-06-15
Risk / 1
  • No LLM code insight snapshot has been generated yet. Run pnpm sync:wiki -- --with-llm.
Next / 2
  • Generate LLM code insights for the most important source files.
  • Add one approved public screenshot or product surface image.
README
public

CC-GLM Dynamic Workflow Shim

Local compatibility shim for using GLM Coding Plan models, especially glm-5.1, behind Claude Code's Anthropic Messages API surface.

It is useful when a tool expects Anthropic-compatible /v1/messages requests, but your GLM Coding Plan access is exposed through an OpenAI-compatible chat/completions endpoint.

Smoke-tested with Claude Code dynamic workflows:

DeepWiki
高置信indexed
Purpose

Unofficial compatibility shim. This project is not affiliated with, endorsed by, or sponsored by Anthropic, Claude Code, Z.ai, or BigModel.

Structure

JavaScript project with 15 indexed file(s).

Entry

package.json

Next scan
  • No LLM code insight snapshot has been generated yet. Run pnpm sync:wiki -- --with-llm.
  • Generate LLM code insights for the most important source files.
  • Add one approved public screenshot or product surface image.
Facts
  • Repository: PIGU-PPPgu/cc-glm-dynamic-shim
  • Default branch: main
  • Indexed files: 15
  • Primary language: JavaScript
  • Frameworks: not detected
  • Last pushed: 2026-06-15
  • Snapshot: 2026-06-27
Repo
15
files
0
frameworks
Next
  1. 1Generate per-file summaries for important source files.
  2. 2Connect modules to project goals and next actions.
  3. 3Detect stale README sections by comparing code signals with project description.
  4. 4Persist historical wiki snapshots to show architecture drift.
Modules

examples

高置信

Repository directory: 2 file(s)

.gitignore

高置信

Repository directory: 1 file(s)

install-launchagent.sh

高置信

Repository directory: 1 file(s)

LICENSE

高置信

Repository directory: 1 file(s)

package.json

高置信

Repository directory: 1 file(s)

quickstart.sh

高置信

Repository directory: 1 file(s)

Key files
package.jsonNode package manifest
README.mdHuman project explanation
结构图谱
drag canvas / zoom / click expand
Gaps
  • No LLM code insight snapshot has been generated yet. Run pnpm sync:wiki -- --with-llm.
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