danghuangshang is a multi-agent collaboration system that organizes specialized AI agents into a hierarchy modeled on historical Chinese government institutions. Users delegate tasks to these agents through platforms such as Discord or Feishu, with roles for coordination, coding, review, memory, and automation. Its catalogue entries are the project's agents and skills.
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 skills add wanikua/danghuangshang --skill githubgit clone --depth 1 https://github.com/wanikua/danghuangshangWrote 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/skills/wanikua/danghuangshang/github)<a href="https://agentmods.dev/skills/wanikua/danghuangshang/github"><img src="https://agentmods.dev/badge/skills/wanikua/danghuangshang/github/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/skills/wanikua/danghuangshang/github"><img src="https://agentmods.dev/badge/skills/wanikua/danghuangshang/github.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 | $0.00044 | $0.00310 |
| Opus 5.5 | $0.00018 | $0.00124 |
| Sonnet 5.5 | $0.00009 | $0.00062 |
| Haiku 4.5 | $0.00004 | $0.00031 |
Grade A, and why
github 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 1mo 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
81% identical to github — 3 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.
What it actually says
GitHub Skill
Use the gh CLI to interact with GitHub. Always specify --repo owner/repo when not in a git directory, or use URLs directly.
Pull Requests
Check CI status on a PR:
gh pr checks 55 --repo owner/repo
List recent workflow runs:
gh run list --repo owner/repo --limit 10
View a run and see which steps failed:
gh run view <run-id> --repo owner/repo
View logs for failed steps only:
gh run view <run-id> --repo owner/repo --log-failed
API for Advanced Queries
The gh api command is useful for accessing data not available through other subcommands.
Get PR with specific fields:
gh api repos/owner/repo/pulls/55 --jq '.title, .state, .user.login'
JSON Output
Most commands support --json for structured output. You can use --jq to filter:
gh issue list --repo owner/repo --json number,title --jq '.[] | "\(.number): \(.title)"'
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.
- 1mo ago First seen · 48 lines · 44 tokens per session scan A 51b28818a6f0
github is a skill published in the GitHub repository wanikua/danghuangshang (2,711 stars, last pushed 5mo ago), licensed MIT. It adds 44 tokens to every session and 310 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 81% identical to github, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
artifact-metadata
Manage artifact metadata, versioning, ownership, and review history across the SDLC lifecycle.
steward-prep-delivery
Steward-assisted prep for filing issues and PRs — environment capture, template selection, duplicate detection, delivery-policy compliance check.
effect-ledger
Record an effect, sign an effect, reconcile an effect or check "did this PR merge" through the signed AIWG effect ledger, using one aiwg effect command with scriptable exit codes.
contribute-start
Initialize an AIWG contribution workflow by creating a feature branch, configuring DCO, and linking a tracking issue.
usecase-dev-task-queue
Create and run an agent-ready development queue from issues/PRs with progress checkpoints.
launch
Planning releases and reporting delivery work from GitHub PR history. Use when versioning, CHANGELOGs, rollout or rollback plans, engineering metrics, retrospectives, or stakeholder reports are needed.