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 zhanglunet/qcc --skill qcc-anchorgit clone --depth 1 https://github.com/zhanglunet/qccWrote 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/zhanglunet/qcc/qcc-anchor)<a href="https://agentmods.dev/skills/zhanglunet/qcc/qcc-anchor"><img src="https://agentmods.dev/badge/skills/zhanglunet/qcc/qcc-anchor/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/zhanglunet/qcc/qcc-anchor"><img src="https://agentmods.dev/badge/skills/zhanglunet/qcc/qcc-anchor.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 | $0.00141 | $0.00727 |
| Opus 5 | $0.00071 | $0.00364 |
| Sonnet 5 | $0.00028 | $0.00145 |
| Haiku 4.5 | $0.00014 | $0.00073 |
Grade A, and why
qcc-anchor 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 12d 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.
What it actually says
qcc-anchor — 企查查实体锚定
何时用
- 任何 QCC 调用前,只要用户给的不是 18 位 USCC,都先跑本 skill
- 用户输入是企业简称 / 品牌名 / 股票简称 / 容易重名的全称(如"小米科技""阿里巴巴")
- 之前调用某个工具返回"无匹配" — 用本 skill 反向锁定再重试
不用的场景
- 用户已经给出 18 位 USCC(如
91110108551385082Q)→ 跳过,直接调下游 - 用户只是问元信息(QCC 本身怎么用) → 不用 skill
调用流程
- 调
qcc-company:get_company_by_query,searchKey= 用户输入原文 - 看返回的 匹配结果 字段:
- 唯一精确匹配 → 取
企业信息.统一社会信用代码,继续下游 - 多候选 → 必须把候选清单完整展示给用户,等用户明确选定后才继续。禁止自动选第一个
- 未匹配 → 提示用户检查拼写,或换关键词(品牌名 / 简称)重试
- 唯一精确匹配 → 取
- (可选)拿到 USCC 后,如果需要二次核验"名称-USCC 是否匹配",调
qcc-company:verify_company_accuracy
用到的 tool
| tool | server | 输入 | 用途 |
|---|---|---|---|
get_company_by_query |
company | searchKey | 模糊匹配 → 唯一/候选/未匹配 |
verify_company_accuracy |
company | searchKey + companyName | 二要素核验(可选) |
调用示例
# 锚定阶段
qcc-py call company get_company_by_query -a '{"searchKey":"小米科技"}'
# → 返回唯一匹配 USCC: 91110108551385082Q
# 后续调用全部用 USCC
qcc-py call company get_company_registration_info -a '{"searchKey":"91110108551385082Q"}'
输出契约
{
"anchor_status": "unique | multi_candidate | no_match",
"uscc": "91110108551385082Q",
"canonical_name": "小米科技有限责任公司",
"candidates": [ /* multi_candidate 时填,最多 5 条 */ ]
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 61 lines · 141 tokens per session scan A 927aed346b1b
qcc-anchor is a skill published in the GitHub repository zhanglunet/qcc (2 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 141 tokens to every session and 727 once invoked, about $0.0007 per session on Opus 5. 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.
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