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 aAAaqwq/AGI-Super-Team --skill company-analyzergit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/company-analyzer)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/company-analyzer"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/company-analyzer/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/aaaaqwq/agi-super-team/company-analyzer"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/company-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.00631 |
| Opus 5 | $0.00000 | $0.00316 |
| Sonnet 5 | $0.00000 | $0.00126 |
| Haiku 4.5 | $0.00000 | $0.00063 |
Grade A, and why
company-analyzer scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Bash 4+, jq, bc, curl What it actually says
company-analyzer
企业深度分析引擎——多框架 LLM 分析，带缓存、成本追踪、速率限制
使用场景
- 对上市公司进行全面分析（基本面、技术面、竞争格局等）
- 多分析框架并行执行（如 SWOT、估值模型、行业分析等）
- 预算控制下的批量公司分析
- 作为 CRO（研究官）的核心分析工具
使用方法
# Shell 库引用方式（在分析脚本中 source）
source ~/clawd/skills/company-analyzer/scripts/lib/api-client.sh
source ~/clawd/skills/company-analyzer/scripts/lib/cache.sh
source ~/clawd/skills/company-analyzer/scripts/lib/cost-tracker.sh
# 调用 LLM 分析
result=$(call_llm_api "分析 AAPL 的竞争优势" 8192)
content=$(extract_content "$result")
prompt_tokens output_tokens=$(extract_usage "$result" "$original_prompt")
# 缓存管理
key=$(cache_key "AAPL" "01-swot" "$prompt")
cached=$(cache_get "$key") # 返回缓存或空
cache_set "$key" "$response" '{"framework":"swot"}'
# 成本追踪
log_cost "AAPL" "swot" 1500 3000 # 记录 API 调用成本
check_budget # 检查是否超预算（默认 $0.10/天）
核心模块
| 模块 | 文件 | 说明 |
|---|---|---|
| API Client | scripts/lib/api-client.sh |
配置驱动的 LLM 客户端，自动读 OpenClaw 配置 |
| Cache | scripts/lib/cache.sh |
SHA256 键 + 7 天 TTL 持久化缓存 |
| Cost Tracker | scripts/lib/cost-tracker.sh |
动态价格查找 + 日预算检查 |
| Trace | scripts/lib/trace.sh |
日志追踪 |
| Prices | scripts/lib/prices.json |
各模型 token 单价表 |
配置要求
- Bash 4+, jq, bc, curl
- OpenClaw 配置：
~/.openclaw/openclaw.json（模型/提供商/密钥） - 认证配置：
~/.openclaw/agents/main/agent/auth-profiles.json - 默认日预算：$0.10（可修改
DAILY_BUDGET变量）
相关文件
scripts/lib/api-client.sh— LLM API 客户端（速率限制、重试、降级）scripts/lib/cache.sh— 生产级持久化缓存scripts/lib/cost-tracker.sh— 动态成本追踪scripts/lib/prices.json— 模型价格表scripts/lib/trace.sh— 日志工具
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.
- 6d ago First seen · 53 lines · 0 tokens per session scan A 5566a8b68e7d
company-analyzer is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 631 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
prompt-tuning
Tune a prompt, or anything whose quality is measured by non-deterministic model output, without chasing noise - a noise baseline before the first edit, medians over repeated runs, enforcement AFTER generation rather than in the wording. Use when iterating on prompts or model-judged output.
memory-patterns
Persistent memory patterns for moflo agents — session memory, long-term knowledge, pattern learning, and cross-session context via moflo's node:sqlite + HNSW vector store. Use when building stateful agents or assistants that need to remember across runs.
vector-search
Semantic vector search with moflo — RAG over your own documents, similarity matching, context-aware retrieval via HNSW (node:sqlite-backed). Use when building retrieval layers for chat, search, or context-assembly.
memory-optimization
Tune moflo's memory stack for speed, RAM, and index quality. Covers HNSW parameters (M, efConstruction, ef), vector quantization, batch operations, and common bottlenecks. Use when scaling past 100k entries or when search latency regresses.
sap-rpt1
SAP-RPT-1-OSS local tabular prediction workflows for FI/CO prototype datasets. Use when preparing SAP finance CSV exports for classification or regression experiments with source-verified setup, leakage checks, and governance review.
ai-cost-guard
Find every code path that can run up a paid-inference bill without a ceiling (loops, retries, fan-out, agent steps, queues) and require a written dollar cap both in code and at the provider. Use before shipping anything that calls an LLM, speech, or image API, or when a bill was larger than expected.