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 t0moon/competitorsmart --skill 46-ai-ai-strategygit clone --depth 1 https://github.com/t0moon/competitorsmartWrote 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/t0moon/competitorsmart/46-ai-ai-strategy)<a href="https://agentmods.dev/skills/t0moon/competitorsmart/46-ai-ai-strategy"><img src="https://agentmods.dev/badge/skills/t0moon/competitorsmart/46-ai-ai-strategy/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/t0moon/competitorsmart/46-ai-ai-strategy"><img src="https://agentmods.dev/badge/skills/t0moon/competitorsmart/46-ai-ai-strategy.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 | $0.00032 | $0.01258 |
| Opus 5 | $0.00016 | $0.00629 |
| Sonnet 5 | $0.00006 | $0.00252 |
| Haiku 4.5 | $0.00003 | $0.00126 |
Grade A, and why
AI产品策略 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 11d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 11d ago First seen · 108 lines · 32 tokens per session scan A 81eca0ae9168
AI产品策略 is a skill published in the GitHub repository t0moon/competitorsmart (24 stars, last pushed 5mo ago), with no licence file. It adds 32 tokens to every session and 1,258 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
azure-ai-projects-java
Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations. Triggers: "AIProjectClient java", "azure ai projects java", "Foundry project java", "ConnectionsClient", "DatasetsClient", "IndexesClient".
duration-prediction
Predict project duration using k-NN and regression. Estimate timeline based on similar historical projects.
schedule-forecaster
Predict project completion dates using ML models. Forecast schedule delays based on current progress, historical patterns, and risk factors.
voice-to-report
Convert voice recordings to structured construction reports. Field workers speak, AI transcribes and formats. Supports daily reports, safety observations, progress updates.
n8n-cost-estimation
Build n8n pipeline for automated cost estimation from Revit/IFC using DDC CWICR database and LLM classification.
pipeline
Data pipeline and ETL -- extraction, transformation, loading, data quality, orchestration.