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 agentmods add skills/opsintech/opsintech-platform/consulting-analysisnpx skills add OpsinTech/opsintech-platform --skill consulting-analysisgit clone --depth 1 https://github.com/OpsinTech/opsintech-platformWhat 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 | $0.00110 | $0.07332 |
| Opus 5 | $0.00055 | $0.03666 |
| Sonnet 5 | $0.00022 | $0.01466 |
| Haiku 4.5 | $0.00011 | $0.00733 |
Grade A, and why
consulting-analysis 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 yesterday.
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
98% identical to consulting-analysis — 12 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.
How it starts
The opening of the file, as written. The whole thing — 632 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Professional Research Report Skill
Overview
This skill produces professional, consulting-grade research reports in Markdown format, covering domains such as market analysis, consumer insights, brand strategy, financial analysis, industry research, competitive intelligence, investment research, and macroeconomic analysis. It operates across two distinct phases:
- Phase 1 — Analysis Framework Generation (第一阶段：分析框架生成): Given a research subject, produce a rigorous analysis framework including chapter skeleton, per-chapter data requirements, analysis logic, and visualization plan.
- Phase 2 — Report Generation (第二阶段：报告生成): After data has been collected by other skills, synthesize all inputs into a final polished report.
The output adheres to McKinsey/BCG consulting voice standards. The report language, plan steps, and phase names (e.g. use "第一阶段", "第二阶段" instead of "Phase 1", "Phase 2") follow the output_locale setting (default: zh_CN for Chinese).
Data Authenticity Protocol
Strict Adherence Rule: All data presented in the report and visualized in charts MUST be derived directly from the provided Data Summary or External Search Findings.
- NO Hallucinations: Do not invent, estimate, or simulate data. If data is missing, state "Data not available" rather than fabricating numbers.
- Traceable Sources: Every major claim and chart must be traceable back to the input data package.
Core Capabilities
- Design analysis frameworks from scratch given only a research subject and scope
- Transform raw data into structured, high-depth research reports
- Follow the "Visual Anchor → Data Contrast → Integrated Analysis" flow per sub-chapter
- Produce insights following the "Data → User Psychology → Strategy Implication" chain
- Embed pre-generated charts and construct comparison tables
- Generate inline citations formatted per GB/T 7714-2015 standards
- Output reports in the language specified by
output_localewith professional consulting tone - Adapt analytical depth and structure to domain (marketing, finance, industry, etc.)
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.
- yesterday First seen · 632 lines · 110 tokens per session scan A 98875268f720
consulting-analysis is a skill published in the GitHub repository OpsinTech/opsintech-platform (92 stars, last pushed 1mo ago), licensed MIT. It adds 110 tokens to every session and 7,332 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to consulting-analysis, differing in 12 lines, and is treated as a copy.
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