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/mit-network/everything-claude-code/investor-materialsnpx skills add mit-network/everything-claude-code --skill investor-materialsgit clone --depth 1 https://github.com/mit-network/everything-claude-codeWrote 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/mit-network/everything-claude-code/investor-materials)<a href="https://agentmods.dev/skills/mit-network/everything-claude-code/investor-materials"><img src="https://agentmods.dev/badge/skills/mit-network/everything-claude-code/investor-materials.svg" alt="Measured on agentmods" 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 | $0.00061 | $0.00573 |
| Opus 5 | $0.00030 | $0.00287 |
| Sonnet 5 | $0.00012 | $0.00115 |
| Haiku 4.5 | $0.00006 | $0.00057 |
Grade A, and why
investor-materials 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 5d 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
98% identical to investor-materials — 1 line 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investor Materials
Build investor-facing materials that are consistent, credible, and easy to defend.
When to Activate
- creating or revising a pitch deck
- writing an investor memo or one-pager
- building a financial model, milestone plan, or use-of-funds table
- answering accelerator or incubator application questions
- aligning multiple fundraising docs around one source of truth
Golden Rule
All investor materials must agree with each other.
Create or confirm a single source of truth before writing:
- traction metrics
- pricing and revenue assumptions
- raise size and instrument
- use of funds
- team bios and titles
- milestones and timelines
If conflicting numbers appear, stop and resolve them before drafting.
Core Workflow
- inventory the canonical facts
- identify missing assumptions
- choose the asset type
- draft the asset with explicit logic
- cross-check every number against the source of truth
Asset Guidance
Pitch Deck
Recommended flow:
- company + wedge
- problem
- solution
- product / demo
- market
- business model
- traction
- team
- competition / differentiation
- ask
- use of funds / milestones
- appendix
If the user wants a web-native deck, pair this skill with frontend-slides.
One-Pager / Memo
- state what the company does in one clean sentence
- show why now
- include traction and proof points early
- make the ask precise
- keep claims easy to verify
Financial Model
Include:
- explicit assumptions
- bear / base / bull cases when useful
- clean layer-by-layer revenue logic
- milestone-linked spending
- sensitivity analysis where the decision hinges on assumptions
Accelerator Applications
- answer the exact question asked
- prioritize traction, insight, and team advantage
- avoid puffery
- keep internal metrics consistent with the deck and model
Red Flags to Avoid
- unverifiable claims
- fuzzy market sizing without assumptions
- inconsistent team roles or titles
- revenue math that does not sum cleanly
- inflated certainty where assumptions are fragile
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.
- 5d ago First seen · 97 lines · 61 tokens per session scan A 0541ac8ede8d
investor-materials is a skill published in the GitHub repository mit-network/everything-claude-code (76 stars, last pushed 5mo ago), licensed MIT. It adds 61 tokens to every session and 573 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to investor-materials, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
sector-rotation
行业轮动分析——申万行业景气度评分、行业动量排名、产业链传导、估值/盈利/资金流多维比较框架.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
chenhao-limit-up
Use when evaluating A-share limit-up (涨停板) setups through Chen Hao's sentiment and momentum lens: market emotion cycles, board strength, follow-through, and short-term aggressive momentum trading.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
multi-expert-analyzer
针对通用问题进行多领域专家联合分析, 综合稿产生前必经 fact-checker 与 red-team 两道独立校验。适用场景: 用户提出跨领域或不确定领域的复杂问题, 需要从多个专家角度分别搜证并相互校验后综合成文, 例如该不该买房、该不该跳槽、是否进入某个赛道等。触发关键词: 多角度分析、专家分析、综合分析、多视角、跨领域分析、从不同角度看、深度分析。问题只属于单一明确领域时, 优先使用该领域的专门 skill, 例如纯财务用 finance-core-analysis、纯技术用 software-architect。输出 (全部 markdown 保存到当前项目 markdown/ 目录): (1) 每位专家的中间分析稿…