apply-non-consensus-category-conviction

apply-non-consensus-category-conviction is a skill for Claude Code from jeffreytse/grimoire-core. It costs 61 tokens per session (1,692 once invoked), scanned A, original, MIT.

An investment approach for backing an unproven business category when most investors doubt it, based on your own research and conviction.

In plain words
What is it for?
Evaluating early investments in new categories, unusual business models, or founders entering markets that established investors dismiss.
Why use it?
It helps investors assess opportunities that have little market history or price evidence, without simply following prevailing opinion.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the grimoire-business plugin — 145 skills shipped together

Good fit Evaluating early investments in new categories, unusual business models, or founders entering markets that established investors dismiss.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jeffreytse/grimoire-core/apply-non-consensus-category-conviction
Install

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.

Any agent
npx skills add jeffreytse/grimoire-core --skill apply-non-consensus-category-conviction
Clone the repo
git clone --depth 1 https://github.com/jeffreytse/grimoire-core

Made for: Claude Code.

Or install grimoire-business, the plugin that ships this one along with the rest of its 145 skills.

Wrote 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.

agentmods badge for apply-non-consensus-category-conviction

README.md
[![agentmods](https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-non-consensus-category-conviction/github.svg)](https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-non-consensus-category-conviction)
Your own site
<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-non-consensus-category-conviction"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-non-consensus-category-conviction/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.

agentmods 80×15 button for apply-non-consensus-category-conviction

Your own site · 80×15
<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-non-consensus-category-conviction"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-non-consensus-category-conviction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,692 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00061 $0.01692
Opus 5 $0.00030 $0.00846
Sonnet 5 $0.00012 $0.00338
Haiku 4.5 $0.00006 $0.00169

Measured 8d ago against content hash 9c5eaaa8d131, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

apply-non-consensus-category-conviction 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 8d 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.

skills/business/entrepreneurship/skills/apply-non-consensus-category-conviction/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Apply Non-Consensus Category Conviction

Bet on an unproven category or business model that the prevailing market consensus actively doubts or dismisses, when independently-formed conviction — grounded in deep prior understanding, not contrarianism for its own sake — supports it, since the earliest, highest-impact opportunities in venture investing are frequently ones the mainstream view has already dismissed.

Why This Is Best Practice

Adopted by: Liu Qin (刘芹) and Wuyuan Capital's (五源资本) early investment in Xiaomi is documented in Chinese and English technology press accounts as a genuinely non-consensus bet at the time — made against prevailing industry skepticism that a new entrant, led by a founder without an established track record in that specific hardware category, could meaningfully compete against entrenched incumbents. The investment is now widely cited in venture capital case studies specifically as an example of conviction formed independently of, and against, the prevailing consensus view at the time of investment. Impact: Because a genuinely unproven category or company has no established market price or track record to validate a thesis against, the entire basis for the investment must come from independently-formed conviction rather than external confirmation — by the time consensus catches up and confirms the thesis, much of the opportunity's earliest, most attractive risk-adjusted return has typically already been captured by earlier, non-consensus investors. The documented Xiaomi case is specifically cited as an example where the return to being early and non-consensus, rather than waiting for confirming signals, was substantial. Why best: Waiting for market consensus to validate a category before investing forfeits the specific return premium available to genuinely early, non-consensus conviction — by definition, once consensus has formed, the highest-conviction, lowest-priced entry point has typically already passed. The willingness to act on independently-formed conviction ahead of consensus, when that conviction is genuinely well-grounded (see apply-deep-industry-immersion-research) rather than merely contrarian for its own sake, is what allows an investor to capture this early-stage opportunity.

Read the full file on GitHub · 68 lines

Changes

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.

  1. 8d ago First seen · 68 lines · 61 tokens per session scan A 9c5eaaa8d131

Subscribe to this mod's changes

apply-non-consensus-category-conviction is a skill published in the GitHub repository jeffreytse/grimoire-core (4 stars, last pushed 23d ago), licensed MIT. It adds 61 tokens to every session and 1,692 once invoked, about $0.0003 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 2026-09-03.

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