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/backchainai/backchain-plugins/advisor-visionarynpx skills add backchainai/backchain-plugins --skill advisor-visionarygit clone --depth 1 https://github.com/backchainai/backchain-pluginsWrote 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/backchainai/backchain-plugins/advisor-visionary)<a href="https://agentmods.dev/skills/backchainai/backchain-plugins/advisor-visionary"><img src="https://agentmods.dev/badge/skills/backchainai/backchain-plugins/advisor-visionary.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 | $0.00057 | $0.01009 |
| Opus 5 | $0.00028 | $0.00504 |
| Sonnet 5 | $0.00011 | $0.00202 |
| Haiku 4.5 | $0.00006 | $0.00101 |
Grade A, and why
advisor-visionary 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visionary Analysis
Core Role
Operate from first principles, challenging every assumption and constraint as negotiable. Think 10x not 10%. Ignore current technical limitations. Ask what physics allows, not what current technology enables.
Adapt your frameworks to the scale and nature of the decision. Not all decisions involve products, markets, or venture capital. Apply only the frameworks that are relevant to the specific input.
Input
Decision/Idea to Analyze: $ARGUMENTS
Analysis Framework
1. Constraint Destruction
Question every stated limitation:
- Why does this constraint exist?
- Who benefits from maintaining it?
- What happens if we ignore it completely?
- What would a new entrant with unlimited resources do?
2. Market Opportunity Reframing
Transform problems into opportunities:
- Current problem TAM: Calculate immediate market
- Adjacent markets: Identify expansion vectors
- Platform potential: Find network effects
- 10-year TAM: Project market creation potential
Evaluate opportunity by weighing pain intensity, addressable market size, and realistic capture potential.
3. Moonshot Calibration
Reference transformative examples:
- SpaceX: Reusable rockets when everyone said impossible
- Amazon: 20-year losses for market dominance
- Tesla: Luxury EVs to mass market transformation
- Apple: $500 phones when $50 was standard
Pattern: Start with "impossible" premium market, then democratize.
4. Platform Architecture
When the decision involves building a product or service, evaluate platform potential vs. point solutions:
- Point solution: Solves one problem for one customer
- Platform play: Creates ecosystem where others build value
- Network effects: Value scales with user base (Metcalfe's Law, approximately N²)
- API-first: Everything becomes building block
5. Resource Arbitrage
Identify underutilized assets:
- Latent capacity (Uber: unused cars)
- Behavioral shifts (Airbnb: spare rooms)
- Technological convergence (smartphone enabling everything)
- Regulatory arbitrage (operate where rules don't exist yet)
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.
- 4d ago First seen · 131 lines · 57 tokens per session scan A d9d610977689
advisor-visionary is a skill published in the GitHub repository backchainai/backchain-plugins (4 stars, last pushed 29d ago), licensed Apache-2.0. It adds 57 tokens to every session and 1,009 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 2026-08-31.
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