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 deciqAI/knowledge-skills --skill industry-learning-sprintgit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote 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/deciqai/knowledge-skills/industry-learning-sprint)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/industry-learning-sprint"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/industry-learning-sprint/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/deciqai/knowledge-skills/industry-learning-sprint"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/industry-learning-sprint.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 | $0.00121 | $0.02108 |
| Opus 5 | $0.00060 | $0.01054 |
| Sonnet 5 | $0.00024 | $0.00422 |
| Haiku 4.5 | $0.00012 | $0.00211 |
Grade A, and why
industry-learning-sprint 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 9d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Industry Learning Sprint
Overview
A structured 3-step process (financial reports → expert dialogue → unique view) for building a working industry mental model in approximately one week. The sequence is strict: financials before experts, experts before view formation. Financial reports reveal how an industry actually works stripped of marketing narrative; gross margin, capex pattern, and disclosed risk factors encode economic reality.
Neighbors: probabilistic-thinking (assign confidence intervals before expert conversations) · first-principles (stress-test the view after Step 3) · confirmation-bias (audit Step 3) · non-consensus-thinking (evaluate if the view is truly non-consensus) · narrow-gate-strategy (identify the leverage point for focused entry).
When to Use
Trigger conditions: Entering an industry for the first time (investor, founder, executive, advisor) · Evaluating an acquisition or partnership in an unfamiliar sector · Preparing for a high-stakes expert conversation with limited prep time · Producing an investment thesis or market entry recommendation under time pressure.
When NOT to use: Deep domain expertise already exists · Timeline under 48 hours (mark output as preliminary) · Industry is primarily informal/unregistered (financial reports will be unrepresentative).
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete industry target → run The Process directly.
- Coach mode: user unfamiliar with financial analysis → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- Reframe the goal: "You need one falsifiable hypothesis that would be contested by an insider — not comprehensive understanding."
- Check fit: confirm this is a new industry entry scenario, not a domain they already know deeply.
- Elicit their real case: "Which industry, and what decision are you trying to make at the end of this sprint?"
[WAIT — do not advance until user responds]
- Run The Process one step at a time with their input: start with financial structure mapping using their named industry.
[WAIT — do not advance until user responds]
- Close by naming the insight: "Your non-consensus view is [X] — here's why it would be contested by an insider."
[WAIT — do not advance until user responds]
What ships with it
2 files 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.
- 9d ago First seen · 121 lines · 121 tokens per session scan A 5da70903b8b6
industry-learning-sprint is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 121 tokens to every session and 2,108 once invoked, about $0.0006 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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