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 GiangGiangTran/ba-skills --skill insight-extractiongit clone --depth 1 https://github.com/GiangGiangTran/ba-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/gianggiangtran/ba-skills/insight-extraction)<a href="https://agentmods.dev/skills/gianggiangtran/ba-skills/insight-extraction"><img src="https://agentmods.dev/badge/skills/gianggiangtran/ba-skills/insight-extraction/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/gianggiangtran/ba-skills/insight-extraction"><img src="https://agentmods.dev/badge/skills/gianggiangtran/ba-skills/insight-extraction.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 | $0.00037 | $0.02234 |
| Opus 5 | $0.00018 | $0.01117 |
| Sonnet 5 | $0.00007 | $0.00447 |
| Haiku 4.5 | $0.00004 | $0.00223 |
Grade A, and why
insight-extraction 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 10d 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 — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Insight Extraction for BA
Data is plentiful. Insights are rare.
What is Insight Extraction?
Definition: Deeper analysis that goes beyond surface observations to uncover root causes, hidden opportunities, and non-obvious implications that change how you think about the problem.
Why it matters:
- Surface observation: "Users don't like feature X"
- Real insight: "Users avoid feature X because they don't trust automated decisions without override"
- Outcome: Different solution (add transparency/override controls, not replace feature)
Surface vs Insight:
- ❌ Observation: "Sales of Premium tier is growing"
- ✅ Insight: "Premium growth driven by large deals, not customer expansion"
- ✅ Implication: Sales model needs enterprise focus, not self-serve
When to Use:
- ✅ When surface answer doesn't feel complete
- ✅ When you want to understand root cause
- ✅ When looking for breakthrough opportunities
- ✅ When contradictions need explanation
- ✅ When you need to understand "why" deeply
5 Insight Extraction Techniques
1. The "Why" Ladder
Keep asking why until you reach real insight.
Surface: "Users don't use real-time notifications feature"
Why 1: Why don't they use it?
→ They don't know it exists
Why 2: Why don't they know it exists?
→ It's in a submenu they never open
Why 3: Why do they never open that submenu?
→ They don't know what's in there
Why 4: Why didn't they discover it?
→ No onboarding or discovery flow for advanced features
Why 5: Why is there no discovery flow?
→ Product team assumes users will explore
INSIGHT: Users don't explore. They need guided discovery.
NOT: "Feature is bad"
REAL INSIGHT: "Discovery mechanism is broken"
SOLUTION: Build discovery flow, don't kill feature
2. The Dichotomy Resolution
When two contradictory observations exist, find the insight.
Contradiction:
- Observation A: "Customers love the product" (high NPS)
- Observation B: "Churn is higher than industry average" (15%)
Surface explanation: "They love it but leave anyway" (doesn't make sense)
Insight Extraction:
Q1: Who loves it? → Early users, power users
Q2: Who churns? → Late-adopter segment
Q3: Why difference? → Onboarding struggles for late-adopters
Q4: Why onboarding struggles? → Product designed for power users
Q5: What's the insight? → Product has adoption cliff for non-technical users
INSIGHT: Not a product problem (power users love it)
It's an adoption/onboarding problem (late-adopters struggle)
SOLUTION: Redesign onboarding for non-technical users
Don't change core product
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.
- 10d ago First seen · 334 lines · 37 tokens per session scan A 3b617eec7251
insight-extraction is a skill published in the GitHub repository GiangGiangTran/ba-skills (6 stars, last pushed 7mo ago), licensed MIT. It adds 37 tokens to every session and 2,234 once invoked, about $0.0002 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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