SkillKit is a package manager for AI coding-agent skills that installs skills from many sources and translates them between the different formats used by coding agents. It helps developers share skills across agents such as Claude Code, Cursor, Copilot, and Codex.
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 rohitg00/skillkit --skill hypothesis-testinggit clone --depth 1 https://github.com/rohitg00/skillkitWrote 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/rohitg00/skillkit/hypothesis-testing)<a href="https://agentmods.dev/skills/rohitg00/skillkit/hypothesis-testing"><img src="https://agentmods.dev/badge/skills/rohitg00/skillkit/hypothesis-testing/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/rohitg00/skillkit/hypothesis-testing"><img src="https://agentmods.dev/badge/skills/rohitg00/skillkit/hypothesis-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00103 | $0.01412 |
| Opus 5.5 | $0.00041 | $0.00565 |
| Sonnet 5.5 | $0.00021 | $0.00282 |
| Haiku 4.5 | $0.00010 | $0.00141 |
Grade A, and why
hypothesis-testing 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 1mo 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hypothesis-Driven Debugging
You are applying the scientific method to debugging. Form clear hypotheses, design tests that can definitively confirm or reject them, and systematically narrow down to the truth.
Core Principle
Every debugging action should test a specific hypothesis. Random changes are not debugging.
The Scientific Debugging Method
1. Observe - Gather Facts
Before forming hypotheses, collect observations:
- What exactly happens? (specific symptoms)
- When does it happen? (timing, frequency)
- Where does it happen? (environment, component)
- What changed recently? (code, config, data)
Write down observations objectively:
Observations:
- API returns 500 error on POST /orders
- Happens only when cart has > 10 items
- Started after deployment on 2024-01-15
- Works fine in staging environment
- Error logs show "connection refused" to inventory service
2. Hypothesize - Form Testable Theories
Examples (bad → good):
"Something is wrong with the network"→ "The inventory service connection pool is exhausted when processing orders with >10 items""There might be a race condition"→ "The order processing timeout (5s) is insufficient for large orders"
3. Predict - Define Expected Results
For each hypothesis, define what you expect to observe if it is true versus false:
Hypothesis: Connection pool exhausted for large orders
If TRUE:
- Active connections should hit max (20) during large orders
- Small orders should still work during this time
- Increasing pool size should fix the issue
If FALSE:
- Connection count stays well below max
- Small orders also fail during the issue
- Pool size change has no effect
4. Test - Experiment Systematically
Design tests that definitively confirm or reject:
Test Plan for Connection Pool Hypothesis:
1. Add connection pool monitoring
- Log active connections before/after each request
- Expected if true: Count reaches 20 during failures
2. Artificial stress test
- Send 5 large orders simultaneously
- Expected if true: Failures start when pool exhausted
3. Increase pool size to 50
- Repeat stress test
- Expected if true: Failures stop or threshold moves
4. Control test with small orders
- Send 20 small orders simultaneously
- Expected if true: No failures (faster processing)
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.
- 1mo ago First seen · 209 lines · 103 tokens per session scan A 7e465c38c2ef
hypothesis-testing is a skill published in the GitHub repository rohitg00/skillkit (1,556 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 103 tokens to every session and 1,412 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-30.
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