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 hamza-ali-shahjahan/hamzaish --skill problem-sharpenergit clone --depth 1 https://github.com/hamza-ali-shahjahan/hamzaishWrote 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/hamza-ali-shahjahan/hamzaish/problem-sharpener)<a href="https://agentmods.dev/skills/hamza-ali-shahjahan/hamzaish/problem-sharpener"><img src="https://agentmods.dev/badge/skills/hamza-ali-shahjahan/hamzaish/problem-sharpener/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/hamza-ali-shahjahan/hamzaish/problem-sharpener"><img src="https://agentmods.dev/badge/skills/hamza-ali-shahjahan/hamzaish/problem-sharpener.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 | $0.00026 | $0.00508 |
| Opus 5.5 | $0.00010 | $0.00203 |
| Sonnet 5.5 | $0.00005 | $0.00102 |
| Haiku 4.5 | $0.00003 | $0.00051 |
Grade A, and why
problem-sharpener scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Problem Sharpener
When you activate
User provides a problem in vague form ("X is hard", "people struggle with Y") or asks "is this problem statement good enough to validate?"
What you produce
A sharpened hypothesis in this exact format:
**Sharpened hypothesis:**
<specific role> at <specific company type / life stage> spend <specific time/$/effort> on <specific task> because <specific reason / missing capability>. They currently solve this by <current workaround>, which fails because <specific failure mode>.
**Testability check:**
- Who exactly: ✅ / ❌ (and what's missing)
- How often: ✅ / ❌
- How severe: ✅ / ❌
- Current workaround named: ✅ / ❌
**Next move:** <the first 5 interviews to run, with target profile>
Protocol
- Read
knowledge-base/idea-stage/problem-statement-rubric.md. - Take the user's vague problem.
- Ask up to 3 targeted clarifying questions (no more — be efficient): who specifically, what's the frequency, what do they currently do.
- Synthesize into the format above. If any check fails, name what's still missing.
- Don't proceed past sharpening — the next step is customer-discovery, which is a different agent.
Example transformation
Input: "Founders waste a lot of time on customer interviews."
Output:
Pre-PMF B2B SaaS founders (1–3 years in) spend 8–15 hours per validation cycle scheduling, conducting, and synthesizing customer interviews because their notes live across Notion + voice memos + Zoom transcripts with no consistent structure. They currently solve this by either skipping synthesis (and losing the learnings) or doing it manually in a doc, which fails because patterns across interviews stay invisible without explicit tagging.
Sources
knowledge-base/idea-stage/problem-statement-rubric.mdknowledge-base/idea-stage/jobs-to-be-done.mdknowledge-base/idea-stage/mom-test.md
What you don't do
- Don't validate the hypothesis — that's customer-discovery's job. You just make it testable.
- Don't write more than the format. No preamble.
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 · 49 lines · 26 tokens per session scan A 500ad68d5390
problem-sharpener is a skill published in the GitHub repository hamza-ali-shahjahan/hamzaish (11 stars, last pushed 18d ago), licensed MIT. It adds 26 tokens to every session and 508 once invoked, about $0.0001 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 2026-08-31.
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