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 lenar-amirov/product-pipeline-public --skill problem-structuringgit clone --depth 1 https://github.com/lenar-amirov/product-pipeline-publicWrote 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/lenar-amirov/product-pipeline-public/problem-structuring)<a href="https://agentmods.dev/skills/lenar-amirov/product-pipeline-public/problem-structuring"><img src="https://agentmods.dev/badge/skills/lenar-amirov/product-pipeline-public/problem-structuring.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 5.1 | $0.00075 | $0.00681 |
| Opus 5 | $0.00037 | $0.00341 |
| Sonnet 5 | $0.00015 | $0.00136 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
problem-structuring 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 8d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Problem Structuring (MECE for the pipeline)
The structuring engine of /hypotheses (step 1) and the synthesis logic of
/validate (step 6). For a facilitated partner-led thinking session on a
problem that is not an initiative yet, use /deep-think.
MECE issue tree
- Root = the metric gap, phrased as a question with a number: "Why do only X% of [segment] reach [outcome]?" — from CONTEXT.md.
- First split — choose ONE dimension and stick to it: funnel stages, user segments, or jobs-to-be-done. Mixing dimensions is the #1 MECE violation.
- Branches must be mutually exclusive, collectively exhaustive. Test: every lost user lands in exactly one branch. If a case fits two branches — re-split. If no branch fits — add "other/unknown" and size it.
- Leaves localize the gap; hypotheses explain it. A leaf states WHERE
the metric is lost ("high-frequency viewers never click") with a size
estimate. For each sized leaf write 1–3 falsifiable hypotheses about WHY
("recommendations are irrelevant for this segment") and register them
(
hypotheses.py add). Don't collapse the two: one leaf often has competing explanations, and validating decides between them. 5–15 hypotheses total is the healthy range. - Stop drilling when a leaf is testable with one data pull or one experiment — deeper splits past that point are decoration.
- Name the blind spots explicitly — branches you cannot size with
current data. These become research questions for
/brief.
Pyramid principle (for validated findings)
Answer first, then grouped support, then evidence: conclusion → 2–3 insight
groups → data per group with sources from the registry. Used in
/validate outputs and gate presentations — never a data walk-through that
ends with "so, in conclusion".
80/20 prioritization
Size every branch before drilling: 80% of the metric gap usually sits in 2–3 leaves. Depth-first into the biggest branch beats breadth-first completeness. The registry's segment-size fields carry the sizing; priorities follow SIF = Severity × Impact × Frequency (each 1–10: how painful per affected user × share of the metric gap / segment size × how often the situation recurs) — format in output-formats.md.
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.
- 8d ago First seen · 56 lines · 75 tokens per session scan A 5ca4fed2f19c
problem-structuring is a skill published in the GitHub repository lenar-amirov/product-pipeline-public (13 stars, last pushed 18d ago), licensed MIT. It adds 75 tokens to every session and 681 once invoked, about $0.0004 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 2026-08-30.
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