problem-structuring

problem-structuring is a skill for Claude Code from lenar-amirov/product-pipeline-public. It costs 75 tokens per session (681 once invoked), scanned A, original, MIT.

A problem-analysis method for breaking a product issue into separate, complete branches and testable explanations. MECE means branches do not overlap and together cover the whole problem; the pyramid principle puts the main conclusion first.

In plain words
What is it for?
Use it to build issue trees, prioritize likely causes, write hypotheses, and prepare structured validation work.
Why use it?
It turns a vague problem, such as a falling metric, into smaller questions that can be measured and checked.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Good fit Use it to build issue trees, prioritize likely causes, write hypotheses, and prepare structured validation work.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lenar-amirov/product-pipeline-public/problem-structuring
Install

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.

Any agent
npx skills add lenar-amirov/product-pipeline-public --skill problem-structuring
Clone the repo
git clone --depth 1 https://github.com/lenar-amirov/product-pipeline-public

Made for: Claude Code.

Wrote 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.

agentmods badge for problem-structuring

README.md
[![agentmods](https://agentmods.dev/badge/skills/lenar-amirov/product-pipeline-public/problem-structuring.svg)](https://agentmods.dev/skills/lenar-amirov/product-pipeline-public/problem-structuring)
Your own site
<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>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 681 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash 5ca4fed2f19c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

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.

.claude/skills/problem-structuring/SKILL.md · 56 lines

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

  1. Root = the metric gap, phrased as a question with a number: "Why do only X% of [segment] reach [outcome]?" — from CONTEXT.md.
  2. 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.
  3. 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.
  4. 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.
  5. Stop drilling when a leaf is testable with one data pull or one experiment — deeper splits past that point are decoration.
  6. 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.

Read the full file on GitHub · 56 lines

Changes

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.

  1. 8d ago First seen · 56 lines · 75 tokens per session scan A 5ca4fed2f19c

Subscribe to this mod's changes

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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