p0-problem

A command that defines the main problem a project is meant to solve and identifies an initial target audience. It produces a problem statement and a first description of the people most affected.

In plain words
What is it for?
Use it to analyze a project idea, investigate root causes, and describe two or three early audience segments.
Why use it?
It gives the project a clear starting point before decisions about features or implementation are made. This reduces the chance of building for an unclear problem or audience.

Command

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.

agentmods
npx agentmods add commands/jonase47/ccpr/p0-problem
Clone the repo
git clone --depth 1 https://github.com/jonase47/ccpr
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,021 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00000 $0.01021
Opus 5 $0.00000 $0.00511
Sonnet 5 $0.00000 $0.00204
Haiku 4.5 $0.00000 $0.00102

Measured yesterday against content hash 52b8792aa7ef, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

p0-problem 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 yesterday.

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.

commands/p0-problem.md · 78 lines

How it starts

The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/p0-problem – Problem Definition & Target Audience Identification

Defines the core problem of a project or business idea and identifies the first rough Target Audience. The result is a clear problem statement that serves as the foundation for all subsequent phases.

Argument: $ARGUMENTS = [Project idea / problem area]

If provided: Use as the starting point for the problem analysis. If not provided: Ask the user for the project idea or problem area before starting. Do not make assumptions.

Execution

1. Read Context

Check whether the phase index docs/discovery/DISCOVERY.md and any existing detail files (e.g. docs/discovery/PROBLEM.md, MARKET.md, REGULATORY.md, or legacy NOTES.md) exist that can serve as input.

2. Delegate to konzeptor Agent

Delegate the main task to the konzeptor agent with the following instructions:

Analyse the following problem area: $ARGUMENTS

Work out:

  1. Problem Statement (1 precise sentence: Who has what problem with what consequence?)
  2. Problem Deep-Dive (Why does the problem exist? What are the root causes?)
  3. First Target Audience Sketch (Who suffers most from this problem? 2–3 rough audience segments)
  4. Non-Target Groups (Who is explicitly NOT meant?)
  5. Open Questions (What do you still need to find out to fully understand the problem?)

Ask targeted follow-up questions if the context is insufficient.

3. Write Detail File

Write the result to docs/discovery/PROBLEM.md (overwrite if it exists). Start with this YAML frontmatter:

---
phase: P0
subskill: problem
status: active   # skeleton | draft | active | frozen | archived | living
last_updated: <DD.MM.YYYY>
---

Below the frontmatter, structure the body with the headings: ## Problem Statement, ## Problem Deep-Dive, ## Target Audience Sketch, ## Non-Target Groups, ## Open Questions.

4. Update Phase Index

Update docs/discovery/DISCOVERY.md (create from the index template if missing — see ~/.claude/docs/PROJECT_PHASES.md):

  • Set **Last Updated:** <DD.MM.YYYY>.
  • In the Detail Files table: ensure a row exists for [PROBLEM.md](PROBLEM.md) with status complete.
  • Lift the 1-sentence problem statement into Key Decisions (e.g. - Problem: <...> → see PROBLEM.md).
  • If the deep-dive surfaced a critical open question, add a 1-liner under Open Risks / Open Questions referencing PROBLEM.md.

Read the full file on GitHub · 78 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. yesterday First seen · 78 lines · 0 tokens per session scan A 52b8792aa7ef

Subscribe to this mod's changes

p0-problem is a command published in the GitHub repository jonase47/ccpr (1 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,021 tokens. 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.