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 agentmods add commands/jonase47/ccpr/p2-pocgit clone --depth 1 https://github.com/jonase47/ccprWhat 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 | $0.00000 | $0.01190 |
| Opus 5 | $0.00000 | $0.00595 |
| Sonnet 5 | $0.00000 | $0.00238 |
| Haiku 4.5 | $0.00000 | $0.00119 |
Grade A, and why
p2-poc 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 2d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/p2-poc – Technical Proof of Concept
Builds a minimal technical Proof of Concept for the riskiest technical area of the project. The goal is not a finished feature but proof that the planned approach fundamentally works – before investing in architecture and implementation.
Argument: $ARGUMENTS = [Technical risk area, e.g. "real-time synchronisation", "AI integration", "payment API"]
If provided: Build the PoC specifically for the named risk area. If not provided: Read ASSUMPTIONS.md and identify the most technically risky area yourself. If ASSUMPTIONS.md is missing, ask for the biggest technical risk of the project.
Execution
1. Read Context
Read the following files (if available):
- ASSUMPTIONS.md (technical Assumptions with highest priority)
- CONCEPT.md / FEATURES.md (What should the system do?)
- DISCOVERY.md (Feasibility assessment from Phase 0)
2. Delegate to system-architekt Agent (Lead)
Delegate PoC planning and technical evaluation to the system-architekt agent:
Plan and evaluate a Proof of Concept for the following risk area: $ARGUMENTS Context: [Insert relevant technical Assumptions from ASSUMPTIONS.md and requirements from CONCEPT.md]
Work out:
A. Define PoC Scope
- What exactly should the PoC prove? (1 clear question that must be answered)
- What is deliberately NOT part of the PoC? (Scope Boundary)
- Success criterion: How do we know the PoC is successful?
B. Choose Technical Approach
- Which technology/library/API should be tested?
- What alternatives exist and why is this approach preferred?
- Which dependencies (external APIs, SDKs, services) are needed?
C. Assessment after PoC Execution
- ✅ Feasible: Approach works, recommendation for architecture
- ⚠️ Feasible with limitations: What needs to be adjusted?
- ❌ Not feasible: Which alternative approach is recommended?
3. Delegate to senior-developer Agent (Support)
Delegate PoC implementation to the senior-developer agent:
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.
- 2d ago First seen · 103 lines · 0 tokens per session scan A f68d22940ade
p2-poc is a command published in the GitHub repository jonase47/ccpr (1 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,190 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.
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research
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help
Show everything the Obsidian Knowledge Agent can do, with every command and an example.
clean-check
Analyze code for cleanliness issues (unused code, comment quality, formatting, naming, complexity). Delegates to the code-cleanliness agent.
build
Mini spec-first development workflow for well-scoped implementation tasks with human in the loop.
firebase-publish
Create or reuse a Firebase project, configure Hosting, and deploy the current static site. Never deletes.