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 skills/future-cx/ai-architecture-toolkit/problem-to-solutionnpx skills add Future-CX/AI-Architecture-Toolkit --skill problem-to-solutiongit clone --depth 1 https://github.com/Future-CX/AI-Architecture-ToolkitWrote 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/future-cx/ai-architecture-toolkit/problem-to-solution)<a href="https://agentmods.dev/skills/future-cx/ai-architecture-toolkit/problem-to-solution"><img src="https://agentmods.dev/badge/skills/future-cx/ai-architecture-toolkit/problem-to-solution.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 | $0.00062 | $0.01771 |
| Opus 5 | $0.00031 | $0.00886 |
| Sonnet 5 | $0.00012 | $0.00354 |
| Haiku 4.5 | $0.00006 | $0.00177 |
Grade A, and why
problem-to-solution 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 4d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Problem To Solution
Quick Start
Use this skill to turn an ambiguous problem into a concise decision document for non-technical business stakeholders.
Store generated problem-to-solution briefs under the consuming repository's private lab root in problem-to-solutions/.
problem-to-solutions/<problem-slug>-problem-to-solution.md
Use templates/problem-to-solution-template.md as the output structure. Replace placeholders and drafting guidance with concrete content; do not leave unanswered sections unless the unknown is explicitly marked as TBD or an open question. Remove the ## Open Questions section when there are no open questions.
Problem-to-solution briefs are written for non-technical business stakeholders first. Architects and delivery teams should get enough context to act, but the opening sections must be easy for a business reader to scan, understand, and use in a decision meeting.
Do not write real-company decision context into this public toolkit repository.
Required Inputs
- Problem statement
- Decision needed
- Decision owner or forum
- Stakeholders and impacted groups
- Business or architecture context
- Constraints, assumptions, and non-goals
- Evaluation criteria
- Candidate solution options
- Recommendation and rationale after solution exploration is complete
- ADR link when the solution is decided
Workflow
- Clarify the problem, why it matters now, who needs to decide, and when a decision is needed.
- Ask for known constraints, non-goals, dependencies, risks, and stakeholder concerns when not already provided.
- Read related architecture documents when paths are provided.
- Validate terminology against
<private-lab-root>/GLOSSARY.mdwhen the brief uses domain-specific applications, capabilities, data objects, or jargon. Use../ubiquitous-language/SKILL.mdto clarify or update terminology before option brainstorming when important terms are missing, ambiguous, or overloaded. - Use
../grill-me/SKILL.mdto pressure-test the problem framing, assumptions, constraints, stakeholder tensions, and evaluation criteria before settling on solution options. - Brainstorm 2-5 realistic solution options. Include a "do nothing" or "defer" option only when it is a plausible decision path.
- Evaluate each option against the same criteria so comparisons are fair and decision-ready.
- Write balanced pros and cons for each option. Avoid strawman alternatives.
- Before writing a recommendation, use
../grill-me/SKILL.mdfor a dedicated exploration review of all options, pros, cons, assumptions, missing alternatives, hidden constraints, and stakeholder trade-offs. Ask questions one at a time and wait for feedback before concluding the review. - Revise the options, pros, cons, criteria, and open questions based on the grill-me session.
- Only after the grill-me exploration review is complete, recommend one option, or a staged combination, with clear rationale, trade-offs, and conditions for success.
- Create or update the problem-to-solution brief from
templates/problem-to-solution-template.md. Preserve the two opening tables: the document metadata table first, followed by theIntegration Overviewtable. - Run the readability and glossary compliance gate before finishing:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 107 lines · 62 tokens per session scan A 7e5b2ec86950
problem-to-solution is a skill published in the GitHub repository Future-CX/AI-Architecture-Toolkit (5 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 1,771 once invoked, about $0.0003 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 2026-08-31.
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