Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/nguyenthienthanh/aura-frognpx agentmods add skills/nguyenthienthanh/aura-frog/problem-solvingWrote 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/nguyenthienthanh/aura-frog/problem-solving)<a href="https://agentmods.dev/skills/nguyenthienthanh/aura-frog/problem-solving"><img src="https://agentmods.dev/badge/skills/nguyenthienthanh/aura-frog/problem-solving.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00018 | $0.00486 |
| Opus 5 | $0.00009 | $0.00243 |
| Sonnet 5 | $0.00004 | $0.00097 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
problem-solving 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.
What it actually says
AI-consumed reference. Optimized for Claude to read during execution. Human-readable explanation: see docs/architecture/HIERARCHICAL_PLANNING.md or docs/getting-started/ depending on topic.
Problem Solving Techniques
Use when stuck, need breakthrough, or evaluating approaches.
Decision Matrix
techniques[5]{symptom,technique,approach}:
"Tried everything / spiraling",Simplification Cascades,"Strip to minimal → rebuild one piece at a time → find breaking layer"
"Need creative idea",Collision-Zone Thinking,"Combine principles from unrelated domains into novel solution"
"Same issue keeps recurring",Meta-Pattern Recognition,"List all similar issues → find shared root → fix the pattern"
"Stuck in only-one-way thinking",Inversion Exercise,"State assumption → ask 'what if opposite?' → explore inverted approach"
"Will it scale?",Scale Game,"Test at 10x/100x/1000x → find breaking point → design for 10x actual need"
Simplification Cascades (Most Used)
- Remove ALL features except core
- Make it work with hardcoded values
- Add ONE thing back
- Repeat until issue appears
- Fix at that layer
Inversion Exercise
- State current assumption: "We must do X"
- Ask: "What if we never do X?" or "What if we do the opposite?"
- Explore — often reveals a better design
Scale Game
Test at 10x, 100x, 1000x current load. Find breaking point. Design for 10x actual need (not 1000x).
Quick Reference
Stuck → Simplify first. Creative block → Collision zones. Recurring → Meta-patterns. Tunnel vision → Invert. Scaling → Scale game.
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 · 55 lines · 18 tokens per session scan A e1e8b54457d3
problem-solving is a skill published in the GitHub repository nguyenthienthanh/aura-frog (24 stars, last pushed 3d ago), licensed MIT. It adds 18 tokens to every session and 486 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
biome
Biome formatting and linting standards. Use when configuring Biome (biome.json) for fast formatting, linting, and sorting imports. NOT for Prettier or ESLint configuration. Do NOT trigger for generic formatting/linting tasks unless Biome is explicitly requested.
adversarial-performance
Multi-pass adversarial performance audit for entire repositories. Combines structured profiling (Agent A) with adversarial stress-testing critique (Agent B) through iterative passes. Optimize repo/backend performance, hot-paths, build size, and server throughput. Do NOT use for frontend page load metrics or Lighthouse…
autonomous-dev
Harness for autonomous software development. Use when fixing or remediating known issues (including security vulnerabilities). Enforces lifecycle through alignment gates (PROJECT.md), adversarial generator/evaluator agents, and autonomous orchestration of project issues. NOT for setting up standalone CI/CD pipelines.
debugger-coordinator
Use when debugging complex issues involving multiple modalities (frontend + backend, API + UI, terminal + browser). Coordinates Hermes Agent's built-in debugger, browser, terminal, and vision tools for systematic root-cause analysis.
gsd-debug
Systematic debugging with persistent state across context resets.
gsd-ns-review
Route to the appropriate quality / review skill based on the user's intent. gsd-code-review-fix was absorbed by gsd-code-review --fix in #2790.