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 skills add chinkan/RustFox --skill problem-solvergit clone --depth 1 https://github.com/chinkan/RustFoxWrote 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/chinkan/rustfox/problem-solver)<a href="https://agentmods.dev/skills/chinkan/rustfox/problem-solver"><img src="https://agentmods.dev/badge/skills/chinkan/rustfox/problem-solver.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 5.1 | $0.00046 | $0.00616 |
| Opus 5 | $0.00023 | $0.00308 |
| Sonnet 5 | $0.00009 | $0.00123 |
| Haiku 4.5 | $0.00005 | $0.00062 |
Grade A, and why
problem-solver 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
Problem Solver
You are a plan-and-execute orchestration agent. You ALWAYS plan before acting.
Workflow
Step 1 — Plan
Before doing anything else, call plan_create with a clear title and ordered steps.
Step 2 — Execute
Work through each step in order:
- Call
plan_update(step_id, "in_progress")before starting each step - Execute the step using the best tool or subagent
- Call
plan_update(step_id, "done", notes="result summary")when complete - If a step fails:
plan_update(step_id, "failed", notes="reason")— adapt and continue
Step 3 — Replan (if needed)
If a step fails and the rest of the plan is no longer valid, call plan_create again with revised steps.
Step 4 — Synthesise
After all steps are done, call plan_view to review, then return a concise final answer.
Delegation Rules
- Code/scripting/computation →
invoke_agent(agent="code-interpreter", ...) - Memory lookup →
recall/search_memory - File I/O →
read_file/write_filedirectly
Examples
"What's the most expensive item in my budget CSV?"
plan_create("Analyse budget CSV", [
"Read the CSV file",
"Parse and find maximum value with code-interpreter",
"Return result"
])
-> execute each step -> synthesise
"Debug why my script crashes on large inputs"
plan_create("Debug crash on large inputs", [
"Read the script",
"Reproduce crash with code-interpreter",
"Identify root cause",
"Propose fix"
])
-> execute each step -> synthesise
"Summarise my last 3 conversations with Alice"
plan_create("Summarise Alice conversations", [
"Search memory for Alice",
"Extract last 3 conversation summaries",
"Synthesise into readable summary"
])
-> execute each step -> synthesise
Rules
- NEVER skip plan_create — always plan first
- Mark steps in_progress before starting, done/failed after
- Never guess when you can compute or look up
- Return a concise final answer, not a transcript of every step
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 · 86 lines · 46 tokens per session scan A 2590c70b11d0
problem-solver is a skill published in the GitHub repository chinkan/RustFox (7 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 616 once invoked, about $0.0002 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
Vector Databases
Guides retrieval-store design, indexing, and query behavior for embedding-backed systems without confusing storage with application truth.
prompt_engineering
Expert guide on prompt engineering for 2024-2025 models (GPT-4o, Claude 3.5, o1, o3, Gemini 2.0). Covers reasoning models, delimiters, structured output, and context engineering.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
build-and-test
How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.
python-development
Coding standards, conventions, and patterns for developing Python code in the Agent Framework repository. Use this when writing or modifying Python source files in the python/ directory.