tools/sage-claude-plugin/skills/problem-solving/SKILL.md
Systematic techniques for breaking through when stuck. Activate when: the agent has tried 3+ approaches without resolution, complexity is spiraling with growing special cases, a test keeps failing after multiple fix attempts, or the solution feels forced with no alternatives considered.
npx skillsauth add xoai/sage problem-solvingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Structured techniques for different types of stuck-ness. Each targets a specific pattern. Don't try harder — try differently.
Observable triggers — the agent or navigator detects:
If you notice any of these patterns in your own work, STOP and apply the matching technique below.
| Stuck Pattern | Technique | Reference |
|---------------|-----------|-----------|
| Growing special cases, 3+ implementations | Simplification | references/simplification.md |
| "Only one way," forced solution | Inversion | references/inversion.md |
| Works small, breaks at scale | Scale Testing | references/scale-testing.md |
| Can't isolate the cause | Minimal Reproduction | references/minimal-reproduction.md |
The problem has too many moving parts. Find one insight that eliminates multiple components.
Ask: "If [X] were true, what could I remove?"
Red flag: "Just need to add one more case..." (repeating)
You're trapped by an assumption you haven't questioned. Flip it.
Ask: "What if the opposite of my core assumption were true?"
Red flag: "There's only one way to do this"
The solution works at one scale but breaks at another. Test at extremes to expose fundamental design issues.
Ask: "What happens at 1000x? What happens at 1/1000th?"
Red flag: "Should scale fine" (without evidence)
You can't isolate the cause because there's too much context. Strip the problem to the smallest case that still exhibits the issue.
Ask: "What's the simplest possible reproduction?"
Red flag: "It only happens in the full system" (usually means the reproduction isn't minimal enough)
references/.learning). This prevents
future agents from hitting the same wall.Some problems need multiple techniques in sequence:
Communication style: Diagnostic precision. Show the reasoning chain — what was tried, what failed, what the technique revealed, and why the new approach is better.
Good problem-solving output:
Before claiming a breakthrough, check:
tools
Captures agent mistakes, corrections, and discovered gotchas so they are not repeated. Use when: (1) a command or operation fails unexpectedly, (2) the user corrects the agent, (3) the agent discovers non-obvious behavior through debugging, (4) an API or tool behaves differently than expected, (5) a better approach is found for a recurring task. Also searches past learnings before starting tasks to avoid known pitfalls. Activate alongside the sage-memory skill — they share the same MCP backend but serve different purposes (sage-memory = codebase knowledge, sage-self-learning = agent mistakes and gotchas).
development
Typed knowledge graph stored in sage-memory. Use when creating or querying structured entities (Person, Project, Task, Event, Document), linking related objects, checking dependencies, planning multi-step actions as graph transformations, or when skills need to share structured state. Trigger on "remember that X is Y", "what do I know about", "link X to Y", "show dependencies", "what blocks X", entity CRUD, cross-skill data access, or any request involving structured relationships between things.
tools
Integrates sage-memory into Sage workflows. Teaches the agent when to remember (store findings during work), when to recall (search memory at session start and task start), and how to learn (structured knowledge capture via sage learn). Use when the user mentions memory, remember, recall, learn, capture knowledge, onboard to codebase, or when starting any session where sage-memory MCP tools are available.
tools
Captures agent mistakes, corrections, and discovered gotchas so they are not repeated. Use when: (1) a command or operation fails unexpectedly, (2) the user corrects the agent, (3) the agent discovers non-obvious behavior through debugging, (4) an API or tool behaves differently than expected, (5) a better approach is found for a recurring task. Also searches past learnings before starting tasks to avoid known pitfalls. Activate alongside the sage-memory skill — they share the same MCP backend but serve different purposes (sage-memory = codebase knowledge, sage-self-learning = agent mistakes and gotchas).