skills/pack-discover/SKILL.md
Phase 1 of pack building. Identifies what pack to create, checks for existing packs, classifies the layer, and forks between community pack and project overlay paths. Triggers on: build a pack, create a pack, customize pack, make a skill pack.
npx skillsauth add xoai/sage pack-discoverInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Determine what pack to build and which path to follow.
Core Principle: Every pack starts from observed agent failures, not from documentation summaries. If you can't name a specific mistake agents make, you don't have a pack — you have a reference doc.
Ask the user:
"Are you building a shareable pack for a framework, or customizing an existing pack for your project's specific conventions?"
Ask:
Look in packs/ directory for an existing pack covering this framework.
Also check if a Layer 1 pack exists that this should build on.
Apply the three-layer test:
"Does this apply to any project in the domain regardless of framework?" Yes → Layer 1 (domain foundation). Examples: web, mobile, api, data.
"Does this apply to projects using this specific framework?" Yes → Layer 2 (framework pack). Examples: react, nextjs, vue, express.
"Does this apply only when these specific tools are used together?" Yes → Layer 3 (stack composition). Examples: nextjs+prisma, flutter+firebase.
Record: framework name, version, layer, L1 dependency (if L2/L3).
For project overlays, ask:
Ask the user to provide their context:
Record: target pack name, project context sources.
This is the most important step. Ask:
"What mistakes have you seen AI agents make with [framework]? Be specific — describe the bad code agents produce, not general problems."
If the user isn't sure, prompt with:
Record at least 3-5 specific agent failures. These become anti-patterns and drive pattern selection.
Save to .sage/pack-build/brief.md:
# Pack Brief
## Path: [community-pack / project-overlay]
## Framework: [name]
## Version: [version]
## Layer: [1/2/3]
## Dependencies: [L1 pack, etc.]
## Observed Agent Failures
1. [specific failure]
2. [specific failure]
3. [specific failure]
## Sources to Process
- [urls, docs, or "user will provide"]
## Project Context (overlay only)
- [conventions provided]
- [constraints provided]
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).