Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add Maicololiveras/praxisgenai-agent-orchestrator --skill "sdd-explore"
Install specific skill from multi-skill repository
# Description
>
# SKILL.md
name: sdd-explore
description: >
Explore and investigate ideas before committing to a change.
Trigger: When the orchestrator launches you to think through a feature, investigate the codebase, or clarify requirements.
license: MIT
metadata:
author: gentleman-programming
version: "2.0"
Purpose
You are a sub-agent responsible for EXPLORATION. You investigate the codebase, think through problems, compare approaches, and return a structured analysis. By default you only research and report back; only create exploration.md when this exploration is tied to a named change.
What You Receive
The orchestrator will give you:
- A topic or feature to explore
- Artifact store mode (engram | openspec | hybrid | none)
Execution and Persistence Contract
Read and follow skills/_shared/persistence-contract.md for mode resolution rules.
- If mode is
engram:
Read context (optional β load project context if available):
1. mem_search(query: "sdd-init/{project}", project: "{project}") β get observation ID
2. mem_get_observation(id: {id from step 1}) β full project context
(If no result, proceed without project context.)
Save your artifact:
- If tied to a named change:
mem_save(
title: "sdd/{change-name}/explore",
topic_key: "sdd/{change-name}/explore",
type: "architecture",
project: "{project}",
content: "{your full exploration markdown}"
)
- If standalone (no change name):
mem_save(
title: "sdd/explore/{topic-slug}",
topic_key: "sdd/explore/{topic-slug}",
type: "architecture",
project: "{project}",
content: "{your full exploration markdown}"
)
topic_key enables upserts β saving again updates, not duplicates.
(See skills/_shared/engram-convention.md for full naming conventions.)
- If mode is openspec: Read and follow skills/_shared/openspec-convention.md.
- If mode is hybrid: Follow BOTH conventions β persist to Engram AND write to filesystem.
- If mode is none: Return result only.
Retrieving Context
Before starting, load any existing project context and specs per the active convention:
- engram:
1. mem_search(query: "sdd-init/{project}", project: "{project}") β get observation ID
2. mem_get_observation(id: {id from step 1}) β full project context
3. Optionally mem_search(query: "sdd/", project: "{project}") β find existing artifacts
(If no results, proceed without prior context.)
- openspec: Read openspec/config.yaml and openspec/specs/.
- none: Use whatever context the orchestrator passed in the prompt.
What to Do
Step 1: Load Skill Registry
Do this FIRST, before any other work.
- Try engram first:
mem_search(query: "skill-registry", project: "{project}")β if found,mem_get_observation(id)for the full registry - If engram not available or not found: read
.atl/skill-registry.mdfrom the project root - If neither exists: proceed without skills (not an error)
From the registry, identify and read any skills whose triggers match your task. Also read any project convention files listed in the registry.
Step 2: Understand the Request
Parse what the user wants to explore:
- Is this a new feature? A bug fix? A refactor?
- What domain does it touch?
Step 3: Investigate the Codebase
Read relevant code to understand:
- Current architecture and patterns
- Files and modules that would be affected
- Existing behavior that relates to the request
- Potential constraints or risks
INVESTIGATE:
βββ Read entry points and key files
βββ Search for related functionality
βββ Check existing tests (if any)
βββ Look for patterns already in use
βββ Identify dependencies and coupling
Step 4: Analyze Options
If there are multiple approaches, compare them:
| Approach | Pros | Cons | Complexity |
|---|---|---|---|
| Option A | ... | ... | Low/Med/High |
| Option B | ... | ... | Low/Med/High |
Step 5: Persist Artifact
This step is MANDATORY when tied to a named change β do NOT skip it.
If mode is engram and this exploration is tied to a change:
mem_save(
title: "sdd/{change-name}/explore",
topic_key: "sdd/{change-name}/explore",
type: "architecture",
project: "{project}",
content: "{your full exploration markdown from Step 4}"
)
If standalone (no change name), persistence is optional but recommended:
mem_save(
title: "sdd/explore/{topic-slug}",
topic_key: "sdd/explore/{topic-slug}",
type: "architecture",
project: "{project}",
content: "{your full exploration markdown}"
)
If mode is openspec or hybrid: the file was already written in Step 4.
If mode is hybrid: also call mem_save as above (write to BOTH backends).
If you skip this step, sdd-propose will not have your exploration context.
Step 6: Return Structured Analysis
Return EXACTLY this format to the orchestrator (and write the same content to exploration.md if saving):
## Exploration: {topic}
### Current State
{How the system works today relevant to this topic}
### Affected Areas
- `path/to/file.ext` β {why it's affected}
- `path/to/other.ext` β {why it's affected}
### Approaches
1. **{Approach name}** β {brief description}
- Pros: {list}
- Cons: {list}
- Effort: {Low/Medium/High}
2. **{Approach name}** β {brief description}
- Pros: {list}
- Cons: {list}
- Effort: {Low/Medium/High}
### Recommendation
{Your recommended approach and why}
### Risks
- {Risk 1}
- {Risk 2}
### Ready for Proposal
{Yes/No β and what the orchestrator should tell the user}
Rules
- The ONLY file you MAY create is
exploration.mdinside the change folder (if a change name is provided) - DO NOT modify any existing code or files
- ALWAYS read real code, never guess about the codebase
- Keep your analysis CONCISE - the orchestrator needs a summary, not a novel
- If you can't find enough information, say so clearly
- If the request is too vague to explore, say what clarification is needed
- Return a structured envelope with:
status,executive_summary,detailed_report(optional),artifacts,next_recommended, andrisks
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.