mikeng-io/agent-skills/context
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Checking that the SKILL.md file is still in the repository.
Analyze conversation context to classify what is being discussed, detect relevant domains from domain-registry, and determine optimal routing (parallel-workflow vs debate-protocol vs deep-council). Can be invoked directly or called by other skills as a pre-flight step.
Install
npx vskill@latest install mikeng-io/agent-skills/contextSKILL.md
Deep Context: Artifact Classifier and Smart Router
Execute this skill to analyze conversation context, detect relevant domains, and determine the optimal review routing strategy.
Execution Instructions
When invoked, analyze the current conversation and produce a context report.
Dependency Check
Before executing any step, verify the domain-registry is present:
Where [skills-root] is the parent of this skill's directory. Resolve with ls ../ from this skill's location.
If missing → stop immediately and output:
Dependency present → proceed to Step 1.
Step 1: Analyze Conversation
Extract from the conversation:
Step 2: Classify Artifact Type
Determine the primary artifact type from signals:
Artifact type selection:
- If signals from 2+ types →
mixed - If dominant signals clearly point to one type → that type
- Default →
code(most common)
Step 2.5: Enrich Context with DeepWiki (Optional)
If the artifact type is code or mixed and a GitHub repository can be identified, optionally invoke the deepwiki skill to get architectural context before domain selection. This enriches domain detection beyond what file names and conversation signals alone can provide.
When to invoke:
- Artifact type is
codeormixed - Minimal conversation context (low-confidence signals from Step 1)
- User asks about architectural intent, not just file contents
- Repository is large or unfamiliar — conversation signals may not surface all relevant domains
Invocation:
Pass the question: "What are the primary technical domains and architectural concerns of this codebase? What systems, components, or subsystems are involved?"
Use the returned answer to:
- Supplement
explicit_domainsbefore running domain-registry matching - Confirm or expand
artifact_typeif the codebase is more complex than conversation signals suggested - Add technical terms from the answer to the conversation signals for better domain trigger matching
If deepwiki returns availability: "unavailable" → skip and proceed to Step 3 with signals from Step 1 only. Non-blocking.
Step 3: Select Domains from Domain-Registry
Read the domain-registry files to match detected signals:
Where [skills-root] is the parent directory of the skill invoking this step. Resolve by navigating up from the current skill's location using ls or Glob if needed.
For each domain in the registry files:
- Check if any of the domain's
trigger_signalsappear in the conversation - Match against: file names, topics, concerns, explicit mentions, technical terms
- Select ALL matching domains (minimum 1, no maximum)
If no domains match clearly:
- For
codeartifact type → default toapi+testing - For
financial→ default tofinance - For
marketing→ default tomarketing - For
research→ default tocontent - For
creative→ default todesign+ux
Step 4: Determine Routing
Apply routing decision rules:
Routing priority: deep-council > debate-protocol > parallel-workflow
Step 5: Determine Confidence and Surface Missing Signals
Check each signal explicitly and record what could not be resolved:
Build the missing_signals list from any signal that resolved to false:
Derive overall confidence from the resolved signals:
Step 6: Produce Context Report
Output the context report:
missing_signals is the direct handoff to preflight — it tells preflight exactly which questions to ask, so preflight skips questions for signals that context already resolved.
Display this report to the user and/or return it to the calling skill.
Step 7: Save Artifact
Save context report to .outputs/context/{YYYYMMDD-HHMMSS}-context.md with YAML frontmatter:
Also save JSON companion: {YYYYMMDD-HHMMSS}-context.json
No symlinks. To find the latest artifact:
QMD Integration (optional):
Notes
- Pre-flight pattern: Other skills can call this at the start to determine routing before spawning agents
- Progressive enhancement: If domain-registry is not found, fall back to basic artifact type detection
- Non-blocking: Always produces a result, even with low confidence
- Model-agnostic: Works in any Claude, Gemini, Codex, or OpenCode context
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/context/SKILL.md
Popularity
- Repository stars
- 6
- 7-day trend score
- 10
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 7 May 2026, 5 months ago.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
- Claude Code
- Cursor
- GitHub Copilot
- Windsurf
- Codex
- Gemini CLI
- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
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