accountable-engineerin…
Use explicit human checkpoints for architecture, security, rollout, or operational decisions.
Use MCP tools to trace behavior that spans multiple modules or history sources.
$ npx -y skills add jellydn/my-ai-tools --skill context-discovery --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/context-discoveryContext preview
The summary Claude sees to decide when to auto-load this skill.
Use MCP tools to trace behavior that spans multiple modules or history sources.
name: "context-discovery" description: "Use MCP tools to trace behavior that spans multiple modules or history sources." license: "MIT" compatibility: "cline, claude, opencode, amp, codex, gemini, cursor, pi" hint: "Use before starting work to understand codebase context via available MCP tools" user-invocable: true
Use this skill **before and during implementation** when:
Leverages available MCP tools to proactively discover context about the codebase, existing patterns, decisions, and related work. Instead of relying solely on grep/read cycles, it uses purpose-built discovery tools.
Find the relevant files using `fff`:
fff auth # Find auth-related files fff "*order*" # Find order-related files by pattern fff config # Find config files
Scan the results to identify the module structure. `fff` returns frecency-ranked results — the files you access most appear first.
Once you know the relevant files, use `sem` to understand the code's history and structure:
sem blame path/to/file.ts # See who changed each line and when sem diff main..HEAD -- path/ # See what changed in this area sem summary path/to/ # Get a summary of the module
`sem` provides entity-level diffs (function-level, not just file-level), making it easier to understand what actually changed.
Search past agent sessions for relevant context:
ctx search "auth implementation patterns" # Past work on auth ctx search "this module" path/to/module/ # Past discussions about this area ctx search "decision" "why did we" path/ # Past decision-making
`ctx` indexes agent sessions, so you can find past discussions, decisions, and patterns the agent has already encountered.
Query durable project knowledge:
qmd query "What architecture decisions exist for X?" qmd search "authentication patterns" qmd get ADR-001 # Get a specific ADR
`qmd` stores project learnings, ADRs, conventions, and gotchas that persist across sessions.
For large codebases, use the code graph to understand structure:
search_graph("OrderHandler") # Find the function/class
trace_path("OrderHandler", calls) # What does it call?
trace_path("OrderHandler", callers) # What calls it?
get_code_snippet("package.OrderHandler") # Read the source
get_architecture() # Project overviewLook up documentation for libraries and frameworks:
context7 "express.js middleware API reference" context7 "react useEffect cleanup pattern"
Where to look depends on what you need: ┌─────────────────────────────┬─────────────────┐ │ Need this │ Use this tool │ ├─────────────────────────────┼─────────────────┤ │ Find files by name/pattern │ fff │ │ Find what changed recently │ sem diff │ │ Find past agent discussions │ ctx search │ │ Find ADRs / project memory │ qmd query │ │ Understand code structure │ codebase-memory │ │ Look up external docs │ context7 │ │ Find related PRs │ github MCP │ └─────────────────────────────┴─────────────────┘
Portable, source-controlled configuration for AI coding assistants. my-ai-tools installs one consistent set of agent guidelines, skills, commands, hooks, MCP servers, and tool settings across supported AI coding assistants.
Use explicit human checkpoints for architecture, security, rollout, or operational decisions.
Record architecture decisions with ADRs — captures why, alternatives considered, and consequences
Use when monitoring an open GitHub PR for CI failures, review feedback, mergeability, and safe retries or fixes.
Inspect history and architecture for hidden constraints before risky cross-cutting changes.
Build an interactive report or experiment when the user asks to explore model capabilities.
Audit a diff for structural maintainability and unnecessary complexity.