implement

Auto-generated from skills/implement/SKILL.md

Source: https://github.com/yonatangross/orchestkit

Implement Feature

Host-neutral workflow. Invoke by skill name (implement). Claude Code slash routing, YAML hook loaders, and .claude/chain live in skills/implement/references/claude-code.md.

Parallel subagent execution for feature implementation with scope control and reflection.

Quick Start

implement user authentication
implement --model=opus real-time notifications
implement dashboard analytics

Argument Resolution

Check the target first, before any other step. This skill forks and cannot see the conversation. Load Read("../../shared/rules/target-resolution.md"): if the target, flags stripped, is a bare pronoun or deictic (this, them, that one) or empty, return exactly TARGET_UNRESOLVED: <word> and stop. The caller resolves it and re-invokes.

FEATURE_DESC = "$ARGUMENTS"  # Full argument string, e.g., "user authentication"
# $ARGUMENTS[0] is the first token, $ARGUMENTS[1] second, etc. (CC 2.1.59)

# Model override detection (CC 2.1.72)
MODEL_OVERRIDE = None
for token in "$ARGUMENTS".split():
    if token.startswith("--model="):
        MODEL_OVERRIDE = token.split("=", 1)[1]  # "opus", "sonnet", "haiku", "fable"
        FEATURE_DESC = FEATURE_DESC.replace(token, "").strip()

Pass MODEL_OVERRIDE to all Agent() calls via model=MODEL_OVERRIDE when set. Accepts symbolic names (opus, sonnet, haiku, fable on harnesses whose Agent tool lists it; note fable is premium API spend after 2026-07-12) or full IDs (claude-opus-5-5) per CC 2.1.74.

Step -1: MCP Probe + Resume Check

Run BEFORE any other step. Detect available MCP servers and check for resumable state.

# Probe MCPs (parallel — all in ONE message):
# memory is alwaysLoad in .mcp.json (CC 2.1.121+, #1541) — probe below kept as fallback for older CC:
ToolSearch(query="select:mcp__memory__search_nodes")
ToolSearch(query="select:mcp__context7__resolve-library-id")

Write(".claude/chain/capabilities.json", JSON.stringify({
  "memory": <true if found>,
  "context7": <true if found>,
  "timestamp": now()
}))

# Resume check:
Read(".claude/chain/state.json")
# If exists and skill == "implement":
#   Read last handoff (e.g., 04-architecture.json)
#   Skip to current_phase
#   "Resuming from Phase {N} — architecture decided in previous session"
# If not: write initial state
Write(".claude/chain/state.json", JSON.stringify({
  "skill": "implement", "feature": FEATURE_DESC,
  "current_phase": 1, "completed_phases": [],
  "capabilities": capabilities,
  "budget_remaining_pct": 100  // advisory; see Budget Awareness below
}))