chain-patterns

# Chain Patterns

## Overview

Foundation patterns for CC 2.1.71 pipeline skills. This skill is loaded via the `skills:` frontmatter field — it provides patterns that parent skills follow.

## Pattern 1: MCP Detection (ToolSearch Probe)

Run BEFORE any MCP tool call. Probes are parallel and instant.

```python
# FIRST thing in any pipeline skill — all in ONE message:
ToolSearch(query="select:mcp__memory__search_nodes")
ToolSearch(query="select:mcp__context7__resolve-library-id")
ToolSearch(query="select:mcp__sequential-thinking__sequentialthinking")

# Store results for all phases:
Write(".claude/chain/capabilities.json", JSON.stringify({
  "memory": true_or_false,
  "context7": true_or_false,
  "sequential": true_or_false,
  "timestamp": "ISO-8601"
}))
```

**Usage in phases:**
```python
# BEFORE any mcp__memory__ call:
if capabilities.memory:
    mcp__memory__search_nodes(query="...")
# else: skip gracefully, no error
```

Load details: `Read("${CLAUDE_SKILL_DIR}/references/mcp-detection.md")`

## Pattern 2: Handoff Files

Write structured JSON after every major phase. Survives context compaction and rate limits.

```python
Write(".claude/chain/NN-phase-name.json", JSON.stringify({
  "phase": "rca",
  "skill": "fix-issue",
  "timestamp": "ISO-8601",
  "status": "completed",
  "outputs": { ... },           # phase-specific results
  "mcps_used": ["memory"],
  "next_phase": 5
}))
```

**Location:** `.claude/chain/` — numbered files for ordering, descriptive names for clarity.

Load schema: `Read("${CLAUDE_SKILL_DIR}/references/handoff-schema.md")`

## Pattern 3: Checkpoint-Resume

Read state at skill start. If found, skip completed phases.

```python
# FIRST instruction after MCP probe:
Read(".claude/chain/state.json")

# If exists and matches current skill:
#   → Read last handoff file
#   → Skip to current_phase
#   → Tell user: "Resuming from Phase N"

# If not exists:
Write(".claude/chain/state.json", JSON.stringify({
  "skill": "fix-issue",
  "started": "ISO-8601",
  "current_phase": 1,
  "completed_phases": [],
  "capabilities": { ... }
}))

# After each major phase:
# Update state.json with new current_phase and append to completed_phases
```

Load protocol: `Read("${CLAUDE_SKILL_DIR}/references/checkpoint-resume.md")`

## Pattern 4: Worktree-Isolated Agents

Use `isolation: "worktree"` when spawning agents that WRITE files in parallel.

```python
# Agents editing different files in parallel:
Agent(
  subagent_type="ork:backend-system-architect",
  prompt="Implement backend for: {feature}...",
  isolation="worktree",       # own copy of repo
  run_in_background=true
)
```

**When to use worktree:** Agents with Write/Edit tools running in parallel.

> **CC 2.1.157 worktree lifecycle:** `EnterWorktree` can switch between Claude-managed worktrees mid-session, and worktrees are left **unlocked** when the agent finishes — so `git worktree remove`/`prune` cleans them up without `--force`.

> **Session-aware worktree check (CC 2.1.145):** before parallel-worktree work, detect concurrent same-repo sessions with `claude agents --json` (filter by `working_dir`) rather than `ps`/`pgrep` — it returns `session_id`, `parent_agent_id`, `working_dir`, `awaiting_input`, and `elapsed` per live session, so you can tell *which* sessions share this repo.
**When NOT to use:** Read-only agents (brainstorm, assessment, review).

Load details: `Read("${CLAUDE_SKILL_DIR}/references/worktree-agent-pattern.md")`

## Pattern 5: CronCreate Monitoring

Schedule post-completion health checks that survive session end.

```python
# Guard: Skip cron in headless/CI (CLAUDE_CODE_DISABLE_CRON)
# if env CLAUDE_CODE_DISABLE_CRON is set, run a single check instead
CronCreate(
  schedule="*/5 * * * *",
  prompt="Check CI status for PR #{number}:
    Run: gh pr checks {number} --repo {repo}
    All pass → CronDelete this job, report success.
    Any fail → alert with failure details."
)
```

Load patterns: `Read("${CLAUDE_SKILL_DIR}/references/cron-monitoring.md")`

## Pattern 6: Progressive Output (CC 2.1.76)

Launch agents with `run_in_background=true` and output results as each returns — don't wait for all agents to finish. Gives ~60% faster perceived feedback.

```python
# Launch all agents in ONE message with run_in_background=true
Agent(subagent_type="ork:backend-system-architect",
  prompt="...", run_in_background=true, name="backend")
Agent(subagent_type="ork:frontend-ui-developer",
  prompt="...", run_in_background=true, name="frontend")
Agent(subagent_type="ork:test-generator",
  prompt="...", run_in_background=true, name="tests")

# As each agent completes, output its findings immediately.
# CC delivers background agent results as notifications —
# present each result to the user as it arrives.
# If any agent scores below threshold, flag it before others finish.
```

**Key rules:**
- Launch ALL independent agents in a single message (parallel)
- Output each result incrementally — don't batch
- Flag critical findings immediately (don't wait for stragglers)
- Background bash tasks are killed at 5GB output (CC 2.1.77) — pipe verbose output to files
- Parallel tool calls fail independently (CC 2.1.161) — a failed Bash no longer cancels siblings in the batch; add explicit per-call error handling instead of relying on cascade-abort

## Pattern 7: SendMessage Agent Resume (CC 2.1.77)

Continue a previously spawned agent using `SendMessage`. CC 2.1.77 auto-resumes stopped agents — no error handling needed.

```python
# Spawn agent
Agent(subagent_type="ork:backend-system-architect",
  prompt="Design the API schema", name="api-designer")

# Later, continue the same agent with new context
SendMessage(to="api-designer", message="Now implement the schema you designed")

# CC 2.1.77: SendMessage auto-resumes stopped agents.
# No need to check agent state or handle "agent stopped" errors.
# NEVER use Agent(resume=...) — removed in 2.1.77.
```

## Pattern 8: /loop Skill Chaining (CC 2.1.71)

`/loop` runs a prompt or skill on a