Agent skill

chain-patterns

Chain patterns for CC 2.1.71 pipelines — MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building multi-phase pipeline skills. Loaded via skills: field by pipeline skills (fix-issue, implement, brainstorm, verify). Not user-invocable.

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Install this agent skill to your Project

npx add-skill https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/chain-patterns

SKILL.md

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="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. 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="backend-system-architect",
  prompt="...", run_in_background=true, name="backend")
Agent(subagent_type="frontend-ui-developer",
  prompt="...", run_in_background=true, name="frontend")
Agent(subagent_type="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

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="backend-system-architect",
  prompt="Design the API schema", name="api-designer")

# Later, continue the same agent with new context
SendMessage(to="api-designer", content="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 recurring interval — session-scoped, dies on exit, 3-day auto-expiry. Unlike CronCreate (agent-initiated), /loop is user-invoked and can chain other skills.

text
# User types these — skills suggest them in "Next Steps"
/loop 5m gh pr checks 42                    # Watch CI after push
/loop 20m /ork:verify authentication        # Periodic quality gate
/loop 10m npm test -- --coverage            # Coverage drift watch
/loop 1h check deployment health at /api/health  # Post-deploy monitor

Key difference from CronCreate:

  • /loop can invoke skills: /loop 20m /ork:verify (CronCreate can't)
  • Both use the same underlying scheduler (50-task limit, 3-day expiry)
  • Skills use CronCreate for agent-initiated scheduling
  • Skills suggest /loop in "Next Steps" for user-initiated monitoring

When to suggest /loop in Next Steps:

  • After creating a PR → /loop 5m gh pr checks {pr_number}
  • After running tests → /loop 10m npm test
  • After deployment → /loop 1h check health at {endpoint}
  • After verification → /loop 30m /ork:verify {scope}

Rules

Rule Impact Key Pattern
rules/probe-before-use.md HIGH Always ToolSearch before MCP calls
rules/handoff-after-phase.md HIGH Write handoff JSON after every major phase
rules/checkpoint-on-gate.md MEDIUM Update state.json at every user gate

References

Load on demand with Read("${CLAUDE_SKILL_DIR}/references/<file>"):

File Content
mcp-detection.md ToolSearch probe pattern + capability map
handoff-schema.md JSON schema for .claude/chain/*.json
checkpoint-resume.md state.json schema + resume protocol
worktree-agent-pattern.md isolation: "worktree" usage guide
cron-monitoring.md CronCreate patterns for post-task health
experiment-journal.md Append-only TSV log for try/measure/keep-or-discard cycles
progressive-output.md Progressive output with run_in_background
sendmessage-resume.md SendMessage auto-resume (CC 2.1.77)
tier-fallbacks.md T1/T2/T3 graceful degradation

Related Skills

  • ork:implement — Full-power feature implementation (primary consumer)
  • ork:fix-issue — Issue debugging and resolution pipeline
  • ork:verify — Post-implementation verification
  • ork:brainstorm — Design exploration pipeline

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