pipeline-executor

name	pipeline-executor
description	Declarative pipeline execution with stages, conditions, data passing, and rollback. Use when executing multi-stage workflows.
version	1
input_schema	{"required":[{"name":"pipeline_name","type":"string","description":"Name of pipeline to execute (maps to configurations/pipelines/{name}.yml)"},{"name":"task_id","type":"string","description":"Task ID for state tracking"}],"optional":[{"name":"initial_inputs","type":"object","description":"Initial data to pass to first stage"},{"name":"skip_stages","type":"string[]","description":"Stage IDs to skip"},{"name":"start_from_stage","type":"string","description":"Stage ID to resume from (for retries)"}]}
output_schema	{"required":[{"name":"status","type":"string","description":"Pipeline result - success, failed, cancelled"},{"name":"stages_executed","type":"object[]","description":"List of executed stages with outcomes"}],"optional":[{"name":"artifacts","type":"object","description":"Collected outputs from all stages"},{"name":"failed_stage","type":"string","description":"Stage ID where failure occurred"},{"name":"rollback_performed","type":"boolean","description":"Whether rollback was triggered"}]}
pre_checks	[{"description":"Pipeline file exists","validation":"configurations/pipelines/{pipeline_name}.yml exists"},{"description":"Task ID is valid format","validation":"task_id matches pattern ^task-[a-z0-9]{6}-[a-z0-9-]+$"}]
post_checks	[{"description":"Status is valid","validation":"status in [success, failed, cancelled]"},{"description":"All stages accounted for","validation":"stages_executed includes all non-skipped stages"}]
cacheable	false

Pipeline Executor

Declarative pipeline execution with stages, conditions, data passing, and rollback for the org-level orchestration system.

Overview

This skill provides the execution protocol for running multi-stage pipelines. It handles:

1. Pipeline loading (YAML parsing and validation)
2. Condition evaluation (stage gating based on task metadata)
3. Stage execution (agent invocation with proper mode)
4. Inter-stage data passing (artifact storage and retrieval)
5. User approval gates (checkpoint pausing)
6. Retry handling (backoff strategies per stage)
7. Rollback orchestration (reverse order stage undo)

Pipeline YAML Schema

Pipelines are defined in configurations/pipelines/{name}.yml:

name: string           # unique pipeline identifier
description: string    # what this pipeline does
version: number        # schema version
max_retries: number    # pipeline-level retry cap (default 5)

stages:
  - id: string                 # unique within pipeline
    agent: string              # agent name (tech-lead for execution, org agents for review)
    mode: plan | agent         # execution mode
    description: string        # what this stage does
    inputs: string[]           # artifact keys from prior stages
    outputs: string[]          # artifact keys this stage produces
    condition: string          # evaluated against task metadata (optional)
    requires_approval: boolean # pause for user (default false)
    timeout_minutes: number    # max duration (default 30)
    retry:                     # stage-level retry config (optional)
      max_attempts: number     # default 3
      backoff: exponential | linear | fixed
      initial_delay_seconds: number
    rollback:                  # how to undo this stage (optional)
      strategy: git_revert | file_restore | memory_cleanup | manual
      description: string
    skill: string              # skill to load for this stage (optional)

Field Descriptions

Field	Required	Description
id	Yes	Unique identifier within the pipeline
agent	Yes	Agent to execute this stage
mode	Yes	plan for read-only review, agent for implementation
description	Yes	Human-readable purpose
inputs	No	Artifact keys from prior stages to load
outputs	No	Artifact keys this stage will produce
condition	No	Expression that must evaluate true for stage to run
requires_approval	No	If true, pause and wait for user confirmation
timeout_minutes	No	Maximum stage duration (default 30)
retry	No	Stage-level retry configuration
rollback	No	Strategy for undoing this stage on failure
skill	No	Skill to load before executing the stage

Pipeline Loading Protocol

Input: pipeline_name
Output: pipeline_config, validation_result

1. Resolve pipeline path:
   - Path: configurations/pipelines/{pipeline_name}.yml
   - If not found → ERROR: "Pipeline '{name}' not found"

2. Parse YAML:
   - Load file contents
   - Parse as YAML
   - If parse error → ERROR: "Invalid YAML in pipeline '{name}'"

3. Validate schema:
   - Check required fields: name, description, version, stages
   - Check stages array is non-empty
   - For each stage:
     - Validate required: id, agent, mode, description
     - Validate mode in [plan, agent]
     - Validate inputs reference existing outputs from prior stages
     - Validate rollback.strategy if present

4. Check agent availability:
   - For each unique agent in stages:
     - Check agent exists (org-level or project-level)
     - If missing → ERROR: "Agent '{agent}' not found for stage '{id}'"

5. Return validated config

Validation Rules

validation_rules:
  stage_ids:
    - Must be unique within pipeline
    - Must match pattern: ^[a-z][a-z0-9_]*$
  
  input_references:
    - Each input must match an output from a prior stage
    - First stage can only use initial_inputs
  
  mode_restrictions:
    - Org agents (cto, vp-*, ciso, staff-engineer, sre-lead) typically use mode: plan; `tech-lead` (org-tier execution orchestrator) and `code-reviewer` use mode: agent. `cto` may use mode: agent for orchestration.
  
  retry_config:
    - max_attempts: 1-10
    - backoff: exponential, linear, or fixed
    - initial_delay_seconds: 1-300
  
  rollback_strategy:
    - git_revert: Revert commits made during stage
    - file_restore: Restore files from backup
    - memory_cleanup: Remove memory entries
    - manual: Describe steps for user

Condition Evaluation

Conditions gate stage execution based on task metadata.

Evaluation Protocol

Input: condition_string, task_metadata
Output: should_execute (boolean)

1. Parse condition:
   - Extract variable references
   - Build expression tree

2. Bind variables from task_metadata:
   - complexity: "low" | "medium" | "high"
   - task_type: string (from classification)
   - tags: string[] (task tags)
   - always_include: string[] (agents that must run)
   - stage_outputs: object (outputs from prior stages)

3. Evaluate expression:
   - Comparison operators: ==, !=, in, not in
   - Logical operators: and, or, not
   - Parentheses for grouping

4. Return boolean result

Condition Examples

# Run only for high complexity tasks
condition: "complexity == 'high'"

# Run for security-related tasks
condition: "task_type == 'security'"

# Run if CISO review was requested
condition: "'ciso' in always_include"

# Run for high or medium complexity
condition: "complexity in ['high', 'medium']"

# Compound condition
condition: "task_type == 'feature' and complexity == 'high'"

# Check prior stage output
condition: "stage_outputs.architecture_review.risks_identified > 0"

Condition Variables

Variable	Type	Source	Description
complexity	string	Task classification	"low", "medium", "high"
task_type	string	Task classification	Classified task type
tags	string[]	Task metadata	Tags from task
always_include	string[]	Routing	Agents that must participate
stage_outputs	object	Prior stages	Outputs keyed by stage ID

Stage Execution Protocol

Execute a single stage within the pipeline.

Execution Flow

Input: stage_config, task_metadata, prior_artifacts
Output: stage_result, artifacts

1. Evaluate condition:
   - If condition present and evaluates false:
     - Log: "Stage '{id}' skipped: condition not met"
     - Return: {status: skipped, reason: condition_false}

2. Load stage's agent:
   - Org agents: ~/.config/opencode/agents/{agent}.md
   - Project agents: {repo}/.opencode/agents/{agent}.md
   - If not found → ERROR

3. Run pre-action rule enforcement:
   - Load rule-enforcement skill
   - Execute: pre_action_check(agent, stage.description, task_id)
   - If blocked → stage fails with rule violation

4. Load stage skill (if specified):
   - Read skill from .opencode/skills/{skill}/SKILL.md
   - Attach to agent context

5. Prepare inputs:
   - For each input key in stage.inputs:
     - Load artifact from session.current/artifact-{task_id}-{source_stage}.md
     - Extract relevant output keys
     - Cap at 500 tokens per artifact
   - Merge with task context

6. Execute agent:
   - Switch to stage.mode (plan or agent)
   - Pass: task description, inputs, stage.description
   - Monitor: timeout_minutes
   - Capture: outputs, errors, metrics

7. Handle result:
   - If success:
     - Store outputs as artifacts
     - Update task state
   - If timeout:
     - Check retry config
     - Either retry or fail
   - If error:
     - Check retry config
     - Either retry or trigger rollback

8. Run post-action rule enforcement:
   - Execute: post_action_check(agent, stage_result, task_id)
   - Log any violations

9. Log metrics:
   - Duration, token usage, retry count
   - Write to session.current/metrics-{task_id}.md

10. Return stage_result with:
    - status: success | failed | skipped | timeout
    - outputs: artifact keys produced
    - duration_ms: execution time
    - retry_count: attempts made

Mode Execution

plan_mode:
  description: Read-only review and analysis
  allowed_actions:
    - Read files
    - Search codebase
    - Query memory
    - Generate recommendations
  forbidden_actions:
    - Write files
    - Execute shell commands
    - Modify memory

agent_mode:
  description: Full implementation capabilities
  allowed_actions:
    - All read operations
    - Write files
    - Execute shell commands
    - Modify memory
    - Spawn project agents (dev-*/reviewer-*/sme-*/qa-*/devops) within target_repo per-task (tech-lead only)

Inter-Stage Data Passing

Artifacts flow between stages through the memory system.

Artifact Storage

Location: session.current/artifact-{task_id}-{stage_id}.md

---
entity_name: session.current.artifact.{task_id}.{stage_id}
namespace: session.current
category: artifact
task_id: task-a1b2c3-feature-name
stage_id: plan
agent: cto
status: success
summary: "Created 4-phase implementation plan with security considerations"
output_keys: [implementation_plan]
token_count: 450
created_at: 2026-04-04T10:15:00Z
tags: [artifact, plan, cto]
---

## implementation_plan

### Phase 1: Foundation
- Set up authentication middleware
- Create session management
- Estimated steps: 3

### Phase 2: Integration
- Connect OAuth providers
- Handle token refresh
- Estimated steps: 4

### Phase 3: Security Hardening
- Add rate limiting
- Implement audit logging
- Estimated steps: 3

### Phase 4: Testing
- Unit tests for auth flows
- Integration tests
- Estimated steps: 2

Artifact Retrieval Protocol

Input: task_id, stage_id, required_keys[]
Output: artifact_data

1. Resolve artifact path:
   - Path: session.current/artifact-{task_id}-{stage_id}.md

2. Load artifact:
   - Read file
   - Parse frontmatter
   - Extract body content

3. Filter by required keys:
   - If required_keys specified:
     - Extract only matching sections
   - Else:
     - Use full artifact body

4. Apply token limit:
   - If content > 500 tokens:
     - Truncate with summary
     - Add: "[truncated - {n} tokens omitted]"

5. Return artifact_data with:
   - metadata: frontmatter fields
   - content: filtered body
   - token_count: actual tokens returned

Artifact Token Limits

Per-artifact limit: 500 tokens
Per-stage input limit: 1500 tokens total (across all inputs)
Truncation strategy:
  1. Keep summary section intact
  2. Keep most recent content
  3. Truncate middle sections
  4. Add truncation marker

User Approval Gates

Stages with requires_approval: true pause for user confirmation.

Approval Protocol

Input: stage_config, stage_preview
Output: user_decision (proceed | abort)

1. Check requires_approval:
   - If false → skip, proceed automatically

2. Generate approval prompt:
   - Stage: {stage.id} - {stage.description}
   - Agent: {stage.agent}
   - Mode: {stage.mode}
   - Inputs: {summarized inputs}
   - Expected outputs: {stage.outputs}

3. Display to user:
   ---
   ## Approval Required: Stage '{stage.id}'
   
   **Description:** {stage.description}
   **Agent:** {stage.agent}
   **Mode:** {stage.mode}
   
   ### Inputs
   {input summaries}
   
   ### Expected Outputs
   {output list}
   
   Type `proceed` to continue or `abort` to cancel pipeline.
   ---

4. Wait for user response:
   - "proceed" | "yes" | "continue" → proceed
   - "abort" | "cancel" | "no" → abort
   - Other → re-prompt

5. Return decision:
   - proceed → continue pipeline
   - abort → trigger cancellation flow

Abort Handling

On abort:
1. Mark pipeline as cancelled
2. Record abort point: stage.id
3. Check if rollback needed:
   - If prior stages modified state → offer rollback
   - If read-only → clean exit
4. Update task state to cancelled
5. Log: "Pipeline cancelled at stage '{id}' by user"

Retry Handling

Stage-level retry with configurable backoff.

Retry Configuration

retry:
  max_attempts: 3        # 1-10, default 3
  backoff: exponential   # exponential, linear, fixed
  initial_delay_seconds: 5

Backoff Strategies

exponential:
  delay(n) = initial_delay * 2^(n-1)
  Example (initial=5s): 5s, 10s, 20s, 40s...

linear:
  delay(n) = initial_delay * n
  Example (initial=5s): 5s, 10s, 15s, 20s...

fixed:
  delay(n) = initial_delay
  Example (initial=5s): 5s, 5s, 5s, 5s...

Retry Protocol

Input: stage_config, error, attempt_count
Output: retry | fail

1. Check retry eligibility:
   - If no retry config → fail immediately
   - If attempt_count >= max_attempts → fail

2. Check error type:
   - Retryable: timeout, transient_error, partial_failure
   - Non-retryable: validation_error, user_abort, security_violation
   - If non-retryable → fail immediately

3. Calculate delay:
   - Apply backoff strategy
   - Cap at 5 minutes maximum

4. Wait for delay

5. Re-execute stage:
   - Increment attempt_count
   - Log: "Retry {n}/{max} for stage '{id}'"
   - Execute stage

6. On success → continue pipeline
7. On failure → repeat from step 1

Retry Logging

retry_log:
  stage_id: implement
  attempts:
    - attempt: 1
      status: failed
      error: timeout
      duration_ms: 60000
      timestamp: 2026-04-04T10:30:00Z
    - attempt: 2
      status: failed
      error: timeout
      duration_ms: 60000
      timestamp: 2026-04-04T10:31:10Z
    - attempt: 3
      status: success
      duration_ms: 45000
      timestamp: 2026-04-04T10:32:25Z
  total_duration_ms: 165000
  final_status: success

Rollback Protocol

On failure, undo completed stages in reverse order.

Rollback Strategies

Strategy	Description	Actions
git_revert	Revert git commits	Find commits from stage, create revert commit
file_restore	Restore file backups	Copy from backup location
memory_cleanup	Remove memory entries	Delete artifacts, task state
manual	User instructions	Display rollback steps

Rollback Execution

Input: failed_stage_index, completed_stages[]
Output: rollback_result

1. Build rollback list:
   - Filter completed_stages where rollback is defined
   - Reverse order (most recent first)

2. For each stage to rollback:
   a. Log: "Rolling back stage '{id}'"
   
   b. Execute rollback strategy:
      
      git_revert:
        - Find commits with message containing task_id and stage_id
        - Create revert commit
        - Log: "Reverted {n} commits"
      
      file_restore:
        - Find backup at session.current/backup-{task_id}-{stage_id}/
        - Restore files to original locations
        - Log: "Restored {n} files"
      
      memory_cleanup:
        - Delete artifact files for this stage
        - Update task state
        - Log: "Cleaned memory entries"
      
      manual:
        - Display rollback.description to user
        - Wait for user confirmation
        - Log: "Manual rollback acknowledged"
   
   c. Record rollback result

3. Update pipeline state:
   - rollback_performed: true
   - rollback_stages: [list of rolled back stage ids]
   - rollback_status: success | partial | failed

4. Return rollback_result

Rollback State Tracking

rollback_state:
  triggered_by: implement
  trigger_error: validation_error
  stages_to_rollback: [implement, plan]
  rollback_results:
    - stage: implement
      strategy: git_revert
      status: success
      commits_reverted: 2
    - stage: plan
      strategy: memory_cleanup
      status: success
      entries_cleaned: 3
  overall_status: success
  completed_at: 2026-04-04T10:40:00Z

Full Pipeline Execution Flow

Complete pipeline execution from start to finish.

Execution Algorithm

Input: pipeline_name, task_id, initial_inputs, options
Output: pipeline_result

1. Load and validate pipeline:
   - Read configurations/pipelines/{pipeline_name}.yml
   - Validate schema and agent availability
   - If error → return {status: failed, error: validation}

2. Initialize task state:
   - Create/update session.current/task-{task_id}.md
   - Set: pipeline, state=running, stages_remaining
   - Store initial_inputs as artifact

3. Determine starting point:
   - If start_from_stage → find stage index
   - Else → start at index 0

4. Execute stages sequentially:
   For each stage from start_index:
   
   a. Check skip list:
      - If stage.id in skip_stages → skip
   
   b. Execute stage:
      - Follow Stage Execution Protocol
   
   c. Handle result:
      - success → store artifacts, continue
      - skipped → log, continue
      - failed:
        - If retries available → retry
        - Else → trigger rollback, exit loop
   
   d. Check approval gate:
      - If requires_approval → wait for user
      - If abort → trigger cancellation

5. Finalize:
   - Merge all stage outputs
   - Update task state to success/failed/cancelled
   - Generate pipeline summary

6. Return pipeline_result:
   - status: success | failed | cancelled
   - stages_executed: [{id, status, duration}...]
   - artifacts: merged outputs
   - failed_stage: id (if failed)
   - rollback_performed: boolean

Pipeline State Machine

States:
  initializing → Pipeline loaded, task state created
  running      → Actively executing stages
  waiting      → Paused at approval gate
  retrying     → Retrying failed stage
  rolling_back → Executing rollback
  success      → All stages completed
  failed       → Stage failed, rollback complete
  cancelled    → User aborted

Transitions:
  initializing → running      (start execution)
  running      → waiting      (approval gate reached)
  waiting      → running      (user approved)
  waiting      → cancelled    (user aborted)
  running      → retrying     (stage failed, retries available)
  retrying     → running      (retry succeeded)
  retrying     → rolling_back (retries exhausted)
  running      → rolling_back (non-retryable failure)
  rolling_back → failed       (rollback complete)
  running      → success      (all stages done)

Cross-Stage Feedback Protocol

Review stages can send structured feedback back to implementation stages for automated fixing.

Feedback Loop Configuration

Stages can declare a feedback_loop config:

stages:
  - id: quality_review
    agent: staff-engineer
    mode: plan
    description: Review code quality
    inputs: [code_changes]
    outputs: [quality_review, feedback_items]
    feedback_loop:
      target_stage: implement       # Stage to re-run with feedback
      max_iterations: 2             # Hard cap before escalation
      feedback_format: structured   # How feedback is passed

Feedback Loop Execution

After a review stage completes:

1. Check for feedback_items in stage outputs:
   if stage.outputs contains "feedback_items":
     feedback_items = stage.outputs.feedback_items

2. Check for blocking items:
   blocking_items = filter(feedback_items, severity == "blocking")
   
   if len(blocking_items) == 0:
     Log: "[pipeline] stage={id} feedback=none, continuing"
     Continue to next stage

3. Invoke cross-stage-feedback skill:
   result = invoke("cross-stage-feedback", {
     source_stage: stage.id,
     target_stage: stage.feedback_loop.target_stage,
     feedback_items: feedback_items,
     task_id: task_id,
     iteration: current_feedback_iteration,
     max_iterations: stage.feedback_loop.max_iterations
   })

4. Handle result:
   
   If result.action == "re_implement":
     Log: "[pipeline] feedback_loop source={stage.id} target={target} iteration={n}"
     
     # Re-run target stage with feedback as additional input
     target_stage = find_stage(stage.feedback_loop.target_stage)
     target_stage.additional_input = {
       feedback_brief: result.implementation_brief,
       feedback_source: stage.id,
       iteration: result.iteration_count
     }
     
     # Jump back to target stage in pipeline
     current_stage_index = index_of(target_stage)
     Continue from target_stage
   
   If result.action == "escalate":
     Log: "[pipeline] feedback_loop escalated source={stage.id} iterations={n}"
     
     # Pause pipeline, present to user
     pipeline_state = "waiting_escalation"
     Display escalation context to user
     
     Wait for user response:
       "fix and retry" → resume from target stage
       "accept" → continue pipeline
       "abort" → cancel pipeline
   
   If result.action == "accept":
     Log: "[pipeline] feedback_loop accepted source={stage.id}"
     Continue to next stage

Feedback Loop State Machine

                    ┌─────────────────┐
                    │  Review Stage   │
                    │   completes     │
                    └────────┬────────┘
                             │
                             ▼
                    ┌─────────────────┐
                    │ Has blocking    │───No───► Continue pipeline
                    │ feedback_items? │
                    └────────┬────────┘
                             │ Yes
                             ▼
                    ┌─────────────────┐
                    │ Invoke cross-   │
                    │ stage-feedback  │
                    └────────┬────────┘
                             │
            ┌────────────────┼────────────────┐
            │                │                │
            ▼                ▼                ▼
     ┌────────────┐   ┌────────────┐   ┌────────────┐
     │ re_implement│   │  escalate  │   │   accept   │
     └──────┬─────┘   └──────┬─────┘   └──────┬─────┘
            │                │                │
            ▼                ▼                ▼
     ┌────────────┐   ┌────────────┐   Continue
     │ Re-run     │   │ Pause for  │   pipeline
     │ target     │   │ user       │
     │ stage      │   └────────────┘
     └──────┬─────┘
            │
            └─────────► Review stage (re-evaluate)

Feedback Loop Constraints

1. User approval gates are NEVER bypassed:

   • Feedback loops only re-run implementation stages
   • If target stage has requires_approval: true, approval is still required
   • Escalation always pauses for user

2. Iteration caps prevent infinite loops:

   • Default: 2 iterations
   • Security-related: 1 iteration (escalate quickly)
   • Configurable per stage via feedback_loop.max_iterations

3. Only review→implement pairs can loop:

   • Source must be a mode: plan review stage
   • Target must be a mode: agent implementation stage
   • No feedback loops between implementation stages

Review Stage Output Convention

Review stages that want to participate in feedback loops must:

1. Include feedback_items in their declared outputs
2. Produce feedback items in the standard schema:

feedback_item:
  severity: blocking | advisory | informational
  file: string
  line: number | null
  description: string
  suggested_fix: string
  source_agent: string
  gate_id: string | null

3. Use consistent severity levels:
   • blocking: Must be fixed before proceeding
   • advisory: Should be fixed but doesn't block
   • informational: FYI only

Feedback Loop Metrics

Captured per feedback loop:

feedback_loop_metrics:
  task_id: string
  source_stage: string
  target_stage: string
  iterations: number
  resolved_without_escalation: boolean
  blocking_items_initial: number
  blocking_items_resolved: number
  total_loop_duration_ms: number

Integration Points

With Task Orchestration

Task orchestration skill calls pipeline-executor:

1. Task classified and routed
2. Pipeline selected (e.g., full-feature)
3. Call pipeline-executor with:
   - pipeline_name: "full-feature"
   - task_id: from orchestration
   - initial_inputs: {task_description, context}
4. Pipeline executor runs stages
5. Return result to orchestration for state update

With Rule Enforcement

Each stage checks rules:

Pre-action:
  - Load rule-enforcement skill
  - Check agent permissions for stage action
  - Block if rule violation

Post-action:
  - Validate stage outputs against constraints
  - Log any warnings

With Context Memory

All state persisted to memory:

Task state:     session.current/task-{task_id}.md
Artifacts:      session.current/artifact-{task_id}-{stage}.md
Metrics:        session.current/metrics-{task_id}.md
Dead letters:   session.current/dead-letter-{task_id}.md

Usage Examples

Execute Full Feature Pipeline

Input:
  pipeline_name: full-feature
  task_id: task-a1b2c3-add-auth
  initial_inputs:
    task_description: "Add OAuth2 authentication"
    complexity: high
    task_type: feature
    always_include: [ciso]

Execution:
  1. plan (cto, plan mode) → implementation_plan
  2. architecture_review (vp-architecture) → architecture_review [conditional: high complexity]
  3. security_review (ciso) → security_review [conditional: ciso in always_include]
  4. implement (tech-lead, agent mode) → code_changes [approval gate]
  5. observability_review (sre-lead) → observability_review
  6. quality_review (staff-engineer) → quality_review

Output:
  status: success
  stages_executed: [plan, architecture_review, security_review, implement, observability_review, quality_review]
  artifacts: {merged outputs}

Execute Bug Fix with Retry

Input:
  pipeline_name: bug-fix
  task_id: task-x7y8z9-fix-login
  initial_inputs:
    task_description: "Fix login button not responding"
    error_context: "Button click event not firing on mobile"

Execution:
  1. diagnose (tech-lead) → diagnosis, affected_files
  2. fix (tech-lead) → code_changes [fails on first attempt]
     - Retry 1: backoff 5s, re-execute → fails
     - Retry 2: backoff 10s, re-execute → success
  3. verify (tech-lead) → verification_results

Output:
  status: success
  stages_executed: [diagnose, fix (3 attempts), verify]
  artifacts: {diagnosis, code_changes, verification_results}

Resume from Failed Stage

Input:
  pipeline_name: full-feature
  task_id: task-a1b2c3-add-auth
  start_from_stage: implement
  initial_inputs: {loaded from prior artifacts}

Execution:
  - Skip: plan, architecture_review, security_review (already completed)
  - Start: implement
  - Continue: observability_review, quality_review

Output:
  status: success
  stages_executed: [implement, observability_review, quality_review]

Error Handling

Common Errors

Error	Cause	Resolution
Pipeline not found	Invalid pipeline_name	Check configurations/pipelines/
Agent not found	Missing agent definition	Run vp-onboarding for project agents
Input artifact missing	Prior stage didn't produce output	Check stage outputs configuration
Condition parse error	Invalid condition syntax	Review condition documentation
Timeout	Stage exceeded time limit	Increase timeout or optimize stage
Rollback failed	Rollback strategy couldn't complete	Manual intervention required

Error Recovery

1. Timeout errors:
   - Check if stage is making progress
   - Consider increasing timeout_minutes
   - Split into smaller stages if needed

2. Validation errors:
   - Non-retryable
   - Review stage outputs for issues
   - Fix and re-run with start_from_stage

3. Rollback failures:
   - Check git status for uncommitted changes
   - Manually verify state
   - Use manual rollback strategy