parallel agent workflow builder
Orchestrate complex tasks via isolated parallel lanes with strict data contracts and contradiction resolution.
$5
Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+21 moreClaude CodeCursorCodex CLIMuseDots
Manus
Grok
Grok Bot+18 agentsparallel agent workflow builder
Example session with this skill installed
Decompose the objective "Analyze a codebase for security vulnerabilities and performance bottlenecks" into a parallel workflow with contracts and merge strategies.
- Read your context and instructions
- Compiled the parallel agent workflow
- Generated the data export
I have decomposed the security and performance analysis objective into three isolated execution lanes. Each lane operates on a specific domain to prevent context contamination, with a final merge stage to synthesize the findings.
Parallel Execution Plan: Code Analysis
| Lane ID | Sub-task Objective | Input Contract | Output Contract | Priority |
|---|---|---|---|---|
| L01 | Static Analysis (SAST) | src/**/*.js | list[{cwe: string, file: string, line: number}] | High |
| L02 | Dependency Audit | package-lock.json | list[{pkg: string, version: string, severity: enum}] | Medium |
| L03 | Runtime Profiling | performance_logs.csv | list[{function: string, latency_ms: number}] | Medium |
Workflow Configuration
workflow:
name: "code-audit-parallel-lane"
version: "1.0.0"
deterministic: true
merge_order: ["L01", "L02", "L03"]
conflict_resolution:
strategy: "priority_by_lane"
fallback: "manual_log"
lanes:
L01:
constraints: ["Only report High/Critical CWEs", "Format as valid JSON"]
L02:
constraints: ["Check against GitHub Advisory Database"]
L03:
constraints: ["Highlight functions exceeding 200ms p99"]
Contradiction Resolution Logic
Deduplication: If L01 and L02 identify the same vulnerable dependency, L01 (SAST) takes precedence for reporting context.
2. Normalization: All file paths are coerced to relative repository roots before merging.
3.
Partial Preservation: If L03 fails due to log size, the merge layer will still output the L01 and L02 findings with a status: partial flag.
Next steps
- Review the
decomposition.jsonfile for lane boundary accuracy. - Initialize the
lane_results/directory structure. - Run the execution manifest to start the parallel agent processes.
performance_logs.csv
CSV · data export
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
Large-scale agent tasks often fail because a single error crashes the entire process, or mixed contexts lead to "hallucination soup." Manually managing dependencies and merging conflicting outputs from multiple sub-tasks is slow and error-prone.
What it does
- Decomposes complex objectives into a Directed Acyclic Graph (DAG) of isolated execution lanes.
- Defines strict input/output schemas (contracts) to prevent data drift between lanes.
- Executes sub-tasks in parallel with independent scratch spaces and zero shared mutable state.
- Resolves data contradictions using priority-based voting, confidence scoring, or timestamp precedence.
- Synthesizes a single coherent deliverable while preserving partial work from failed or timed-out lanes.
Frameworks & tools
Compatible with any LLM orchestration layer. Uses YAML for configuration, JSON for data contracts, and Python-based deep merging strategies.
Why this beats prompting it yourself
A single prompt trying to handle a massive task lacks isolation, leading to context contamination and "all-or-nothing" failures. This skill enforces deterministic execution and data validation, ensuring that if one sub-task fails, you still get a verified, merged result from the others without manual cleanup.
Use cases
- Generating technical documentation by analyzing source code, tests, and logs in parallel.
- Auditing large datasets where different agents check for different classes of violations.
- Synthesizing market research from disparate sources like web search, PDF reports, and raw CSV data.
Known limitations
Requires an initial decomposition phase which adds overhead to small tasks. Merge logic for complex nested arrays requires manual strategy definitions in the YAML config.
Requires initial decomposition overhead. Nested array merging requires manual strategy definition in YAML. Determinism depends on fixed seeds and merge ordering.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
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- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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