Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/postindustria-tech/agentic-toolkit/langgraph-dev-parallel-executionnpx skills add postindustria-tech/agentic-toolkit --skill langgraph-dev-parallel-executiongit clone --depth 1 https://github.com/postindustria-tech/agentic-toolkitWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00067 | $0.02698 |
| Opus 5 | $0.00034 | $0.01349 |
| Sonnet 5 | $0.00013 | $0.00540 |
| Haiku 4.5 | $0.00007 | $0.00270 |
Grade A, and why
parallel-execution-in-langgraph scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 335 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parallel Execution in LangGraph
Parallel execution allows multiple nodes to process state concurrently, with results automatically merged using state reducers.
Fan-Out/Fan-In Pattern
from typing import Annotated, TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.types import Overwrite
import operator
class State(TypedDict):
results: Annotated[list, operator.add] # Accumulates results via concatenation
input_data: str
def branch_1(state: State) -> dict:
return {"results": [1, 2, 3]}
def branch_2(state: State) -> dict:
return {"results": [4, 5, 6]}
def branch_3(state: State) -> dict:
return {"results": [7, 8, 9]}
def combine_results(state: State) -> dict:
"""
Process aggregated results from parallel branches.
Note: Results are already combined by the state reducer (operator.add).
This node performs post-processing (sorting) and uses Overwrite to
replace the accumulated results rather than appending to them.
"""
sorted_results = sorted(state["results"])
return {"results": Overwrite(value=sorted_results)}
def fan_out(state: State) -> list[str]:
"""Route to multiple branches for parallel execution."""
return ["branch_1", "branch_2", "branch_3"]
workflow = StateGraph(State)
workflow.add_node("branch_1", branch_1)
workflow.add_node("branch_2", branch_2)
workflow.add_node("branch_3", branch_3)
workflow.add_node("combine", combine_results)
# Fan-out from START to parallel branches (path_map ensures correct visualization)
workflow.add_conditional_edges(START, fan_out, ["branch_1", "branch_2", "branch_3"])
# Fan-in: all branches must complete before combine (list syntax)
workflow.add_edge(["branch_1", "branch_2", "branch_3"], "combine")
workflow.add_edge("combine", END)
# Compile the graph before invocation
graph = workflow.compile()
# Invoke the graph
result = graph.invoke({"input_data": "test", "results": []})
Result: result["results"] = [1, 2, 3, 4, 5, 6, 7, 8, 9] (sorted by combine_results after accumulation)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 335 lines · 67 tokens per session scan A 1d3309e142e0
parallel-execution-in-langgraph is a skill published in the GitHub repository postindustria-tech/agentic-toolkit (2 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 2,698 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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