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 skills add soba-labs/langchain-agent-skills --skill langgraph-error-handlinggit clone --depth 1 https://github.com/soba-labs/langchain-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/soba-labs/langchain-agent-skills/langgraph-error-handling)<a href="https://agentmods.dev/skills/soba-labs/langchain-agent-skills/langgraph-error-handling"><img src="https://agentmods.dev/badge/skills/soba-labs/langchain-agent-skills/langgraph-error-handling/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/soba-labs/langchain-agent-skills/langgraph-error-handling"><img src="https://agentmods.dev/badge/skills/soba-labs/langchain-agent-skills/langgraph-error-handling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00069 | $0.01410 |
| Opus 5 | $0.00034 | $0.00705 |
| Sonnet 5 | $0.00014 | $0.00282 |
| Haiku 4.5 | $0.00007 | $0.00141 |
Grade A, and why
langgraph-error-handling 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 11d ago.
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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangGraph Error Handling
Use This Skill For
- Adding
RetryPolicyto flaky nodes (API, DB, model/tool calls) - Designing LLM recovery loops (
Command+ error state + retry counters) - Adding human approval/escalation with
interrupt()and resume - Handling prebuilt
ToolNodefailures - Debugging transactional failure behavior in parallel supersteps
Strategy Selection
Use this order:
- Transient/infrastructure issue (
429, timeout,5xx, temporary DB lock) ->RetryPolicy - Recoverable by model/tool args correction -> store error in state and route back with
Command - Needs user approval or missing info ->
interrupt()+ resume - Unknown/programming bug -> let it bubble up and debug
| Error Type | Owner | Primary Mechanism |
|---|---|---|
| Transient | System | RetryPolicy |
| LLM-recoverable | LLM | State update + Command(goto=...) |
| User-fixable | Human | interrupt() + Command(resume=...) |
| Unexpected | Developer | Raise/log/debug |
For full taxonomy, load references/error-types.md.
Minimal Patterns
1) Retry Transient Failures
from langgraph.types import RetryPolicy
builder.add_node(
"call_api",
call_api,
retry_policy=RetryPolicy(max_attempts=3, initial_interval=1.0),
)
builder.addNode("callApi", callApi, {
retryPolicy: { maxAttempts: 3, initialInterval: 1.0 },
});
Notes:
- Python and JS default retry behavior differs by exception type.
- Prefer targeted
retry_on/retryOnfor non-transient domains.
2) LLM Recovery Loop
Use MessagesState in Python for message state.
from typing import Literal
from typing_extensions import NotRequired
from langgraph.graph import MessagesState
from langgraph.types import Command
class State(MessagesState):
error: NotRequired[str]
retry_count: NotRequired[int]
def agent(state: State) -> Command[Literal["tool", "__end__"]]:
if state.get("retry_count", 0) >= 3:
return Command(goto="__end__")
if state.get("error"):
return Command(goto="tool")
return Command(goto="tool")
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/examples/human-loop-example/js/index.js 3.0 KB runs code
- assets/examples/human-loop-example/js/package.json 239 B
- assets/examples/human-loop-example/python/graph.py 3.1 KB runs code
- assets/examples/human-loop-example/python/requirements.txt 39 B
- assets/examples/retry-example/js/index.js 2.8 KB runs code
- assets/examples/retry-example/js/package.json 234 B
- assets/examples/retry-example/python/graph.py 2.9 KB runs code
- assets/examples/retry-example/python/requirements.txt 39 B
- references/error-types.md 12 KB
- references/human-escalation.md 21 KB
- references/llm-recovery.md 14 KB
- references/retry-strategies.md 17 KB
- scripts/classify_error.py 7.2 KB runs code
- scripts/wrap_with_retry.py 12 KB runs code
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.
- 11d ago First seen · 172 lines · 69 tokens per session scan A 1e4cba605bf6
langgraph-error-handling is a skill published in the GitHub repository soba-labs/langchain-agent-skills (106 stars, last pushed 24d ago), licensed MIT. It adds 69 tokens to every session and 1,410 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 2026-08-30.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
diagnose
Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.
repro-admin
Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
byted-util-volcengine-detect-retry
An orchestration workflow for Volcengine Cloud Detect, a service that checks websites or network endpoints from test locations.