Agent skill

Langchain Debug Bundle

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Produce a reproducible, sanitized diagnostic bundle for a LangChain / LangGraph incident — environment snapshot, version manifest, filtered astreamevents(v2) transcript, propagating callback stack…

MITAuto-check passedAI & LLM Engineering

Install Langchain Debug Bundle

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill langchain-debug-bundle -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace langchain-debug-bundle --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/langchain-debug-bundle .claude/skills/langchain-debug-bundle && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
langchain-debug-bundle
GitHub stars
2.8k
Token cost
~4.6k tokens
SKILL.md length
1,162 words
Files
6 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Produce a reproducible, sanitized diagnostic bundle for a LangChain / LangGraph incident — environment snapshot, version manifest, filtered astreamevents(v2) transcript, propagating callback stack…

  • Works in 6 steps: Assemble the environment manifest → Capture astream_events(version="v2")… → Attach callbacks that propagate into… → …
  • Triaging a production incident
  • SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
  • Reaches smith.langchain.com; needs LANGSMITH_API_KEY

What it does

Langchain Debug Bundle is an agent skill from jeremylongshore/tons-of-skills-marketplace. Produce a reproducible, sanitized diagnostic bundle for a LangChain / LangGraph incident — environment snapshot, version manifest, filtered astreamevents(v2) transcript, propagating callback stack, LangSmith trace URL — so a debug colleague can reproduce the failure without a live terminal. Use when triaging a production incident, filing a Discord or GitHub bug report, asking for help on the LangChain forum, or archiving a post-mortem artifact. Trigger with "langchain debug bundle", "langgraph debug dump"…

Its SKILL.md is about 4.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/astream-events-capture.md`, `references/callback-propagation.md` and `references/env-manifest-template.md`). Compatibility notes: Designed for Claude Code

It sits in AI & LLM Engineering, covering Building AI agents and LLM observability. It works with LangChain, LangSmith, LangGraph and GitHub. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Triaging a production incident
  • Filing a Discord
  • GitHub bug report
  • Asking for help on the LangChain forum

Example prompts

  • “langchain debug bundle”
  • “langgraph debug dump”
  • “langchain diagnostic export”
  • “/langchain-debug-bundle”

Requirements

  • Python 3
  • A credential in LANGSMITH_API_KEY
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(python:*), Bash(pip:*)

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Assemble the environment manifest
  2. Capture astream_events(version="v2") with a filter
  3. Attach callbacks that propagate into subgraphs (P28)
  4. Record the LangSmith trace URL
  5. Sanitize before packaging
  6. Bundle with an index

What it can do on your machine

Read from SKILL.md and the folder at commit 80f86df. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash(python:*)
    • Bash(pip:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • smith.langchain.com

    Also links to:

    • python.langchain.com
    • docs.smith.langchain.com
    • langchain-ai.github.io
    • blog.langchain.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • LANGSMITH_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Langchain Debug Bundle loads about 4.6k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 161 tokens; SKILL.md has 1,162 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~161
When it runs · the whole SKILL.md, loaded when a task matches
~4.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~12k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit 80f86df, republished under its MIT licence (© jeremylongshore). 1,162 words, ~4,602 tokens.

Download SKILL.mdSave it as .claude/skills/langchain-debug-bundle/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
langchain-debug-bundle
description
Produce a reproducible, sanitized diagnostic bundle for a LangChain / LangGraph incident — environment snapshot, version manifest, filtered astream_events(v2) transcript, propagating callback stack, LangSmith trace URL — so a debug colleague can reproduce the failure without a live terminal. Use when triaging a production incident, filing a Discord or GitHub bug report, asking for help on the LangChain forum, or archiving a post-mortem artifact. Trigger with "langchain debug bundle", "langgraph debug dump", "langchain diagnostic export", "langsmith trace export", "astream_events dump", "langchain incident bundle".
allowed-tools
Read, Write, Edit, Bash(python:*), Bash(pip:*)
compatibility
Designed for Claude Code
version
2.7.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, langchain, langgraph, python, langchain-1.0, debugging, observability, incident-response

LangChain Debug Bundle (Python)

Overview

An on-call engineer pages you at 2am: the production agent loops, ToolMessage outputs are empty strings, the user sees "I could not find the answer." Someone asks the right question — what state was the graph in when it gave up? — and there is no answer, because the terminal that caught the failure is already gone, the Kubernetes pod has restarted, and the LangSmith URL was never recorded.

This skill produces one artifact: a single bundle-<incident_id>.tar.gz (typically 1-10 MB) containing everything a second engineer needs to reproduce the failure without a live terminal — environment and version manifest, filtered astream_events(version="v2") JSONL, a propagating callback stack, the LangSmith trace URL, and a post-write sanitization pass.

Four pitfalls make naive bundles useless:

  • P01 — ChatAnthropic.stream() reports token_usage only on stream close; token math read from on_llm_end lags by stream duration, so cost context in the bundle is wrong.
  • P28 — BaseCallbackHandler.with_config(callbacks=[...]) does NOT propagate into subgraphs or inner create_react_agent loops. A debug callback bound that way silently captures zero events from the place the incident actually happened.
  • P47 — astream_events(version="v2") emits 2,000+ events per invocation. A raw dump is 50 MB and unreadable; an SSE viewer crashes on it.
  • P67 — astream_log() is soft-deprecated in 1.0. Diagnostic tooling built on it breaks on the next minor version.

The skill's answer: assemble the manifest, capture v2 events with a whitelist (drop lifecycle noise, keep on_chat_model_stream / on_tool_* / any *_error event), attach DebugCallbackHandler via config["callbacks"] at invoke time, pull the LangSmith URL from the active RunTree, run the sanitization pass, tar it up. Pinned: langchain-core 1.0.x, langgraph 1.0.x, langsmith 0.1.x. Pain-catalog anchors: P01, P28, P47, P67.

Prerequisites

  • Python 3.10+
  • langchain-core >= 1.0, < 2.0, langgraph >= 1.0, < 2.0
  • langsmith >= 0.1.40 for RunTree access
  • Active LangSmith project (LANGSMITH_TRACING=true, LANGSMITH_API_KEY=..., LANGSMITH_PROJECT=...) — canonical 1.0 env-var names, not the legacy LANGCHAIN_TRACING_V2 (see P26).
  • Write access to a staging directory outside the repo tree.

Instructions

Step 1 — Assemble the environment manifest

Record the runtime snapshot that lets a colleague reproduce on a different host. See env-manifest-template.md for the exact YAML shape.

python
import platform, sys, os, subprocess, datetime

RELEVANT = [
    "langchain-core", "langchain", "langgraph",
    "langchain-anthropic", "langchain-openai",
    "langsmith", "anthropic", "openai", "pydantic",
]

def pip_show(name: str) -> str | None:
    try:
        out = subprocess.check_output(
            [sys.executable, "-m", "pip", "show", name],
            stderr=subprocess.DEVNULL, text=True,
        )
        for line in out.splitlines():
            if line.startswith("Version:"):
                return line.split(":", 1)[1].strip()
    except subprocess.CalledProcessError:
        return None

def build_manifest(incident_id: str, invoke_meta: dict) -> dict:
    return {
        "bundle_spec_version": "1.0",
        "generated_at": datetime.datetime.utcnow().isoformat() + "Z",
        "incident_id": incident_id,
        "runtime": {
            "python": sys.version.split()[0],
            "platform": platform.platform(),
            "cpu_count": os.cpu_count(),
        },
        "packages": [
            {"name": n, "version": pip_show(n)}
            for n in RELEVANT if pip_show(n) is not None
        ],
        # NAMES only — never values. Sanitized by design (P27 posture).
        "env_var_names_present": sorted(
            k for k in os.environ
            if k.startswith(("LANGSMITH_", "LANGCHAIN_", "ANTHROPIC_", "OPENAI_", "GOOGLE_"))
        ),
        "invocation": invoke_meta,
    }

Record env-var names, not values. Values go through the sanitization pass in Step 5, but the safest design is never to capture them.

Step 2 — Capture astream_events(version="v2") with a filter

Raw v2 events flood 2,000+ per invocation (P47). A server-side filter drops lifecycle noise (on_chain_start/on_chain_end) and keeps model, tool, and error events — yielding 50-200 events per invocation and a ~500 KB JSONL.

python
import json, itertools
from pathlib import Path

KEEP = {
    "on_chat_model_start", "on_chat_model_end",
    "on_tool_start", "on_tool_end", "on_tool_error",
    "on_retriever_start", "on_retriever_end",
    "on_custom_event",
}
# Additionally: any event whose name ends in "_error"
# Additionally: 1-in-10 sampled on_chat_model_stream (for response reconstruction)

async def capture_events(graph, inputs, config, out_path: Path) -> int:
    sample = itertools.count()
    written = 0
    with out_path.open("w") as f:
        async for evt in graph.astream_events(inputs, config=config, version="v2"):
            name = evt["event"]
            if name == "on_chat_model_stream" and next(sample) % 10 != 0:
                continue
            if name not in KEEP and not name.endswith("_error"):
                continue
            f.write(json.dumps({
                "event": name,
                "name": evt.get("name"),
                "run_id": str(evt.get("run_id")),
                "tags": evt.get("tags"),
                "metadata": evt.get("metadata"),
                "data": _json_safe(evt.get("data", {})),
            }, default=str) + "\n")
            written += 1
    return written

Never use astream_log() (P67). The full event taxonomy and _json_safe helper live in astream-events-capture.md.

Step 3 — Attach callbacks that propagate into subgraphs (P28)

Callbacks bound via Runnable.with_config(callbacks=[...]) fire on the outer chain only. They go silent the moment the graph crosses into a subgraph or an inner create_react_agent loop — exactly where incidents happen. Pass them via config["callbacks"] at invoke time instead.

python
from langchain_core.callbacks import BaseCallbackHandler

class DebugCallbackHandler(BaseCallbackHandler):
    def __init__(self): self.records: list[dict] = []
    def on_tool_start(self, serialized, input_str, *, run_id, parent_run_id=None, **kw):
        self.records.append({
            "kind": "tool_start", "run_id": str(run_id),
            "parent_run_id": str(parent_run_id) if parent_run_id else None,
            "tool": serialized.get("name"), "input": input_str[:500],
        })
    def on_tool_error(self, error, *, run_id, **kw):
        self.records.append({
            "kind": "tool_error", "run_id": str(run_id),
            "error_type": type(error).__name__, "error_message": str(error)[:1000],
        })

debug = DebugCallbackHandler()

result = await agent.ainvoke(
    {"messages": [("user", reproducer_prompt)]},
    config={
        "configurable": {"thread_id": thread_id},
        "callbacks": [debug],                      # propagates into subgraphs
        "tags": ["debug-bundle", incident_id],
        "metadata": {"incident_id": incident_id},
    },
)

The full handler (LLM + retriever + tool lifecycle) and a propagation smoke test live in callback-propagation.md.

Step 4 — Record the LangSmith trace URL

A trace URL is cheaper than any local artifact — one click and the colleague sees the full run with latency, token counts, and input/output per node. Pull it from the active RunTree if you have a live handle; otherwise construct it from the invoke's run_id:

python
from langsmith.run_helpers import get_current_run_tree

def capture_langsmith_url() -> str | None:
    rt = get_current_run_tree()
    if rt is None:
        return None  # tracing not enabled or run already closed
    return rt.get_url()  # https://smith.langchain.com/o/.../r/<run_id>

# Write to langsmith.url in the bundle:
url = capture_langsmith_url()
(staging / "langsmith.url").write_text(url or "(no trace URL available)")

The URL requires the colleague to have access to the LangSmith project. For public sharing, use RunTree.share() to generate a public snapshot URL. Never paste a non-shared URL into a public Discord thread — the page redirects to a login and leaks the project name.

Step 5 — Sanitize before packaging

Every file in the staging dir passes through the redaction pass before the tar.gz is written. This is the last-mile guard; upstream redaction middleware should already have caught credential material, but the bundle cannot assume that.

python
import re

PATTERNS = [
    ("openai_key",    r"sk-proj-[A-Za-z0-9_-]{16,}|sk-[A-Za-z0-9_-]{32,}"),
    ("anthropic_key", r"sk-ant-[A-Za-z0-9_-]{16,}"),
    ("google_key",    r"AIza[A-Za-z0-9_-]{35}"),
    ("langsmith_key", r"lsv2_(?:pt|sk)_[A-Za-z0-9]{32,}"),
    ("bearer",        r"(?i)bearer\s+[A-Za-z0-9._~+/=-]{20,}"),
    ("db_uri",        r"[a-z]+://[^:/\s]+:[^@\s]+@[^/\s]+"),
    ("private_key",   r"-----BEGIN [A-Z ]*PRIVATE KEY-----[\s\S]*?-----END [A-Z ]*PRIVATE KEY-----"),
]

def sanitize_file(path, patterns=PATTERNS) -> dict[str, int]:
    text, counts = path.read_text(), {}
    for name, pat in patterns:
        new, n = re.subn(pat, f"[REDACTED:{name}]", text)
        if n: counts[name] = n; text = new
    path.write_text(text)
    return counts

The full pattern catalog (credentials, session tokens, PII, internal URLs) and the pre-upload tar -xzf ... && grep scan live in sanitization-checklist.md. For the production-grade upstream redaction middleware, use the forthcoming langchain-security-basics skill.

Step 6 — Bundle with an index

Write a top-level MANIFEST.yaml that describes every file and records the sanitization summary. Then tar.gz the staging dir.

python
import tarfile, yaml
from pathlib import Path

def write_bundle(staging: Path, manifest: dict, sanitize_report: dict,
                 out: Path, events_count: int, callback_count: int) -> Path:
    index = {
        "bundle_spec_version": "1.0",
        "incident_id": manifest["incident_id"],
        "generated_at": manifest["generated_at"],
        "files": [
            {"name": "manifest.yaml",  "purpose": "env + version snapshot"},
            {"name": "events.jsonl",   "purpose": f"filtered astream_events(v2), {events_count} events"},
            {"name": "callbacks.txt",  "purpose": f"DebugCallbackHandler records, {callback_count} entries"},
            {"name": "langsmith.url",  "purpose": "trace URL (shared) or (none)"},
            {"name": "notes.txt",      "purpose": "free-form engineer notes, sanitized"},
        ],
        "sanitization": sanitize_report,
    }
    (staging / "MANIFEST.yaml").write_text(yaml.safe_dump(index, sort_keys=False))
    with tarfile.open(out, "w:gz") as tar:
        tar.add(staging, arcname=out.stem)
    return out

bundle = write_bundle(
    staging=Path("/tmp/bundle-INC-2026-0421-A"),
    manifest=m, sanitize_report=report,
    out=Path("/tmp/bundle-INC-2026-0421-A.tar.gz"),
    events_count=n_events, callback_count=len(debug.records),
)
Show full SKILL.md (495 more words)Show less

Output

File in bundlePurposeSourceSanitization step
MANIFEST.yamlIndex + file descriptions + redaction countsStep 6N/A (authored)
manifest.yamlPython/OS/package/env-var-name snapshotStep 1Run pass; env-var names only by design
events.jsonlFiltered astream_events(v2) — model, tool, error eventsStep 2Per-line regex redaction
callbacks.txtDebugCallbackHandler records (JSONL)Step 3Per-line regex redaction
langsmith.urlRunTree.get_url() (shared if public)Step 4Verify no embedded API key param
notes.txtEngineer's free-form observationsManualPer-line regex redaction

Typical size: 1-10 MB compressed. Typical event count after filter: 50-200 per invocation (down from 2,000+ raw). Bundle is self-contained — no external dependencies beyond tar -xzf and a text editor.

Error Handling

ErrorCauseFix
events.jsonl has no subgraph eventsCallbacks bound via Runnable.with_config(callbacks=[...]) instead of config["callbacks"] (P28)Move callbacks to invoke-time config; see callback-propagation.md
events.jsonl is 50 MB+Filter not applied or on_chain_* events not excluded (P47)Enforce KEEP whitelist and 1:10 streaming sample; Step 2
DeprecationWarning: astream_log is deprecatedCaptured via astream_log() instead of astream_events(v2) (P67)Migrate to graph.astream_events(..., version="v2")
response_metadata["token_usage"] empty in on_chat_model_end recordsRead before stream closed (P01)Aggregate from on_chat_model_stream chunks with usage_metadata; see model-inference skill
langsmith.url is emptyTracing not enabled or get_current_run_tree() returned NoneSet LANGSMITH_TRACING=true, LANGSMITH_API_KEY=..., LANGSMITH_PROJECT=... (P26)
TypeError: Object of type X is not JSON serializable in events captureTool returned a custom class with no .model_dump()Extend _json_safe in astream-events-capture.md
Pre-upload scan finds sk-... patternUpstream middleware missed a key, or regex too laxAdd specific pattern to PATTERNS, re-run Step 5, re-archive

Examples

Triage decision tree — "which file in the bundle do I read first?"

Give a colleague this table with the bundle so they know where to start:

Symptom in the ticketStart withThen
"Agent looped forever" / GraphRecursionErrorevents.jsonl (filter on_tool_start)callbacks.txt for tool→tool timing
"Tool returned empty" / "Could not find the answer" (P09)events.jsonl (grep on_tool_error)callbacks.txt for the parent run_id
"Wrong answer, correct tool called"events.jsonl (grep on_chat_model_start + last on_tool_end)LangSmith trace URL for full context
"Token count wrong in dashboard" (P01, P25)events.jsonl on_chat_model_stream chunksmanifest.yaml for retry middleware presence
"Works locally, fails in prod"manifest.yaml diff against localevents.jsonl for env-specific branches
"Memory resets between turns" (P16)manifest.yaml → langgraph.thread_id present?events.jsonl → checkpointer restore events
Incident-driven capture — reproducing and bundling in one script

See callback-propagation.md for the full invoke-time config pattern. The skeleton:

python
async def reproduce_and_bundle(agent, reproducer, incident_id: str) -> Path:
    debug = DebugCallbackHandler()
    staging = Path(f"/tmp/bundle-{incident_id}"); staging.mkdir(exist_ok=True)

    try:
        result = await agent.ainvoke(
            reproducer,
            config={"configurable": {"thread_id": f"debug-{incident_id}"},
                    "callbacks": [debug], "tags": ["debug-bundle", incident_id]},
        )
        invoke_meta = {"status": "success"}
    except Exception as e:
        invoke_meta = {"status": "error",
                       "error_class": type(e).__name__, "error_message": str(e)[:500]}

    # Step 2 — capture events (separate invocation with same inputs OR replay
    # from RunTree if already in LangSmith)
    n_events = await capture_events(agent, reproducer, {...}, staging / "events.jsonl")

    # Step 1 — manifest, Step 3 — callbacks, Step 4 — LangSmith URL, Step 5 — sanitize
    # Step 6 — bundle
    return write_bundle(staging, build_manifest(incident_id, invoke_meta), ..., ...)
Discord / forum bug report checklist

Before posting to the LangChain Discord or GitHub Issues:

  1. Run the pre-upload tar -xzf && grep scan (sanitization-checklist.md)
  2. Confirm langsmith.url is a shared URL (public), not a project-internal one
  3. Strip the incident_id if it maps to internal ticket numbers you cannot disclose
  4. Include in the post: bundle attachment, a 3-sentence symptom description, the exact reproducer prompt, the first line of MANIFEST.yaml (spec version and versions of langchain-core + langgraph)

Resources

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in skills/.curated/langchain-debug-bundle of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/astream-events-capture.md
  • references/callback-propagation.md
  • references/env-manifest-template.md
  • references/one-pager.md
  • references/sanitization-checklist.md

Open the folder on GitHubat commit 80f86df

Compare with similar skills

Langchain Debug Bundle next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Langchain Debug Bundle compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Langchain Debug Bundle this skilljeremylongshore/tons-of-skills-marketplace2.8k—~4.6kAutomated safety check: PassMIT
LangSmith Trace DebuggingComposioHQ/awesome-claude-skills77k8 repos~2.7kAutomated safety check: PassNone
Agentsop Observability Setupagentsope/SkillAlchemy466—~4.4kAutomated safety check: PassMIT
Langchain Dependencieslangchain-ai/langchain-skills1.3k—~3.6kAutomated safety check: PassMIT
Langgraph Testing Evaluationsoba-labs/langchain-agent-skills107—~2.3kAutomated safety check: PassMIT
Langsmithlangchain-ai/docs426—~935Automated safety check: PassMIT

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    jeremylongshore/tons-of-skills-marketplace

    Aggregate and centralize performance metrics from applications, systems, databases, caches, and services.

    2.8k GitHub stars~1.2k tokensUpdated today
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  • Analyzing Capacity Planning

    jeremylongshore/tons-of-skills-marketplace

    Execute this skill enables AI assistant to analyze capacity requirements and plan for future growth.

    2.8k GitHub stars~947 tokensUpdated today
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  • Analyzing Database Indexes

    jeremylongshore/tons-of-skills-marketplace

    Process use when you need to work with database indexing. An agent skill from jeremylongshore/tons-of-skills-marketplace.

    2.8k GitHub stars~2k tokensUpdated today
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Questions about Langchain Debug Bundle

What does Langchain Debug Bundle do?

Produce a reproducible, sanitized diagnostic bundle for a LangChain / LangGraph incident — environment snapshot, version manifest, filtered astreamevents(v2) transcript, propagating callback stack…. Langchain Debug Bundle is an agent skill from jeremylongshore/tons-of-skills-marketplace. Produce a reproducible, sanitized diagnostic bundle for a LangChain / LangGraph incident — environment snapshot, version manifest, filtered astreamevents(v2) transcript, propagating callback stack, LangSmith trace URL — so a debug colleague can reproduce the failure without a live terminal.

When should I use Langchain Debug Bundle?

Langchain Debug Bundle fits situations like: triaging a production incident; filing a Discord; GitHub bug report; asking for help on the LangChain forum.

How do I install Langchain Debug Bundle in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill langchain-debug-bundle -a claude-code`. Or copy the skill folder (skills/.curated/langchain-debug-bundle in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/langchain-debug-bundle in your project. Claude Code loads it when a task matches its description.

How do I install Langchain Debug Bundle in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill langchain-debug-bundle -a codex`. Or copy the skill folder (skills/.curated/langchain-debug-bundle in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/langchain-debug-bundle in your project. Codex loads it when a task matches its description.

Can I use Langchain Debug Bundle in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill langchain-debug-bundle -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/langchain-debug-bundle, .gemini/skills/langchain-debug-bundle, .github/skills/langchain-debug-bundle and .opencode/skills/langchain-debug-bundle in your project.

What does Langchain Debug Bundle need to run?

Going by SKILL.md and its folder, Langchain Debug Bundle needs credentials named LANGSMITH_API_KEY. Our summary lists: Python 3; A credential in LANGSMITH_API_KEY. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(python:*), Bash(pip:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Langchain Debug Bundle access the network?

SKILL.md names 5 domains. In commands or code: smith.langchain.com; the agent is likely to contact it when it follows the instructions. As links in the text: python.langchain.com, docs.smith.langchain.com, langchain-ai.github.io and blog.langchain.com. This is read from the text; nothing was executed.

Is Langchain Debug Bundle safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Langchain Debug Bundle use?

Langchain Debug Bundle is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Langchain Debug Bundle use?

About 4.6k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.4k tokens, read only when the agent opens those files.

What are the alternatives to Langchain Debug Bundle?

Skills that share tags, products or a category with Langchain Debug Bundle: LangSmith Trace Debugging (ComposioHQ/awesome-claude-skills, 77k stars), Agentsop Observability Setup (agentsope/SkillAlchemy, 466 stars), Langchain Dependencies (langchain-ai/langchain-skills, 1.3k stars) and Langgraph Testing Evaluation (soba-labs/langchain-agent-skills, 107 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Langchain Debug Bundle?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,825 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 9, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.