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BB langsmith-fetch

Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

w95/awesome-claude-corporate-skills Agent Skills author: w95 MIT 1 file body ≈ 2 608 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio.

As a process B 67/100 · Nearly there — weak spots: when it triggers, progress reporting

IntegrationGitHubSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
90
Quality 40%
85
Run on models
none yet
Process rating
B
67/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Failures and branches w 10
50
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: RA-Skills, RA-Skills

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 2

    ✓ No critical or high findings

    Medium and low: 2
    • medium Dangerous commands cmd-shell-rc SKILL.md:393
      Writes to a shell startup file
      echo 'export LANGSMITH_API_KEY="your_key"' >> ~/.bashrc
    • medium Dangerous commands cmd-shell-rc SKILL.md:394
      Writes to a shell startup file
      echo 'export LANGSMITH_PROJECT="your_project"' >> ~/.bashrc

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 67/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 70 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2608 tokens
    • 100Running it twice. Mutating operations check current state
    • low 11 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • -222 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 336: enough signal without eating the budget
    • +4Structure: 35 headings
    • +3Step-by-step instructions: 70 items
    • +3Output format is stated explicitly
    • +4Has examples (27 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.