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BB symbolicate-crash-dump

Symbolicate a native VS Code crash dump (.dmp) using electron-minidump. Use when given a crash dump file, asked to symbolicate a crash, resolve missing method names in a native crash backtrace, or attach Electron/Insiders/Stable symbol files. VS Code team members only; requires macOS or Linux.

The skillemall take

This skill parses VS Code crash dumps via electron-minidump and extracts readable function names from native stacks. Promises to handle .dmp files, recover call traces, and attach Electron symbols. Single file, 2609 tokens of instructions, no critical issues found. Quality score sits at 84, but process score dropped to 66—suggests some logic gaps. Aimed at VS Code team members on macOS or Linux only; useless elsewhere. No sandbox test or model runs recorded, so real-world performance against actual dumps remains untested.

Install if you're in the VS Code team and debug crashes regularly. Otherwise skip it.

microsoft/vscode Agent Skills author: microsoft MIT 1 file body ≈ 2 609 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Symbolicate a native VS Code crash dump (.dmp) using electron-minidump. Use when given a crash dump file, asked to symbolicate a crash, resolve missing method…

As a process B 66/100 · Nearly there — weak spots: result and completion, when it triggers

ProcedureVS CodeGitHubSoftware developmenttype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
When it triggers w 12
20
Tools and files w 18
60
the three weakest of ten parameters · all ten

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 · 0

    ✓ No critical or high findings

    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 66/100

    • 0Result and completion. Does not say what the result is
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 16 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2609 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (8 tags): a typed call is more reliable

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 294: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (12 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.