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AC debug-enhancement-framework

Adds structured JSON logging, error classification, retry with exponential backoff and full jitter, circuit breaking, performance profiling, state capture, and auto-healing to any skill or script, in Python and Bash. Use when a skill or agent needs debugging, error recovery, resilience against flaky network or rate-limited calls, thundering-herd-safe retries, crash diagnostics, performance profiling, memory monitoring, or a reproduce-diagnose-fix-verify workflow for an existing bug.

ClawHub Agent Skills author: orionshaowswmw v2.1.4 MIT-0 10 files · 1 script body ≈ 1 821 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Adds structured JSON logging, error classification, retry with exponential backoff and full jitter, circuit breaking, performance profiling, state capture…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "categories"
    • note frontmatter-key unknown frontmatter key "topics"

    Process rating: all ten parameters 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 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, python) that frontmatter does not declare
    • 100Steps. 6 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1821 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 487: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 6 items
    • +4Has examples (6 code blocks)
    • +3All 3 scripts are documented

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

    External checks

    ClawHub: suspicious
    This is a real debugging framework, but it exposes powerful command, install, process, network, and file-write behaviors with some weak scoping.
    LLM: suspicious (high) · 6 Sept 2026