SKILLEMALL.ai
GitHub ↗ RU Sign in

BC repo-json-generator

Convert Git repository code to structured JSON instructions for AI agents. Fetches code from Git repositories (GitHub, GitLab, etc.), generates structured JSON instructions for accurate code updates and processing. Platform-agnostic tool for AI agent workflows.

ClawHub Agent Skills author: xiaoanso v3.2.1 MIT-0 19 files body ≈ 8 099 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 55/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

GeneratorGitHubGitLabSoftware 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
B
84/100
safety, quality, tests
Safety 60%
98
Quality 40%
62
Run on models
none yet
Process rating
C
55/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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
  • low Secrets in code secret-password-literal README.md:403
    Hard-coded password / key literal (may be an example)
    Input:  https://x-access-token:gh…@….com/user/repo.git
  • low Secrets in code secret-password-literal SKILL.md:447
    Hard-coded password / key literal (may be an example)
    Input:  https://x-access-token:gh…@….com/user/repo.git

Files scanned: 19. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 8099 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 55/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 31 mutating operations with no state check
  • 40Consistency. Frontmatter name (repo-json-generator) differs from the folder (miaoda-app-chat-sync)
  • 40Execution cost. Instruction body is 8099 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 131 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 40 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (10 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)
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -224 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 261: enough signal without eating the budget
  • +4Structure: 95 headings
  • +3Step-by-step instructions: 131 items
  • +3Output format is stated explicitly
  • +4Has examples (52 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
The skill has a legitimate repo-to-JSON purpose, but it handles credentials and downstream file-overwrite instructions in ways that need human review before use.
LLM: suspicious (high) · VirusTotal: · 29 May 2026