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BF claude-code-dev

How to develop, modify, and extend the Claude Code codebase following its established programming style, patterns, and conventions. Use this skill whenever working on the claude-code source — adding tools, commands, utils, components, types, hooks, services, or fixing bugs. Also use when the user asks about claude-code architecture, coding standards, file organization, or wants to understand how the codebase works. This skill ensures all contributions match the existing codebase style exactly.

The skillemall take

The skill promises a guide for Claude Code development — architecture, patterns, coding style. 16 files, 680 body tokens, no scripts. Checks found no critical issues, quality scored 77, but process score sits at 32%. No model runs or sandbox tests.

Files are in place, syntax is clean, but the skill hasn't been tested in real conditions — unclear how it handles actual coding tasks. Useful as a style reference, but install knowing this is more of a foundation than a finished tool.

chatgptprojects/clear-code Agent Skills author: chatgptprojects 16 files body ≈ 680 tokens Open the sourcegithub.com↗ analyzed 5 d ago

How to develop, modify, and extend the Claude Code codebase following its established programming style, patterns, and conventions.

As a process F 32/100 · No process to follow — weak spots: steps, result and completion, inputs and preconditions

ProcedureSoftware 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
F
32/100
No process to follow
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
0
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: 16. 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 32/100

    • 0Steps. Prose only: no discrete steps
    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (claude-code-dev) differs from the folder (claude-code-skills)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Execution cost. Instruction body is 680 tokens
    • 100Running it twice. No mutating operations

    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)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 498: enough signal without eating the budget
    • +4Structure: 3 headings

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.