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BC codebase-onboarding

Gathers facts about a codebase (stack, layers, entry points, setup gaps) and turns them into onboarding docs: architecture overview, file map, setup guide, debugging guide. Use when onboarding engineers or re-documenting after a refactor.

borghei/Claude-Skills Agent Skills author: borghei 12 files · 5 scripts body ≈ 2 209 tokens Open the sourcegithub.com↗ analyzed 8 h ago

Gathers facts about a codebase (stack, layers, entry points, setup gaps) and turns them into onboarding docs: architecture overview, file map, setup guide…

As a process C 64/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

ProcedureNotionConfluenceSoftware developmentWriting and documentstype 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%
99
Quality 40%
87
Run on models
none yet
Process rating
C
64/100
Has gaps
When it triggers w 12
20
Running it twice w 4
30
Result and completion w 14
40
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration read-dotenv references/documentation-templates.md:102
      Reads a .env file
      cp .env.example .env

    Files scanned: 11. 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 64/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, git, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 37 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2209 tokens
    • 100Progress reporting. Reports progress
    • low 10 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)
    • +3Output format is not stated: the model decides each time
    • -5TODO / placeholder text left in the skill
    • +2Single-language instructions
    • +3Description length 238: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 37 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 3 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.