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BC capgo-cli-usage

Entry point for the Capgo CLI (`npx @capgo/cli@latest`). Maps the full command surface (init, login, doctor, probe, app, bundle, channel, key, account, organization, build, observe, get-qr, notifications, run device, mcp), explains API-key precedence (--apikey, CAPGO_TOKEN, ~/.capgo, ./.capgo), connects agents to Capgo through the hosted MCP (https://api.capgo.app/mcp) or the local CLI MCP, and routes to the narrower Capgo skill. Use when the user asks generally how to use the Capgo CLI, which command to run, how to authenticate it, how to connect Claude or another agent to Capgo, or gets "No Capgo API key found". Do not use when a specific Capgo skill already matches (capgo-live-updates, capgo-release-management, capgo-native-builds, capgo-organization-management).

Cap-go/capgo-skills Agent Skills author: Cap-go 2 files body ≈ 2 058 tokens Open the sourcegithub.com↗ analyzed 9 h ago

Entry point for the Capgo CLI (npx @capgo/cli@latest).

As a process C 63/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationAI 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
30
Running it twice w 4
30
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: capgo-cli-usage (Cap-go/capgo-skills)

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: 2. 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 63/100

    • 0Result and completion. Does not say what the result is
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 14 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 27 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2058 tokens
    • 100Progress reporting. Reports progress
    • low 10 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (7 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
    • +3Output format is not stated: the model decides each time
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 776: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 27 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.