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CC phone-harness

Control the user's phone - an iPhone through the Mac's iPhone Mirroring window, an Android over adb, a rented cloud Android, or a cloud iPhone over HTTPS: open apps, tap, type, swipe, read the screen.

ShawnPana/phone-harness Agent Skills author: ShawnPana 51 files · 34 scripts body ≈ 8 730 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Control the user's phone - an iPhone through the Mac's iPhone Mirroring window, an Android over adb, a rented cloud Android, or a cloud iPhone over HTTPS…

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

ProcedureGitHubData and analyticstype 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
C
85/100
safety, quality, tests
Safety 60%
99
Quality 40%
63
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Inputs and preconditions w 11
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token src/phone_harness/telemetry.py:21
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    POSTHOG_KEY = "phc_…TKt"
    quoted

Files scanned: 36. 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 ≈ 8730 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 16 mutating operations with no state check
  • 40Execution cost. Instruction body is 8730 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, git, python) that frontmatter does not declare
  • 100Steps. 87 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • high The skill tells the model to perform an irreversible action with no human approval
  • low The response is described with custom markup (8 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)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 200: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 87 items
  • +4Has examples (4 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.