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BC ghostty-use

Snapshots, restores and reconciles Claude Code / Codex sessions inside Ghostty tabs across reboots. Use when quitting the Mac for an update (保存终端会话 / 重启前备份终端), after restart to reopen tabs with their original session IDs (恢复之前的窗口 / restore my ghostty tabs), or to audit which sessions survived. Not for resuming one conversation via its own --resume, terminal screenshots, or tmux state.

daymade/claude-code-skills Agent Skills author: daymade MIT 5 files · 2 scripts body ≈ 1 353 tokens Open the sourcegithub.com↗ analyzed 87 min ago

Snapshots, restores and reconciles Claude Code / Codex sessions inside Ghostty tabs across reboots.

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerSoftware developmentAI and agentsFinancetype 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
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token scripts/test_ghostty_session.py:53
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      path = os.path.join(d, "roll…00-" + sid + ".jsonl")
      fixturequoted

    Files scanned: 5. 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 50/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 85Steps. 10 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1353 tokens

    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
    • -2localhost URLs: will not work for another user
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 387: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 10 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

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