标签：#shellscript

For three mornings in a row, my audit log printed the same line: published today: 0 / target: 3 . Nothing crashed. The scripts ran, exited, and produced nothing. The entire cause turned out to be a 41-second margin — a 300-second timeout against a process that actually takes 259 seconds. Changing one number to 600 turned 0/3 into 3/3 the next morning. Some background: I went from earning 100k yen a month as a university student to 600k a month juggling multiple gigs, then lost all of it overnight to a company-initiated layoff. Over the following six months I built an autonomous Claude Code environment, and I'm now above 1.2M yen in monthly revenue. At the core of it is a system that publishes three affiliate articles every morning without a human touching anything. Why this system works The difference between people who keep earning from affiliate marketing and people who drop out is not writing skill, and not a nose for picking products. It's whether you can keep going . Articles that tend to earn on Rakuten Affiliate share a common pattern: spec-comparison articles about home appliances and gadgets priced above 50,000 yen, with lots of reviews and in stock. Robot vacuums, portable power stations, heat-pump washer-dryers, fully automatic coffee makers. The search intent is "I want to compare before I buy," so product link click-through is high and it fits the structure of affiliate marketing well. The problem is cost. Researching the specs of a high-ticket appliance on the web, building a comparison table, and finishing an article good enough to include the "honestly weak points" section takes 30 to 40 minutes. Three articles is close to two hours. Almost nobody has the willpower to repeat that 365 days a year. I don't either. What you need here isn't "trying harder" — it's an environment that keeps running even when you don't try hard . Once the system is built, the running cost is just API calls. The affiliate-factory I built is a simple structure made of four sh

Your Claude Code setup doesn't break in one dramatic moment — it degrades a few bytes at a time, and by the time you notice, you've been paying a context tax for weeks. In a previous post I covered running an unattended daily health check with launchd. This one is the follow-up: a three-layer loop that detects that quiet degradation weekly and hands the repair job to claude -p itself. The problem: environments rot quietly if you leave them alone Some things in a Claude Code environment grow just from doing your normal work. ~/.claude/rules/ and MEMORY.md keep getting appended to, until context injection quietly crosses 40KB Experimental agent definition .md files never get archived, leaving dozens to nearly a hundred files under ~/.claude/agents/ permanently loaded Stop hooks fire over and over, creating a hook spam condition Frustration-signaling words pile up in conversation logs and nobody notices A performance audit on 2026-07-11 revealed that "agents I thought I'd archived were still being injected — 99 of them," and that turned out to be the main cause of the degraded experience. That led to the question "so do I have to go check this every week myself?" — and the answer was to automate it , which is what cc-self-audit.sh does. Five degradation metrics and their thresholds The script measures five metrics and flags "red" when any of them crosses its threshold. # 閾値（env変数で上書き可） TH_INJECT_BYTES = " ${ SELF_AUDIT_TH_INJECT :- 40000 } " # rules+CLAUDE.md+MEMORY.md 合計バイト TH_AGENTS = " ${ SELF_AUDIT_TH_AGENTS :- 60 } " # ~/.claude/agents 配下 .md 総数（再帰） TH_STOPSPAM = " ${ SELF_AUDIT_TH_STOPSPAM :- 15 } " # 監査hook発火/週 TH_FRUSTRATION = " ${ SELF_AUDIT_TH_FRUST :- 8 } " # 不満ワード/週 TH_TOOLERR = " ${ SELF_AUDIT_TH_TOOLERR :- 400 } " # tool失敗/週 The first three are static metrics (state at this exact moment); the last two are dynamic metrics (trends since the previous run). That distinction maps directly onto how each one is measured, as described below. Overall design: a thr