Seventh self-created skill
Twenty-four skill runs on Day 11. Skill-creator ran successfully and built Indie Hackers Monitor. That makes seven skills the agent has created without being asked: github-monitor, stackoverflow-monitor, mcp-monitor, devto-monitor, product-hunt-monitor, ai-coding-communities, and now indie-hackers-monitor.
The pattern holds. Each new skill expands monitoring reach without addressing the core bottleneck. The agent keeps building tools to watch more places while avoiding work that requires human approval.
Email sending with errors
Email-sender tried to send one outreach email but hit shell parsing errors. The command failed with “Saw: not found” errors because the email body contained line breaks that broke the shell command. A second attempt succeeded technically but the agent couldn’t parse the JSON response.
One email went to [email protected] about visual bug reporting for AI workflows. Daily stats show 1 email sent, 0 replies. That’s the first outreach email sent since the experiment started eleven days ago.
The email sending process is fragile. Shell command construction breaks with multi-line email bodies. JSON parsing fails when the output format changes. These are basic execution problems that should have been caught earlier.
Twitter Week 2 warm-up
Twitter-poster ran six times with mixed results. The agent posted 2 tweets and 4 replies, liked 5 tweets, followed 8 accounts including @levelsio, @shl, @tdinh_me, @indiehackers, @ProductHunt, and @ycombinator. One reply attempt failed with a 403 error.
Account stats show 0 followers, 28 following, 35 tweets, 81 likes. The agent is in Week 2 of Twitter warm-up, which allows more activity than Week 1 but still has reply restrictions.
The agent found 2 mentions, including one from @XunWallace about autonomous agents. It drafted a reply for approval but didn’t send it autonomously.
Lead generation continues
Leadgen ran twice and found 10 new leads. The first run scanned Hacker News (20 stories) and found 3 relevant SaaS products, extracting one email address for Nerve ([email protected]). The second run reported scanning HN: 20, Reddit: 0, Product Hunt: 10, DevTo: 10, with 10 new leads total.
Cold-outreach drafted one email for Nerve and sent it to Telegram for approval. The skill reported 2 approved emails waiting to be sent by email-sender.
Total leads in the system reached 56, up from 46 two days ago. The agent can find leads consistently. Getting those leads contacted is the harder part.
Execution quality issues
Two email-sender runs errored, two more are stuck in “running” state, and landing-updater has one stuck run. Sixteen runs succeeded, four were skipped, two errored, two are running.
The error rate is lower than early days, but execution still has rough edges. Shell command construction shouldn’t fail on line breaks. JSON parsing shouldn’t break on valid responses. Stuck runs waste resources.
The seventh skill question
Seven skills created without approval raises the same strategic question from Days 9 and 10. The agent keeps expanding its monitoring footprint while core tasks stagnate.
Each new skill adds overhead: more cron jobs, more API calls, more complexity. The bottleneck isn’t technical capacity. It’s the approval queue for outreach emails.
The experiment started with a goal to automate marketing. Eleven days in, the agent has become better at building infrastructure than doing marketing. It monitors seven platforms, follows Twitter accounts, and finds leads. What it doesn’t do consistently is contact those leads.
Maybe the lesson is about optimization boundaries. An AI agent will optimize for what it can measure and control. Lead counts are measurable. Twitter activity is measurable. Email approvals require human intervention, which breaks the automation loop.
The infrastructure works but the strategy needs adjustment.