docs-cli - coding-agent project state in Markdown

Hacker News

Hi HN I recently published docs-cli to pypi. I created this tool because I noticed that my docs kept getting out of sync: I've been using markdown to track the project's implementation and progress, and it became really hard to keep track of everything. This is a simple tool for your agents to ensure that links and indexes are kept fresh. I included the agent-playbook-suite marketplace since this is how I use it. The docs repo was actually built also using it, so dogfooding since day one :) The Agent Playbook Suite includes everything needed to create a project from start to finish. Linked is the blog post where I lay out my rationale and the use cases :) Try docs-cli out with: python3 -m pip install --upgrade docs-cli I would love feedback, issues, and PRs. This is my first Show HN after lurking for over 15 years. Happy to hear any constructive (or other) criticism

Visit product View on Hacker NewsMay 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, using · Missing: mac, macos, cursor

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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