Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add chhoumann/t3-pstack --skill automate-megit clone --depth 1 https://github.com/chhoumann/t3-pstackWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/chhoumann/t3-pstack/automate-me)<a href="https://agentmods.dev/skills/chhoumann/t3-pstack/automate-me"><img src="https://agentmods.dev/badge/skills/chhoumann/t3-pstack/automate-me/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/chhoumann/t3-pstack/automate-me"><img src="https://agentmods.dev/badge/skills/chhoumann/t3-pstack/automate-me.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00070 | $0.01766 |
| Opus 5.5 | $0.00028 | $0.00706 |
| Sonnet 5.5 | $0.00014 | $0.00353 |
| Haiku 4.5 | $0.00007 | $0.00177 |
Grade A, and why
automate-me scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
83% identical to automate-me — 29 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automate me
A guided flow for turning the user's working conventions into a skill agents will follow. The output is one -mode skill tailored to them (e.g. jay-mode, priya-mode).
This skill orchestrates three others: an inline mining pass (see step 1), the harness's skill-authoring skill (skill-creator where installed, else the Agent Skills format), and the unslop skill (prose discipline). It sequences them. It doesn't replace them.
Flow
0. Check for an existing skill
Look recursively for *-mode/SKILL.md in the project's and the user's skills directories (.claude/skills/ and ~/.claude/skills/ on Claude Code, .agents/skills/ and ~/.agents/skills/ on Codex and most other harnesses) matching the user's handle. Mode skills can live in a personal category directory (<skills dir>/<handle>/), not only at the top level. If one exists, confirm intent with a multiple-choice question (AskUserQuestion on Claude Code, numbered options in plain text elsewhere) (unless they already said "update my skill" or similar):
- Update the existing skill (default for repeat runs)
- Start fresh (rare, ask why before doing it)
Update mode changes the rest of the flow:
- Step 1 mines only history since the skill was last edited (
git log -1 --format=%cI <path>). - Step 2 asks what's changed or missing, not what to capture from zero.
- Step 4 edits the existing file in place. Preserve sections the user hasn't contradicted. Revise ones with new evidence. Add new sections only for genuinely new rules.
1. Mine their history
Locate this project's conversations before fanning out. They are T3 threads: t3_thread_list lists them newest first and t3_thread_search searches them, both within this thread's project. Use only that project. Don't search other projects. That crosses workspace boundaries and reads private chats from unrelated projects.
Survey recent agent conversations within that scope for recurring patterns. Run multiple parallel subagents across slices of history (e.g. last 2-4 weeks, split into 3 slices so each has enough material). Pick each subagent's reasoning level and fast or standard speed for the job, per the Subagents section of the poteto-mode skill. Each slice mining subagent reads, with t3_thread_read, the threads the parent lists for its slice, looks for the signals below, and returns a short structured list of patterns it saw with evidence pointers. Default signals worth hunting:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · -3 lines · -2 tokens per session 3db0f8aa703e
- 3d ago First seen · 108 lines · 72 tokens per session scan A cec3ea561bd0
automate-me is a skill published in the GitHub repository chhoumann/t3-pstack (190 stars, last pushed yesterday), licensed MIT. It adds 70 tokens to every session and 1,766 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 83% identical to automate-me, differing in 29 lines, and is treated as a copy.
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