Borrowing it
Nothing to install: this file belongs to tellahq/opensession. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tellahq/opensession/main/.agents/skills/pstack-suite/skills/automate-me/SKILL.mdgit clone --depth 1 https://github.com/tellahq/opensessionWrote 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/tellahq/opensession/automate-me)<a href="https://agentmods.dev/skills/tellahq/opensession/automate-me"><img src="https://agentmods.dev/badge/skills/tellahq/opensession/automate-me.svg" alt="Measured on agentmods" 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 | $0.00072 | $0.01700 |
| Opus 5 | $0.00036 | $0.00850 |
| Sonnet 5 | $0.00014 | $0.00340 |
| Haiku 4.5 | $0.00007 | $0.00170 |
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 3d ago.
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- automate-me — 89% identical, 34 lines differ
- automate-me — 89% identical, 34 lines differ
How it starts
The opening of the file, as written. The whole thing — 108 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 repository skill conventions (authoring), and the unslop skill (prose discipline). It sequences them; it doesn't replace them.
Flow
0. Check for an existing skill
Look recursively for .agents/skills/**/*-mode/SKILL.md and .agents/skills/*-mode/SKILL.md matching the user's handle. Mode skills can live in a personal category directory (.agents/skills/<handle>/), not only at the top level. If one exists, confirm intent with one concise user question (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
Discover the policy-gated Open Session session and history tools before fanning out. Identify sessions by exact id and explicit creator. Read only relevant transcript windows returned by those tools. Never scan session files, databases, or global transcript storage.
Survey recent agent conversations within that scope for recurring patterns. Run multiple ask-mode /pstack child sessions across slices of the gated history results (e.g. last 2-4 weeks, split into 3 slices so each has enough material). Each slice mining child session reads transcripts from the workspace-scoped path the parent provides, 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.
- 3d ago First seen · 108 lines · 72 tokens per session scan A 90abe4ed0ff1
automate-me is a skill published in the GitHub repository tellahq/opensession (355 stars, last pushed yesterday), licensed MIT. It adds 72 tokens to every session and 1,700 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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