Borrowing it
Nothing to install: this file belongs to pedrocivita/tocket. 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/pedrocivita/tocket/main/.agents/skills/tocket/SKILL.mdgit clone --depth 1 https://github.com/pedrocivita/tocketWrote 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/pedrocivita/tocket/tocket)<a href="https://agentmods.dev/skills/pedrocivita/tocket/tocket"><img src="https://agentmods.dev/badge/skills/pedrocivita/tocket/tocket/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/pedrocivita/tocket/tocket"><img src="https://agentmods.dev/badge/skills/pedrocivita/tocket/tocket.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 | $0.00036 | $0.00498 |
| Opus 5.5 | $0.00014 | $0.00199 |
| Sonnet 5.5 | $0.00007 | $0.00100 |
| Haiku 4.5 | $0.00004 | $0.00050 |
Grade A, and why
tocket 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 1mo 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.
What it actually says
Tocket
Tocket is the shared project notebook (.context/). Agents read and write those files. tocket decide writes the next move as JSON. Workers (Cursor, Claude, GrokBot, CI) execute. Tocket does not.
Before expensive tools: tocket decide --dry-run, or read .context/decisions/.
Harness your context. Jev is the judge, not the writer. Meta-attention handoff: Jev judges chunks; workers read the filtered handoff.
When to use
- Read
AGENTS.mdand.context/before expensive tools. - If
.context/decisions/already has a handoff, honor it. Do not re-ask Jev. tocket decide= judge (Jev or stub).tocket work= plan.tocket work --apply= receipt.- Honor
tool_gate(allow|block|ask). Before bash/deploy/browser:--choice tool_gate:allow,block,ask, thentocket work. tocket handoff --aware --query "..."filters.context/chunks. Receipt:.context/attention/. Filtered handoff:.context/handoffs/.- Gotcha packs live in
.context/gotchas/.tocket packs load --query "..."writes.context/active/packs.md. - After work: update
.context/activeContext.md.
Setup
npx @pedrocivita/tocket init
npx skills add pedrocivita/tocket --skill tocket
tocket doctor
tocket decide --dry-run --state '{"goal":"docs"}' --choice next:research,write,review
- Optional
TYPESAFE_API_KEYfor live Jev. Never paste API keys into chat. - Prefer
--dry-runor--shadowfirst. - Run
tocket doctorfor green/yellow/red setup checks.
Do not
- Do not ask Tocket to write prose, do math, or run workers.
- Do not rebuild the app around Jev. Swap this decision node in.
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.
- 1mo ago Changed · +4 lines ec1e997ca903
- 1mo ago First seen · 37 lines · 36 tokens per session scan A 73f261f30287
tocket is a skill published in the GitHub repository pedrocivita/tocket (38 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 498 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-21.
Other skills, from other repositories
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
agent-memory-discipline
Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
project-memory-keeper
Analyzes the last session to log progress, extract lessons learned, and save them to the project memory instructions.
rag-and-memory
Patterns for Retrieval-Augmented Generation (RAG) and agent memory systems. Retrieves only relevant context, prevents context bloat, and maintains coherent state across sessions.
context-loading
Load minimum necessary context into agent context windows. Prevents token bloat, reduces cost, and improves focus. Only load what the current task needs.
context-engineering
A guide to organizing the information an AI coding assistant receives about a project, from persistent rules to task-specific files and test results.