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 agentmods add commands/jeet129/praxis/drivegit clone --depth 1 https://github.com/jeet129/praxisWrote 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/commands/jeet129/praxis/drive)<a href="https://agentmods.dev/commands/jeet129/praxis/drive"><img src="https://agentmods.dev/badge/commands/jeet129/praxis/drive.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 | $0.00035 | $0.00912 |
| Opus 5 | $0.00017 | $0.00456 |
| Sonnet 5 | $0.00007 | $0.00182 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade B, and why
drive scanned grade B with 1 finding 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 5d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
- Marketplace/plugin install (Claude Code): `find ~/.claude/plugins -name praxis-drive.sh 2>/dev/null | head -1` How it starts
The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Start or resume autonomous drive.
Step 1 — Check the dial
Read governance/autonomy.yaml. Note stop_after (task | slice | phase | gate), run_budget, and stall.max_iterations_without_ledger_change. This is the OPTIONAL human boundary — the three non-negotiable stops (decision points, governance gates, budget/stall/exhaustion) fire regardless of this setting.
Step 2 — Locate or confirm the active ledger
Look for .project/working/slice-<id>-tasks.yaml for the current slice (per .project/working/active-workflow.md). If none exists, there's nothing to drive yet — route through /slice, which delegates to delivery-lead → lead-developer to produce the packet AND the task ledger (per references/loop-contracts.md §2) before drive can start. Do not fabricate a ledger yourself.
If a ledger exists, confirm it against the user: slice id, task count, how many are already done, and any existing stop_flags.
Step 3 — Explain the budget and stops once
Before iterating, tell the user (once, not every iteration):
- the
stop_afterdial and what it means for this run run_budgetcaps (max_slices_per_run,max_iterations_per_run,max_task_attempts,cost_ceiling_proxy)- the three non-negotiable stops that fire regardless of the dial
- that slice-close summaries post to
.project/telemetry/summaries/even when the loop doesn't wait
Step 4 — Run it
Offer both paths; let the user pick (or infer from context — a long-running/background request implies unattended):
- Unattended: instruct the user to run the runner script, which invokes the harness headlessly per
governance/autonomy.yaml'sharnessesblock, enforcing iteration caps and stall detection outside the agent's own context. Locating it: the script ships inside the PLUGIN, not the project. Resolve the path first:- Marketplace/plugin install (Claude Code):
find ~/.claude/plugins -name praxis-drive.sh 2>/dev/null | head -1 install.shfile install:./scripts/praxis-drive.shin the project- Git clone used via
--plugin-dir:<clone>/scripts/praxis-drive.shThen run:bash <resolved-path> --project-dir . --dry-runfirst, and without--dry-runwhen satisfied. If the script is not found anywhere, the installed plugin predates drive mode — update the plugin. In-session drive (above) needs no script at all.
- Marketplace/plugin install (Claude Code):
- In-session: iterate the drive protocol yourself, continuously, within this session — each iteration is
delivery-leadexecuting exactly oneautonomous-drivepass (seeagents/delivery-lead.md's Drive mode section) and reporting the outcome. After a pass returns, immediately begin the next one — do NOT report a completed task and wait for me. Nothing re-invokes delivery-lead in this path; you are the loop. Keep going through the slice drain (code review, security review, QA, closure) and into the next ledger, honoring the same stops as the unattended path. Stop cleanly ONLY when a non-negotiable stop or the dial's boundary is actually reached — a finished task understop_after: gateis not one.
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.
- 5d ago First seen · 43 lines · 35 tokens per session scan B 5f1a34745fdb
drive is a command published in the GitHub repository jeet129/praxis (7 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 912 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.