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/dosatos/minimise/onboardgit clone --depth 1 https://github.com/dosatos/minimiseWrote 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/dosatos/minimise/onboard)<a href="https://agentmods.dev/commands/dosatos/minimise/onboard"><img src="https://agentmods.dev/badge/commands/dosatos/minimise/onboard.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 | $0.00011 | $0.00358 |
| Opus 5 | $0.00005 | $0.00179 |
| Sonnet 5 | $0.00002 | $0.00072 |
| Haiku 4.5 | $0.00001 | $0.00036 |
Grade A, and why
onboard 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 4d 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
Read the latest project handoff to orient yourself:
cat worklogs/handoffs/session-latest.md
Execution rule: delegate to mini job, don't implement inline
For any real implementation work this session, delegate it to a mini job
rather than editing files directly in this conversation. Reserve direct edits
for trivial one-liners and for mini-blocked changes (schema migrations,
internal refactors mini can't invoke).
Two reasons this is the default:
- Keeps this context clean. Execution/implementation churn (file dumps, test output, agent narration) stays inside the job, not in the orchestrating session — so this context stays about decisions, not diffs.
- Quality gates come free. A job run through the
/minimise:review-planskill gets its plan reviewed before execution — the gates are already wired, you just have to use the path.
The flow:
# 1. Write a plan.yaml (scratch plans -> worklogs/scratch/)
# 2. Review it (adds the quality gate): invoke the /minimise:review-plan skill
mini job new --plan worklogs/scratch/<plan>.yaml
mini job start <job-id>
mini job status <job-id> # watch progress
mini job results <job-id> # logs + diffs when done
Context loaded. $ARGUMENTS
If you'd like to continue with a specific task, just tell me what to work on!
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.
- 4d ago First seen · 46 lines · 11 tokens per session scan A e6ffadf5a7ba
onboard is a command published in the GitHub repository dosatos/minimise (11 stars, last pushed 15d ago), licensed MIT. It adds 11 tokens to every session and 358 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.