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 skills/emaballarin/ccplugins/startnpx skills add emaballarin/ccplugins --skill startgit clone --depth 1 https://github.com/emaballarin/ccpluginsWhat 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 | $0.00166 | $0.01248 |
| Opus 5 | $0.00083 | $0.00624 |
| Sonnet 5 | $0.00033 | $0.00250 |
| Haiku 4.5 | $0.00017 | $0.00125 |
Grade A, and why
start 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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ar:start — begin an autoresearch run
Stand up the loop: dedicated branch, ./.ar/ state, a real harness, and a
measured noise floor. Starting does not iterate — /ar:resume does that.
First action, always
Check for existing state before anything else:
ls -la ./.ar/ar.jsonl 2>/dev/null && git branch --show-current
If ar.jsonl already exists, this is not a fresh run. Print the status block and
say so — offer /ar:resume to continue or an explicit new-segment start
over. Never silently overwrite a run in progress.
Hard rules
- Confirm before creating. If this fired on inferred rather than stated intent, describe what opening a loop would do and confirm it is wanted before step 2. Ordinary optimisation — making something faster, tuning a few hyperparameters, running a benchmark — is handled directly. A loop is for many measured iterations under a locked harness, and it is not free: it branches and commits.
- Never act on a branch this run did not create. Create
ar/<slug>-<DD-MM-YYYY>, carrying the current working tree across — uncommitted and untracked files included — then commit it immediately as the revert floor. Full sequence in${CLAUDE_PLUGIN_ROOT}/references/protocol.md§0. - Never fabricate a benchmark. The harness is filled in by the operator, and the loop does not start until it emits a real number. An invented benchmark makes every number downstream of it fiction.
- Locked harness.
benchmark.sh,checks.sh,evaluator.pyand the code emitting the metric are never edited, for the whole run. A score is only meaningful while the thing measuring it holds still. - State lives only under
./.ar/. Nothing is written outside the target repo, and no global or shared store is touched. - Defer execution. Print exact commands for long runs; do not launch training inside the session.
./.ar/is gitignored as.ar/*plus!.ar/final_report.md— which is also what keepsgit clean -fdfrom eating the loop's own state.
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 First seen · 89 lines · 166 tokens per session scan A 35f11f03ce66
start is a skill published in the GitHub repository emaballarin/ccplugins (3 stars, last pushed 26d ago), licensed MIT. It adds 166 tokens to every session and 1,248 once invoked, about $0.0008 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 2026-08-31.
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