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 skills add primeline-ai/claude-adaptive-research --skill auto-rungit clone --depth 1 https://github.com/primeline-ai/claude-adaptive-researchWrote 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/primeline-ai/claude-adaptive-research/auto-run)<a href="https://agentmods.dev/skills/primeline-ai/claude-adaptive-research/auto-run"><img src="https://agentmods.dev/badge/skills/primeline-ai/claude-adaptive-research/auto-run/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/primeline-ai/claude-adaptive-research/auto-run"><img src="https://agentmods.dev/badge/skills/primeline-ai/claude-adaptive-research/auto-run.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 | $0.00070 | $0.00110 |
| Opus 5 | $0.00035 | $0.00055 |
| Sonnet 5 | $0.00014 | $0.00022 |
| Haiku 4.5 | $0.00007 | $0.00011 |
Grade A, and why
auto-run 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 12d 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
This skill is handled by the /auto-run command. Invoke it via the Skill tool or directly as a slash command.
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.
- 12d ago First seen · 8 lines · 70 tokens per session scan A b28f3e87dc2e
auto-run is a skill published in the GitHub repository primeline-ai/claude-adaptive-research (12 stars, last pushed 3mo ago), licensed MIT. It adds 70 tokens to every session and 110 once invoked, about $0.0003 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 skills, from other repositories
diligence-deck
Produce structured acquisition due-diligence findings from deal inputs and a private data room. Triggers on "run diligence", "due diligence on", "diligence deck", "DD findings", "acquisition analysis", "data room", "ingest data room", "/diligence".
inference-engineer
Productize an open-source model into a hosted inference endpoint the researcher (or their agent) can call. Picks the right hardware, the right serving stack (vLLM / Triton / TEI / BentoML), wraps it in an OpenAI-compatible gateway (LiteLLM) with per-tenant auth, exposes it as an MCP tool in chat, and runs a quality +…
agent-builder
Build any goal-declared agent end to end. Turns one declared goal into a portable, self-hardening, publishable agent repo on the Claude-Code harness. Declare goal, pick runtime/tools and write policies/hooks/safeguards, scaffold a portable repo (config-over-code split), build, run a fresh no-memory adversarial…
autoresearch
Canonical around-the-clock research loop. Defines the agent's outer loop — read taste corpus + queue, pick the next experiment, mutate the explicitly-declared mutation surface, run the experiment under a hard time budget against a frozen metric, score, codify, repeat. Augmented with Karpathy's sharp primitives (frozen…
experiment
Run a materials-science / ML compute job on Rockie GPU capacity. Trigger words "run experiment", "submit job", "/experiment", or requests to quote/approve GPU spend before an experiment. Picks the right GPU type and count from a natural-language description (DFT for QE/VASP/ABINIT, MD for GROMACS/LAMMPS/OpenMM…
upstream-contribute
Scan the current session for harness-level patterns that would be useful to other rockie users, then either package a reviewed local harness patch or dispatch a public upstream contribution PR. Uses Scout/Generator/Verifier/Updater separation, never auto-merges, and requires human sign-off before pushing. Triggers…