Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add drivelineresearch/autoresearch-claude-code/plugin install autoresearchWrote 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/drivelineresearch/autoresearch-claude-code/autoresearch)<a href="https://agentmods.dev/commands/drivelineresearch/autoresearch-claude-code/autoresearch"><img src="https://agentmods.dev/badge/commands/drivelineresearch/autoresearch-claude-code/autoresearch/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/commands/drivelineresearch/autoresearch-claude-code/autoresearch"><img src="https://agentmods.dev/badge/commands/drivelineresearch/autoresearch-claude-code/autoresearch.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 | $0.00014 | $0.00544 |
| Opus 5 | $0.00007 | $0.00272 |
| Sonnet 5 | $0.00003 | $0.00109 |
| Haiku 4.5 | $0.00001 | $0.00054 |
Grade A, and why
autoresearch 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.
What it actually says
Autoresearch command
Arguments: $ARGUMENTS
Read ${CLAUDE_PLUGIN_ROOT}/skills/autoresearch/SKILL.md and follow its protocol.
Claude substitutes that plugin path in this command's text; it need not exist as
an environment variable in Bash. For a manual install, where the placeholder is
not expanded, read ~/.claude/skills/autoresearch/SKILL.md instead. Resolve
AR_SCRIPTS to the scripts directory beside the file you read. Do not invoke
autoresearch through the Skill tool again: this adapter shares that name and
can shadow the shared skill. If neither known path exists, pause and report the
missing installation rather than searching outside those locations.
Handle the requested mode before any resume or setup action:
- off: create
.autoresearch-offin the experiment workspace, preserve partial work, and stop. Do not launch another experiment. - status: read state/dashboard/recent worklog and show current segment budget,
baseline/best, counts, and pause status. Do not resume or change experiment data.
For a live Claude session, first create
.autoresearch-inspectso the Stop hook permits this inspection turn to end. This small control write is the only write. - report: for a live Claude session create
.autoresearch-inspect; writeautoresearch-report.mdwith results, evidence limits, winning changes, failures, and remaining ideas. Do not run experiments or unpause. This mode writes a report. - resume (session exists): read session/state/worklog and git status/log. Resolve
partial/unlogged work first and check the budget. Remove
.autoresearch-offonly when explicitly resuming within budget; do not reset the config to gain runs. An exhausted budget needs a user extension or deliberate new experiment contract. - fresh goal (no session): verify git and preserve existing work, then follow skill setup. Infer goal/metric/scope from supplied arguments where possible.
A user stop, cancellation, or new scope overrides continuation. Complete one experiment per autonomous turn; the Claude Stop hook handles the next turn when valid state and budget permit it. Never treat a plugin code review as a request to start an optimization session.
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 Changed · -15 lines · +5 tokens per session c4f2fbbdbf67
- 10d ago First seen · 63 lines · 9 tokens per session scan A 7a7404d4ab9f
autoresearch is a command published in the GitHub repository drivelineresearch/autoresearch-claude-code (342 stars, last pushed yesterday), licensed MIT. It adds 14 tokens to every session and 544 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
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.