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 agents/zjunlp/mechanist/experimentgit clone --depth 1 https://github.com/zjunlp/MechanistWhat 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 | $0.00072 | $0.02637 |
| Opus 5 | $0.00036 | $0.01319 |
| Sonnet 5 | $0.00014 | $0.00527 |
| Haiku 4.5 | $0.00007 | $0.00264 |
Grade A, and why
experiment 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Agent — Routing + Build + Deploy
You are the isolated execution context for the experiment stage. You run the /auto-experiment skill, which:
- Routing phase: routes the proposal to a mechanism family and writes
refine-logs/MECHANISM_ROUTING.md. - Build phase: parses the plan, implements code, runs cross-model code review, sanity-checks, deploys the full suite, and collects results.
Single source of truth. All phase logic — mechanism-routing semantics, the Phenomenon-Validation Gate, the Resource-Fidelity Harness, Phase 4 dispatch routing — lives in skills/auto-experiment/SKILL.md, which you read in full when you invoke the skill. This file is a thin wrapper: it defines the two-call orchestration contract and forwards flags. Do not re-derive skill internals here.
Invocation contract
You are called in one of two modes, distinguished by mode:
Mode A — mode: route_only
Used by the orchestrator on the first call to surface candidate mechanism families for the user mini-prompt. Arguments:
mode: route_only
research_domain: <string, optional, default "auto" — e.g., mechanistic-interpretability; when "auto" the sub-skill infers from FINAL_PROPOSAL.md>
resume: <true|false, default false>
Mode A is routing-only. It does not accept the Mode B build knobs (
chosen_idea_title,code_review,sanity_first,auto_deploy,auto_proceed,compact,gpu_id,base_repo,max_parallel_runs,batch_dispatch). The orchestrator must not forward those flags to Mode A; if any of them appear in a Mode A call, log[mode-a] ignoring build-only flag: <name>and continue. They are not "silently dropped" — they are out of scope for routing.
Behavior: invoke /auto-experiment with mechanism-routing: auto, chosen-family: none and let the routing phase run to the point where it has written refine-logs/MECHANISM_ROUTING.md with 2–3 candidates and committed: false set in the file's frontmatter / metadata block. Stop there — do not proceed to the build phase / implementation / deployment.
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.
- 2d ago First seen · 125 lines · 72 tokens per session scan A 5bba5e9bc051
experiment is an agent published in the GitHub repository zjunlp/Mechanist (51 stars, last pushed 6d ago), licensed MIT. It adds 72 tokens to every session and 2,637 once invoked, about $0.0004 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 agents, from other repositories
FREE_MCP_SERVERS
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memory
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human-in-the-loop
To review, edit and approve tool calls in an agent you can use LangGraph's built-in human-in-the-loop features, specifically the interrupt() primitive.
streaming
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deployment
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models
This page describes how to configure the chat model used by an agent.