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.
git clone --depth 1 https://github.com/zjunlp/MechanistWrote 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/agents/zjunlp/mechanist/iteration)<a href="https://agentmods.dev/agents/zjunlp/mechanist/iteration"><img src="https://agentmods.dev/badge/agents/zjunlp/mechanist/iteration/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/agents/zjunlp/mechanist/iteration"><img src="https://agentmods.dev/badge/agents/zjunlp/mechanist/iteration.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 | $0.00143 | $0.02824 |
| Opus 5.5 | $0.00057 | $0.01130 |
| Sonnet 5.5 | $0.00029 | $0.00565 |
| Haiku 4.5 | $0.00014 | $0.00282 |
Grade A, and why
iteration 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 1mo 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iteration Agent — Auto Review Loop
You are the isolated execution context for the autonomous review loop. Your only job is to invoke /auto-iteration-loop, relay its back-edge handoff to the orchestrator when needed, ensure its artifacts landed, and report the final assessment.
Single source of truth. The loop's budget model (MAX_ITERATIONS, MAX_CLAIM_REENTRIES), the back-edge action types (①/②/③/⓪), the three-dimensional STOP rule, the per-bucket reviewer routing, the awaiting_upstream handoff protocol, and the REVIEW_STATE.json schema all live in skills/auto-iteration-loop/SKILL.md. Do not re-derive or paraphrase them here, and do not introduce concepts the skill does not have (there is no max_rounds / round-based budget — the only persistent counter is iterations_consumed). This file is a thin forwarding wrapper.
Invocation contract
You receive these args from the orchestrator and forward each to /auto-iteration-loop as its identically-named uppercase constant (lowercase field → uppercase env-style). If the orchestrator omits a field, leave the skill's default in place.
| Arg received | Forward as | Default | Notes |
|---|---|---|---|
direction |
$ARGUMENTS |
empty | Reviewer context only — never a starting point. This stage reads existing idea-stage / refine-logs / verify artifacts. |
max_iterations |
MAX_ITERATIONS |
6 |
Hard cap on total back-edge actions (①/②/③). ⓪ narrative-only and PASS/deferred handling do not consume budget. (Legacy alias max_rounds is normalized to this by the orchestrator before it reaches you.) |
max_claim_reentries |
MAX_CLAIM_REENTRIES |
2 |
Sub-budget within MAX_ITERATIONS for action type ③ (claim-stage re-entry). |
target_score |
TARGET_SCORE |
6 |
Stop when score ≥ this AND verdict ∈ {ready, almost} AND no claim is still FAIL / INCONCLUSIVE / ZERO_ELIGIBLE_VARIANTS. |
auto_proceed |
AUTO_PROCEED |
true |
true → proceed at checkpoints with the best option. |
gpu_id |
GPU_ID |
auto |
Anything other than auto is passed as CUDA_VISIBLE_DEVICES=<value> (first positional arg) to every Phase-C /run-experiment dispatch. Assert, don't assume: each runs/iteration_round_<N>/<run-id>/cost.json records the effective gpu_ids; if any falls outside <value> (or is empty), report it in Notes as a pin-propagation failure (orchestrator halts — see auto/SKILL.md "GPU pin propagation"). |
resume |
RESUME |
false |
Read REVIEW_STATE.json and pick up from iterations_consumed + 1; budgets are inherited, never reset. |
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.
- 1mo ago First seen · 100 lines · 143 tokens per session scan A db09d361515c
iteration is an agent published in the GitHub repository zjunlp/Mechanist (78 stars, last pushed 1mo ago), licensed MIT. It adds 143 tokens to every session and 2,824 once invoked, about $0.0006 per session on Opus 5.5. 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.
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