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 zjunlp/Mechanist --skill hypothesis-batchgit 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/skills/zjunlp/mechanist/hypothesis-batch)<a href="https://agentmods.dev/skills/zjunlp/mechanist/hypothesis-batch"><img src="https://agentmods.dev/badge/skills/zjunlp/mechanist/hypothesis-batch/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/zjunlp/mechanist/hypothesis-batch"><img src="https://agentmods.dev/badge/skills/zjunlp/mechanist/hypothesis-batch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00016 | $0.10616 |
| Opus 5.5 | $0.00006 | $0.04246 |
| Sonnet 5.5 | $0.00003 | $0.02123 |
| Haiku 4.5 | $0.00002 | $0.01062 |
Grade A, and why
hypothesis-batch 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 — 424 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow: Claim Stage — Behavior Discovery × Mechanism Discovery
Orchestrate the claim stage for: $ARGUMENTS.
Overview
This skill chains sub-skills into a single automated, non-interactive pipeline:
/research-lit → /idea-creator → /novelty-check → /impact-check → /research-review → /research-refine-pipeline
(survey) (brainstorm) (verify novel) (verify it (critical feedback) (refine method + plan)
matters)
Batch shape: 30 in, 10 out. Phase 2 brainstorms ~30 candidate ideas across three angle-partitioned /idea-creator rounds. Novelty (Phase 3) is a hard gate; impact (Phase 3.5) and external review (Phase 4) then score every survivor, and Phase 4.25 makes the single cut to 10 — vetoing fatal design flaws outright, then ranking by impact first, reviewer score second, novelty last. From Phase 4.5 on, each of the 10 is worked independently and in parallel, and each gets its own directory.
Deliverables.
idea-stage/
RESEARCH_LIT.md # raw retrieval dump (audit-only)
LANDSCAPE.md # synthesized landscape
IDEA_REPORT.md # all ~30 ideas, ranked, with eliminations — the batch index
claims/
01_<name>/ # one directory per surviving idea, rank-ordered
FINAL_PROPOSAL.md
EXPERIMENT_PLAN.md
claim.json # ⭐ the deliverable a human reviewer reads
02_<name>/
…
10_<name>/
claim.json is the point of the whole run: a self-contained paper skeleton written for a human expert reviewer, who sees that file and nothing else. Everything above it is working material. Its specification is Phase 4.8 and is binding.
Pipeline
Phase 1: Literature Survey
Invoke /research-lit to map the research landscape:
/research-lit "$ARGUMENTS"
What this does:
- Search Zotero, Obsidian, local PDFs, the web, and the cloud mechanic-db SEARCH service (via the skill
/mechanic-db-search) for relevant papers - Build a landscape map: sub-directions, approaches, open problems
- Identify structural gaps and recurring limitations
- Output two files:
idea-stage/RESEARCH_LIT.md(raw retrieval dump, audit-only) andidea-stage/LANDSCAPE.md(synthesized landscape — Phase 2 reads this from disk)
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 · 424 lines · 16 tokens per session scan A 5123f7f3377d
hypothesis-batch is a skill published in the GitHub repository zjunlp/Mechanist (78 stars, last pushed 1mo ago), licensed MIT. It adds 16 tokens to every session and 10,616 once invoked, about $0.0001 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.
Other skills, from other repositories
nanoresearch-writing
Draft a LaTeX research paper from all previous stage outputs.
nanoresearch-experiment
Generate a Python code skeleton from an experiment blueprint.
nanoresearch-ideation
Search academic literature and generate research hypotheses.
scienceworld-measurement-taker
Use when the agent needs to measure a quantitative property (temperature, weight, pH) of a target object or substance using a measurement tool. This skill covers acquiring the tool, preparing both tool and target with focus actions, executing the measurement via "use [TOOL] on [TARGET]", and interpreting the resulting…
scienceworld-object-selector
Use when the agent needs to choose a specific object from multiple candidates in the environment based on task criteria such as object type (non-living thing, electrical component, container), properties, or category. This skill surveys visible objects with "look around", evaluates each against the task requirements…
scienceworld-threshold-evaluator
Use when the agent has just obtained a numerical measurement (temperature, weight, pH) and must compare it against a predefined threshold to determine a binary outcome. This skill extracts the measured value, evaluates it against the threshold condition (above/below), and executes the corresponding branch action such…