Borrowing it
Nothing to install: this file belongs to vlasenkoalexey/tpu_performance_autoresearch_wiki. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vlasenkoalexey/tpu_performance_autoresearch_wiki/main/.claude/skills/formulate-hypothesis/SKILL.mdgit clone --depth 1 https://github.com/vlasenkoalexey/tpu_performance_autoresearch_wikiWrote 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/vlasenkoalexey/tpu_performance_autoresearch_wiki/formulate-hypothesis)<a href="https://agentmods.dev/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/formulate-hypothesis"><img src="https://agentmods.dev/badge/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/formulate-hypothesis/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/vlasenkoalexey/tpu_performance_autoresearch_wiki/formulate-hypothesis"><img src="https://agentmods.dev/badge/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/formulate-hypothesis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.08729 |
| Opus 5 | $0.00000 | $0.04365 |
| Sonnet 5 | $0.00000 | $0.01746 |
| Haiku 4.5 | $0.00000 | $0.00873 |
Grade A, and why
formulate-hypothesis 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.
How it starts
The opening of the file, as written. The whole thing — 502 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are formulating one optimization hypothesis for the autoresearch loop. The output of this skill MUST be a structured proposal that the master agent reviews before dispatching. Do not skip steps.
Step 1 — Gather inputs from caller / session context
The caller (typically the loop in /start-experiment Step 9, step 2(c)(i)) provides:
<model>+<lane>— e.g.<model>+jax<variant>— current variant being optimized (e.g.3B/v5p-16,24B/v5p-32)<mode>(optional, defaultfrontier) — one of:frontier(default): variant has a measured-best baseline and regression would be costly. Full ceremony. Pick this when the model page's variant matrix shows a non-trivial Current best for the variant.exploration: variant is early-stage, stuck, or being probed. Lighter ceremony, hunches allowed. Pick for stalled variants where the ladder has saturated, or when probing a hypothesis category the wiki doesn't yet cover.bootstrap: brand-new model with thin/no per-model state (no per-model refuted-patterns.md, no retrospectives, variant matrix has < 3 rows or no Current best). Even lighter ceremony — Steps 4, 5 degrade gracefully; first-principles reasoning from index + blueprint is the primary input. See the "Bootstrap mode" section below for the auto-detection rule and the full mode-specific override behavior.user-override: the caller has specified an exact candidate (passed in<user_specified_candidate>). Skill skips hypothesis generation and just validates the candidate (refuted-pattern check + topic fit + semantics-preservation check), then emits the proposal.
<user_specified_candidate>(optional, REQUIRED if<mode>isuser-override) — the exact hypothesis the caller wants validated (one sentence, falsifiable form).<topic_hint>(optional) — caller's best guess at the topic. If not provided, infer from recent profile / frontier-shift conversation.<recent_profile_summary>(optional but strongly preferred) — bucket attribution from the most recent xprof. If absent, the proposal should recommend capturing a profile first rather than committing to a candidate.<current_frontier>— MFU + experiment ID of the variant's current frontier<last_2_3_experiments>— what was just tried, with verdicts<in_flight_v_ids>— workload v-IDs other tracks are running, so the proposal steers clear of duplicates
If any input is missing AND not derivable from session context, ask the caller for it before proceeding. Do not invent.
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 · 502 lines · 0 tokens per session scan A 7c2b26841047
formulate-hypothesis is a skill published in the GitHub repository vlasenkoalexey/tpu_performance_autoresearch_wiki (55 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 8,729 tokens. 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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