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 skills/zjunlp/mechanist/steering-block-selectionnpx skills add zjunlp/Mechanist --skill steering-block-selectiongit 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/steering-block-selection)<a href="https://agentmods.dev/skills/zjunlp/mechanist/steering-block-selection"><img src="https://agentmods.dev/badge/skills/zjunlp/mechanist/steering-block-selection.svg" alt="Measured on agentmods" 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 | $0.00249 | $0.00712 |
| Opus 5 | $0.00125 | $0.00356 |
| Sonnet 5 | $0.00050 | $0.00142 |
| Haiku 4.5 | $0.00025 | $0.00071 |
Grade A, and why
steering-block-selection 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 6d 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.
What it actually says
Steering Block / Layer Selection
The point
Where and how many sites you intervene on changes both the effective strength of the intervention and whether a localization claim is testable.
- Too few (single site on a deep stack) → downstream norm/attention denoise it → looks like "no effect".
- Too many (whole stack) → off-distribution collapse → the effect is collapse-driven and no longer localized.
Choosing where to intervene
Pick the site(s) by a screening method, by heuristic, or both.
By method (pre-screen the layers):
- Gradient-based — rank layers by gradient / attribution score w.r.t. the target.
- Activation-based — rank by an activation signal (probe accuracy, diff-mean magnitude, separability).
By heuristic:
- Mid-to-late layers usually work best — start there.
- If a single layer shows no effect, intervene on 3–5 layers instead.
- To sweep the whole stack, use spaced intervals (e.g. every 2–3 layers) rather than every layer.
What to intervene on (transformer)
Target the attention, the MLP, or the residual stream — choose by what the claim is about.
Note: circuit discovery generally studies attention heads + MLP.
Two rules to keep it honest
- Match the claim. If the claim is about a region (e.g. "early layers 0–7"), intervene on a window covering that region and use matched windows elsewhere as null controls — a single site can't adjudicate a regional claim. Report the localization you actually observe; don't rewrite the claim to fit the data.
- Never copy a raw index across models of different depth. "Layer 16 of 32" → scale by relative depth (0.5 → layer 24 of 48), then check neighbors.
Composition
Lock the site set here first (the coefficient plateau is site-dependent), then sweep α via ../steering-coefficient-tuning/. Re-sweep the coefficient whenever the site set changes.
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.
- 6d ago First seen · 42 lines · 249 tokens per session scan A 7ece6cf1ac74
steering-block-selection is a skill published in the GitHub repository zjunlp/Mechanist (55 stars, last pushed 10d ago), licensed MIT. It adds 249 tokens to every session and 712 once invoked, about $0.0012 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.
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