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 yogsoth-ai/de-anthropocentric-research-engine --skill ablation-designgit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote 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/yogsoth-ai/de-anthropocentric-research-engine/ablation-design)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/ablation-design"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/ablation-design/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/yogsoth-ai/de-anthropocentric-research-engine/ablation-design"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/ablation-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Prompt Injection · line 53 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00015 | $0.00604 |
| Opus 5 | $0.00008 | $0.00302 |
| Sonnet 5 | $0.00003 | $0.00121 |
| Haiku 4.5 | $0.00002 | $0.00060 |
Grade A, and why
ablation-design 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 11d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy: Ablation Design
Question: What does each component contribute?
Methodology
- Systematic Ablation (Newell 1974): Remove one component at a time, measure degradation.
- Replacement Ablation: Replace component with simpler alternative to isolate contribution.
- Combinatorial Ablation (ABLATOR): Test component subsets to detect interaction effects.
- Conditional Ablation: Ablate components under specific data conditions to find context-dependent contributions.
Execution Flow
- ablation-component-mapping → Map system architecture to ablatable units
- baseline-selection → Select full-system and minimal-system anchors
- metric-specification → Define metrics that capture component contribution
- sample-size-estimation → Determine runs needed for reliable delta estimation
- statistical-method-selection (tactic) → Choose appropriate significance tests for deltas
Budget Gate
| Ablation Type | Components (N) | Min Runs | When to Use |
|---|---|---|---|
| Systematic (leave-one-out) | 3-8 | N + 2 | Standard component analysis |
| Replacement | 3-8 | 2N + 2 | Need to distinguish "removal" vs "simplification" |
| Combinatorial (selected) | 4-6 | ~2N | Suspected interactions between components |
| Combinatorial (full) | 3-4 | 2^N | Small systems, need complete picture |
| Conditional | 3-6 | N * conditions | Context-dependent contributions |
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| statistical-method-selection | Select appropriate statistical methods for experiment analysis |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| ablation-component-mapping | Map system architecture to ablatable units for ablation studies |
| baseline-selection | Select appropriate baselines for experimental comparison |
| metric-specification | Define experiment metrics and significance standards |
| sample-size-estimation | SOP: power analysis and required experiment count estimation |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 75 lines · 15 tokens per session scan A 3b0962fa52e2
ablation-design is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (456 stars, last pushed yesterday), licensed Apache-2.0. It adds 15 tokens to every session and 604 once invoked, about $0.0001 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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