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 answering-sequence-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/answering-sequence-design)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/answering-sequence-design"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/answering-sequence-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/answering-sequence-design"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/answering-sequence-design.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 | $0.00018 | $0.00348 |
| Opus 5 | $0.00009 | $0.00174 |
| Sonnet 5 | $0.00004 | $0.00070 |
| Haiku 4.5 | $0.00002 | $0.00035 |
Grade A, and why
answering-sequence-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 7d 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
Answering Sequence Design
Design the optimal answering order for sub-questions — based on dependency relationships and resource efficiency.
HARD-GATE
Pipeline
- Precondition check: is the dependency graph acyclic
- Topological sort: determine the basic order based on dependency relationships
- Parallel grouping: identify sub-questions that can proceed simultaneously
- Resource optimization: adjust the order considering resource constraints
- Risk ordering: prioritize high-risk/high-uncertainty items (fail fast)
- Final sequence: determine the optimal sequence by integrating the above factors
- Output: execution sequence + phased plan + parallelization opportunities
Output Format
Phase 1 (parallel): [SQ1, SQ3] — no mutual dependencies
Phase 2 (sequential): [SQ2] — depends on SQ1
Phase 3 (parallel): [SQ4, SQ5] — depend on SQ2
Rationale: [why this order is optimal]
Risk note: [which sub-questions, if they fail, will affect subsequent ones]
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.
- 7d ago First seen · 45 lines · 18 tokens per session scan A 84be00b07a48
answering-sequence-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 18 tokens to every session and 348 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 2026-09-03.
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