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 ask-obstacle-acceptancegit 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/ask-obstacle-acceptance)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/ask-obstacle-acceptance"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/ask-obstacle-acceptance/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/ask-obstacle-acceptance"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/ask-obstacle-acceptance.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 | $0.00042 | $0.00210 |
| Opus 5 | $0.00021 | $0.00105 |
| Sonnet 5 | $0.00008 | $0.00042 |
| Haiku 4.5 | $0.00004 | $0.00021 |
Grade A, and why
ask-obstacle-acceptance 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ask-obstacle-acceptance — 100% identical, 4 lines differ
What it actually says
Ask Obstacle Acceptance
Get user's acceptance of the obstacle landscape.
Execution
Dialogue — inline, no subagent.
What to Present
Each obstacle with:
- Severity assessment
- Proposed mitigation
- Estimated effort
What to Ask (one at a time)
- Given these obstacles and mitigations, can you accept this direction?
- Are there difficulties I missed that you're aware of?
- Is there any obstacle here that's a complete deal-breaker?
Search
Optional — may use the 5 imported skills if user raises new concerns that need investigation.
If Unacceptable
Signal to tactic that direction needs to change (return to present-candidates in direction-narrowing tactic).
Output
User's acceptance decision + any new concerns raised.
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.
- 8d ago First seen · 41 lines · 42 tokens per session scan A 2f8aeb78d441
ask-obstacle-acceptance is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (462 stars, last pushed 2d ago), licensed Apache-2.0. It adds 42 tokens to every session and 210 once invoked, about $0.0002 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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