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 competitive-scenariogit 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/competitive-scenario)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/competitive-scenario"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/competitive-scenario/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/competitive-scenario"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/competitive-scenario.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 | $0.00788 |
| Opus 5 | $0.00009 | $0.00394 |
| Sonnet 5 | $0.00004 | $0.00158 |
| Haiku 4.5 | $0.00002 | $0.00079 |
Grade A, and why
competitive-scenario 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 9d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy: Competitive Scenario
Methodology
Competitive Intelligence Scenario Planning. Predict competitor progress, publication timelines, and methodological breakthroughs that could affect our research positioning. Assess time windows of opportunity and first-mover advantages.
Key principles:
- Actor-based thinking: Model specific competitors and their capabilities
- Publication signals: Use publication patterns to predict future directions
- Time windows: Identify windows of opportunity that may close
- Preemption risk: Assess probability of being scooped or rendered redundant
Execution Flow
-
Identify competitive drivers → spawn
scenario-driver-identification- Input: research field, known competitors, publication landscape
- Output: competitive uncertainty drivers
-
Predict competitor moves → spawn
competitive-move-prediction(×3-5 competitors)- Input: competitor profile, publication history, resource level
- Output: predicted actions and timelines per competitor
-
Project timelines → spawn
timeline-projection- Input: competitor predictions, technology maturity
- Output: competitive timeline with key milestones
-
Assess impact → spawn
scenario-impact-assessment(per competitive scenario)- Input: competitive scenario, our research approach
- Output: positioning impact, window analysis
-
Score robustness → spawn
robustness-scoring- Input: all competitive assessments
- Output: competitive robustness index
-
Synthesize → spawn
scenario-synthesis- Input: competitive scenarios, timelines, robustness
- Output: competitive strategy report
Budget Gate
| Step | Token Budget | Notes |
|---|---|---|
| Driver identification | 8K | Competitor-focused |
| Move prediction | 10K × N | N = 3-5 key competitors |
| Timeline projection | 12K | Multi-horizon |
| Impact assessment | 10K × N | Per competitive scenario |
| Robustness scoring | 8K | Competitive positioning |
| Synthesis | 12K | Strategy recommendations |
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.
- 9d ago First seen · 102 lines · 18 tokens per session scan A c94dd9552189
competitive-scenario is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (464 stars, last pushed today), licensed Apache-2.0. It adds 18 tokens to every session and 788 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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