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 comparative-feasibility-rankinggit 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/comparative-feasibility-ranking)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/comparative-feasibility-ranking"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/comparative-feasibility-ranking/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/comparative-feasibility-ranking"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/comparative-feasibility-ranking.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 | $0.00021 | $0.00748 |
| Opus 5 | $0.00010 | $0.00374 |
| Sonnet 5 | $0.00004 | $0.00150 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
comparative-feasibility-ranking 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comparative Feasibility Ranking
Purpose: Produce a defensible ranking of candidates by feasibility. Uses multi-dimensional radar charts to visualize relative strengths and a weighted feasibility index to collapse multiple dimensions into a single comparable score.
When to use:
- Multiple candidates have been assessed and need to be compared
- Stakeholders need a clear ranking to prioritize resource allocation
- You need to identify which candidates are most implementable given current constraints
Budget
| Metric | Target |
|---|---|
| Candidates compared | >= 2 |
| Dimensions in radar | >= 5 |
| Weight justifications | 1 per dimension |
State Ledger
| Key | Type | Description |
|---|---|---|
| candidates[] | array | All candidates being compared |
| dimension_weights{} | map | Dimension -> weight mapping |
| radar_data[] | array | Per-candidate radar scores |
| feasibility_index[] | array | Weighted composite scores |
| ranking[] | array | Final ranked list |
Available Tactics
| Tactic | When |
|---|---|
| multi-dimensional-readiness-scan | To generate per-candidate radar data for comparison |
| staged-gate-evaluation | To compare gate-passage likelihood across candidates |
Available SOPs
| SOP | Purpose |
|---|---|
| radar-synthesis | Produce radar data for each candidate |
| feasibility-synthesis | Produce final comparative matrix |
Execution Guidance
- Ensure all candidates have been assessed on the same dimensions
- Normalize scores to a common scale (1-9 recommended)
- Assign dimension weights based on context (stakeholder priorities, strategic fit)
- Calculate weighted feasibility index for each candidate
- Produce comparative radar visualization data
- Rank candidates and identify clear tiers (strong/moderate/weak feasibility)
Output Format
comparative_ranking:
dimensions: [technical, market, regulatory, resource, organizational]
weights: {technical: 0.3, market: 0.25, regulatory: 0.2, resource: 0.15, organizational: 0.1}
candidates:
- {name, scores: {...}, weighted_index: 0.X, rank: N, tier: strong|moderate|weak}
radar_data: [{candidate, dimension_scores: [...]}]
recommendation: <top candidate(s) with rationale>
caveats: [...]
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 · 98 lines · 21 tokens per session scan A f550234035b6
comparative-feasibility-ranking 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 21 tokens to every session and 748 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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