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 muend/geoai-skills --skill mcda-suitability-analysisgit clone --depth 1 https://github.com/muend/geoai-skillsWrote 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/muend/geoai-skills/mcda-suitability-analysis)<a href="https://agentmods.dev/skills/muend/geoai-skills/mcda-suitability-analysis"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/mcda-suitability-analysis/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/muend/geoai-skills/mcda-suitability-analysis"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/mcda-suitability-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00073 | $0.01255 |
| Opus 5 | $0.00036 | $0.00628 |
| Sonnet 5 | $0.00015 | $0.00251 |
| Haiku 4.5 | $0.00007 | $0.00126 |
Grade A, and why
mcda-suitability-analysis 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MCDA & Suitability Analysis
Purpose: produce suitability maps whose weights, scales, and assumptions are explicit, consistent, and stress-tested. A suitability map without a sensitivity analysis is an opinion with a legend.
Workflow
- Structure: goal → criteria (factors) → constraints. Constraints are binary masks (legal exclusions, water bodies, slope > threshold) applied at the END by multiplication; factors are continuous and weighted. Keep them apart — encoding a constraint as a heavily-weighted factor is a classic error that lets forbidden areas score "acceptable".
- Criteria layers: each factor as a raster on a COMMON grid (same CRS, extent, cell size, snap). Resample categorical layers with nearest, continuous with bilinear; document each.
- Standardization to a common suitability scale (0-1 or 0-255):
- Linear min-max for monotonic "more is better/worse".
- Fuzzy membership (sigmoid/linear with control points) when suitability saturates — justify control points from domain knowledge.
- Categorical layers: explicit reclass table, shown to the user. Direction check: confirm for EVERY layer whether high raw value means high or low suitability (slope: low=good; distance-to-road: usually low=good). Direction bugs survive to the final map invisibly.
- Weights (AHP below, or direct/ranked methods with rationale).
- Aggregation: weighted linear combination (WLC) default; OWA when the decision-maker's risk attitude (AND-like vs OR-like) matters.
- Constraint mask multiply; classify the result (equal interval or quantiles — say which and why); sensitivity analysis; validate against known good/bad sites if any exist.
AHP with consistency enforcement
Pairwise comparisons on Saaty's 1-9 scale; weights from the principal
eigenvector; consistency ratio (CR) must be < 0.10 or the matrix goes back
for revision. Run scripts/ahp_weights.py to compute weights + CR from a
reciprocal comparison matrix (it validates reciprocity and reports λ_max).
What ships with it
3 files 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 · 112 lines · 73 tokens per session scan A 57b93518aeea
mcda-suitability-analysis is a skill published in the GitHub repository muend/geoai-skills (17 stars, last pushed 7d ago), licensed MIT. It adds 73 tokens to every session and 1,255 once invoked, about $0.0004 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 2026-08-31.
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