Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add geledek/enterprise-ai-transformation-skills/plugin install enterprise-ai-transformation-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/geledek/enterprise-ai-transformation-skills/tech-data-deployment)<a href="https://agentmods.dev/skills/geledek/enterprise-ai-transformation-skills/tech-data-deployment"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/tech-data-deployment/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/geledek/enterprise-ai-transformation-skills/tech-data-deployment"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/tech-data-deployment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00179 | $0.02934 |
| Opus 5 | $0.00089 | $0.01467 |
| Sonnet 5 | $0.00036 | $0.00587 |
| Haiku 4.5 | $0.00018 | $0.00293 |
Grade A, and why
tech-data-deployment 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tech — Data-Trust Deployment Pattern
A four-dimension assignment of data sensitivity class to deployment pattern with explicit per-tier controls. Anchored to NIST AI RMF (GOVERN/MAP/MEASURE/MANAGE), ISO/IEC 42001, EU AI Act risk tiers, and IMDA Model AI Governance Framework + GenAI Companion.
Empirical anchor: BCG 2025 broad-use-vs-value 88/25 gap — 88% of organizations use AI broadly, only ~25% capture material value; the gap is largely a data-trust and deployment-pattern gap, not a model gap. (A different BCG 88/25 finding — manager role-modeling — is cited in people-readiness-conversation and people-literacy-curriculum; always read "88/25" here as broad-use-vs-value.) MIT 95% — most enterprise GenAI pilots fail to reach production, and shadow-AI usage runs ~2x sanctioned usage in the surveyed cohort.
Verdict vocabulary (stable output contract): Approved / Conditional / Blocked, with assigned deployment tier and per-tier control checklist.
Dimension 1: Data Sensitivity Classification
Core question: What class of data does this workflow actually touch — and which regulatory regime governs it?
Classify the data INPUT, the data IN-FLIGHT (prompt + retrieval), and the data OUTPUT separately. The most sensitive of the three sets the tier.
CLASS LADDER (assign one):
- PUBLIC — already published; no confidentiality cost if exposed.
- INTERNAL — non-public business data, low individual harm if leaked (org charts, internal docs, generic SOPs).
- CONFIDENTIAL — commercially sensitive (M&A, pricing, source code, unreleased product, supplier contracts, legal privilege).
- REGULATED — named regime applies. Identify which:
- PII under GDPR (EEA), PDPA (SG), CCPA (CA)
- PHI under HIPAA (US health) — requires BAA
- PCI-DSS (cardholder data)
- MNPI under MAS (SG financial), MiFID II (EU), SEC Reg FD (US)
- Student records under FERPA (US education) / equivalent
- Cross-border transfer triggers — GDPR Chapter V, China PIPL, India DPDP
What ships with it
2 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 · 193 lines · 179 tokens per session scan A f5b061ed465c
tech-data-deployment is a skill published in the GitHub repository geledek/enterprise-ai-transformation-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 179 tokens to every session and 2,934 once invoked, about $0.0009 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.
Other skills, from other repositories
visualize
Visualize the Semantica knowledge graph — topology, centrality, communities, paths, embeddings, decision insights, and temporal evolution. Uses GraphAnalyzer, CentralityCalculator, CommunityDetector, PathFinder, and ContextGraph analytics. Sub-commands: topology, centrality, community, path, decision-graph, insights…
embed
Generate, inspect, and use node/text embeddings in Semantica — compute Node2Vec embeddings, find similar nodes, score link predictions, batch similarity, and pairwise similarity. Uses NodeEmbedder, SimilarityCalculator, LinkPredictor, and AgentContext. Sub-commands: compute, similar, similarity, predict-link…
reason
Run reasoning over the Semantica knowledge graph — deductive logic, abductive hypothesis generation, Datalog programs, SPARQL queries, Rete network evaluation. Uses DeductiveReasoner, AbductiveReasoner, DatalogReasoner, SPARQLReasoner, ReteEngine. Sub-commands: deductive, abductive, datalog, sparql, rete, prove…
validate
Validate Semantica pipelines, extraction quality, graph schemas, and ontology consistency. Returns structured error/warning checklists. Uses PipelineValidator, PipelineBuilder.validatepipeline(), GraphValidator, and OntologyValidator. Sub-commands: pipeline, step, dependencies, extraction, graph, ontology, performance.
temporal
Temporal graph operations on Semantica — scoped queries at a point in time, graph snapshots, node change timelines, temporal causal analysis, and graph state reconstruction. Uses AgentContext.findprecedents(asof=), ContextGraph.stateat(), CausalChainAnalyzer.traceattime(), and TemporalQueryRewriter. Sub-commands…
extract
Run the full Semantica semantic extraction pipeline on a file or selected text — NER, relations, events, coreference resolution, triplets, and validation. Clears result cache before each run. Returns Markdown tables with entity/relation/event/triplet results and inline validator warnings.