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 sananthanarayan/skilldrop --skill ai-use-case-triagegit clone --depth 1 https://github.com/sananthanarayan/skilldropWrote 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/sananthanarayan/skilldrop/ai-use-case-triage)<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/ai-use-case-triage"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/ai-use-case-triage/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/sananthanarayan/skilldrop/ai-use-case-triage"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/ai-use-case-triage.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 | $0.00119 | $0.01316 |
| Opus 5 | $0.00060 | $0.00658 |
| Sonnet 5 | $0.00024 | $0.00263 |
| Haiku 4.5 | $0.00012 | $0.00132 |
Grade A, and why
ai-use-case-triage 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 10d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-use-case-triage
Most AI adoption stalls not from lack of ideas but from too many, all sounding equally promising. This produces a ranked portfolio with rejections: what to do first, what to hold, and the condition that would move a held item onto the list.
A triage that promotes everything has triaged nothing. The not-yet list is the deliverable's spine.
How to respond
-
Inventory the candidates. From whatever the user brought — a brainstorm, a backlog, interview notes. Each candidate is stated as a workflow and a person, not a technology: ✅ "Support agents drafting first-response replies" — ❌ "Use LLMs in support." If a candidate is a technology looking for a job, say so and either recast it or drop it.
-
Score each on three axes, 1–5, and show the weights. Default weights: value 40 · feasibility 35 · risk 25. State them, and restate them if the user's context justifies different ones (a regulated environment legitimately weights risk higher). Hidden weights make a ranking an opinion.
- Value — time recovered, quality lifted, or revenue/cost moved. Quantify where the input allows; mark
directionalwhere it doesn't. Never invent a number. - Feasibility — is the input data reachable, is the output checkable, does the workflow already have a review step? A task whose output nobody can verify is not feasible, however easy the prompt.
- Risk — blast radius if the output is wrong and someone acts on it. Score the consequence, not the technology's reputation.
- Value — time recovered, quality lifted, or revenue/cost moved. Quantify where the input allows; mark
-
Rank, then sanity-check the top. The highest score wins only if its failure is survivable and visible. A high-value, high-risk item where a wrong answer reaches a customer unreviewed is not a first slice, whatever the arithmetic says — demote it and say why.
-
Name the first slice, and what it proves. One use case, with the observable that would confirm or kill the thesis and a time box. ✅ "Draft first-response replies for the top 3 ticket categories; the thesis dies if agents edit more than half the draft body after 4 weeks." A first slice with no kill condition is a pilot that runs forever.
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.
- 10d ago First seen · 60 lines · 119 tokens per session scan A e8e7a587c660
ai-use-case-triage is a skill published in the GitHub repository sananthanarayan/skilldrop (2 stars, last pushed 26d ago), licensed MIT. It adds 119 tokens to every session and 1,316 once invoked, about $0.0006 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
weights-and-biases
W&B: log ML experiments, sweeps, model registry, dashboards.
flash-attention
Speed up long-sequence transformer training and inference.
nemo-curator
Curate LLM training data: dedupe, filter, PII redaction.
llava
Vision-language chat: VQA, captioning, image dialogue.
claude-api
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude…
simpo
Reference-free preference alignment, simpler than DPO.