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 sfc-gh-eraigosa/dotfiles --skill research-evaluationgit clone --depth 1 https://github.com/sfc-gh-eraigosa/dotfilesWrote 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/sfc-gh-eraigosa/dotfiles/research-evaluation)<a href="https://agentmods.dev/skills/sfc-gh-eraigosa/dotfiles/research-evaluation"><img src="https://agentmods.dev/badge/skills/sfc-gh-eraigosa/dotfiles/research-evaluation/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/sfc-gh-eraigosa/dotfiles/research-evaluation"><img src="https://agentmods.dev/badge/skills/sfc-gh-eraigosa/dotfiles/research-evaluation.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 | $0.00234 | $0.02085 |
| Opus 5 | $0.00117 | $0.01043 |
| Sonnet 5 | $0.00047 | $0.00417 |
| Haiku 4.5 | $0.00023 | $0.00209 |
Grade A, and why
research-evaluation 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
research-evaluation — evaluate before adopting
Turn "should we use ?" into a consistent, evidence-backed evaluation instead of an ad-hoc impression. Works for one target or a batch; generalizes to any repo.
The rubric (all nine, every target)
| Dim | Question |
|---|---|
| (a) Value | What is it worth to us — which of our real problems does it solve, and what do we already have that overlaps or conflicts? |
| (b) Setup cost & licensing | Install steps, prerequisites, pain points; the exact license, commercial tiers, telemetry/data terms. |
| (c) Adversarial review | The case AGAINST adopting: negatives, dangers, unknown pitfalls, failure modes, lock-in. Written to refute (a). |
| (d) Security & safety | Known and unknown gotchas: what it executes, what data it touches/stores/sends, supply-chain surface, CVEs/advisories. |
| (e) Stability | Likelihood it destabilizes our workflow or running services; maturity, breaking-change history, blast radius. |
| (f) Quality & support | Maintenance signals with the observation date: stars, contributors/bus factor, release cadence, issue responsiveness, docs, last commit. |
| (g) Demo | A workable sandboxed demo to validate first-hand: quickstart + real use case + success criteria. Docker if possible; otherwise skip the demo entirely — no unsandboxed demos. |
| (h) Borrowable features (build-vs-adopt) | For each valuable capability the tool has that our stack lacks, could we implement just that feature in our existing setup more simply than adopting the whole tool? Table it: gap → value → build-it-ourselves sketch → worth it? Ground the sketches in what we already run. This can flip the verdict to reject-but-build-the-feature — often the simpler, safer conclusion. |
| (i) Business outcomes | The financial/ROI vector: does this move us toward financially positive — efficiency gains, hard-time saved, cost that pays for itself, or a step toward a self-propelling revenue outlet? Tag qualitative tiers (low/med/high) for time saved, cost savings, and revenue potential; fold them in at this dimension's weight. Even small value counts — the point is to add a money vector to a decision that is otherwise all opinion. |
What ships with it
5 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 · 130 lines · 234 tokens per session scan A 0ea56610758c
research-evaluation is a skill published in the GitHub repository sfc-gh-eraigosa/dotfiles (46 stars, last pushed yesterday), licensed Apache-2.0. It adds 234 tokens to every session and 2,085 once invoked, about $0.0012 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 2026-08-30.
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