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 nearform/lastlight --skill lastlight-clientgit clone --depth 1 https://github.com/nearform/lastlightWrote 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/nearform/lastlight/lastlight-client)<a href="https://agentmods.dev/skills/nearform/lastlight/lastlight-client"><img src="https://agentmods.dev/badge/skills/nearform/lastlight/lastlight-client/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/nearform/lastlight/lastlight-client"><img src="https://agentmods.dev/badge/skills/nearform/lastlight/lastlight-client.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 | $0.00101 | $0.00814 |
| Opus 5.5 | $0.00040 | $0.00326 |
| Sonnet 5.5 | $0.00020 | $0.00163 |
| Haiku 4.5 | $0.00010 | $0.00081 |
Grade A, and why
lastlight-client 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 1mo 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Install & configure the Last Light CLI client
The lastlight CLI is a thin HTTP client: it POSTs triggers and reads a running
instance's admin API. As a client it needs only the instance URL and an auth
token, saved to ~/.lastlight/config.json (mode 0600).
1. Install the CLI
command -v lastlight >/dev/null && echo "installed" || npm i -g lastlight
2. Get the server URL
Ask the user for the instance URL (e.g. https://lastlight.example.com). If they
don't know it, they need it from whoever runs the server.
3. Log in
Two paths — pick based on environment:
-
Browser handoff (default, interactive desktop):
lastlight login https://lastlight.example.comOpens the dashboard; the user authenticates with whatever method the server has (password / Slack / GitHub OAuth) and the token is captured automatically.
-
Headless / no browser (SSH, CI, server box):
lastlight login https://lastlight.example.com --passwordPrompts for the admin password and POSTs it to
/admin/api/login. Requires the server to haveADMIN_PASSWORDset.
The token (≈7-day TTL) is saved to ~/.lastlight/config.json.
4. Verify
lastlight status
Confirm: Server healthy, Token valid. If the token shows
expired/invalid, re-run lastlight login.
Overrides & alternatives
- Skip the saved file with env vars or flags (precedence: flags → env → saved
file → default
http://localhost:8644):LASTLIGHT_URL=https://ll.example.com LASTLIGHT_TOKEN=... lastlight status lastlight status --url https://ll.example.com --token ... lastlight logoutclears the saved config.- One-shot wizard:
lastlight setup --clientis equivalent tolastlight login.
What you can do once connected
lastlight chat "hello" # chat with the bot (REPL if no message)
lastlight owner/repo#123 # triage an issue (default, cheap)
lastlight build owner/repo#123 # full build cycle
lastlight triage|review owner/repo[#N] # repo scan or single issue/PR
lastlight health|security owner/repo # repo-level report
lastlight workflow list|log <id> # inspect runs
lastlight session list|log <id> -f # tail a sandbox session
lastlight logs search "<text>" # search errors / transcripts
lastlight approvals list|approve|reject
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.
- 1mo ago First seen · 86 lines · 101 tokens per session scan A 63143e558b74
lastlight-client is a skill published in the GitHub repository nearform/lastlight (41 stars, last pushed 2d ago), licensed MIT. It adds 101 tokens to every session and 814 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
open-code-review
Performs AI-powered code review on Git changes using the ocr CLI from alibaba/open-code-review. Use when the user asks to review code, review a pull request, review staged/unstaged changes, review a commit, or compare branches for code quality issues. Produces line-level review comments and can automatically apply…
shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing…
torch-geometric
Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
pymc-bayesian-modeling
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
umap-learn
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
rowan
Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve…