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 debug-productiongit 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/debug-production)<a href="https://agentmods.dev/skills/nearform/lastlight/debug-production"><img src="https://agentmods.dev/badge/skills/nearform/lastlight/debug-production/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/debug-production"><img src="https://agentmods.dev/badge/skills/nearform/lastlight/debug-production.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 | $0.00066 | $0.01023 |
| Opus 5.5 | $0.00026 | $0.00409 |
| Sonnet 5.5 | $0.00013 | $0.00205 |
| Haiku 4.5 | $0.00007 | $0.00102 |
Grade A, and why
debug-production 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug a Last Light instance via the CLI
Read a running instance's state over its admin API with the lastlight CLI —
no SSH, no poking SQLite or jsonl files on the box. Every command takes
--json for machine-parseable output; prefer it when you need to extract
fields.
0. Connect
The CLI talks to whatever instance you logged into (saved in
~/.lastlight/config.json). Confirm you're pointed at the right one and the
token is valid:
lastlight status # instance URL, server health, token validity
If the token is missing/expired, authenticate (the prod instance URL lives in local agent memory — see the "Last Light production server" note):
lastlight login https://<instance> # browser handoff (password/Slack/GitHub)
lastlight login https://<instance> --password # headless fallback
For one-off targeting without saving, pass --url / --token on any command,
or set LASTLIGHT_URL / LASTLIGHT_TOKEN (env wins over the saved config).
1. Find the broken run
lastlight workflow list --status failed # recent failures
lastlight workflow list --status active # running + paused now
lastlight workflow list --workflow pr-review --limit 50
Copy the run ID from the table.
2. Inspect the run's phases
lastlight workflow log <run-id> # run header + per-phase table (✓/✗, duration, session id, error)
lastlight workflow log <run-id> --follow # then tail the current phase live
The phase table shows which phase failed, its error snippet, and the SESSION
id you need for the transcript.
3. Read / tail the agent transcript
lastlight session list # recent sandbox sessions (TYPE, MODEL, LIVE)
lastlight session log <session-id> # full transcript
lastlight session log <session-id> --follow # live tail (SSE) of an in-flight phase
4. Search across runs
lastlight logs search "ECONNREFUSED" # errors ledger (default)
lastlight logs search "rate limit" --scope all # errors + transcript content
lastlight logs search "could not clone" --scope messages --limit 20
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 · 109 lines · 66 tokens per session scan A 7e56a2c9a8e8
debug-production is a skill published in the GitHub repository nearform/lastlight (40 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 1,023 once invoked, about $0.0003 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
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.
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…
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…
claw-admin
Claw system administration: service management, IM connections, logs, cron, and workspace diagnostics. Use when the user asks to manage claw services, connect/disconnect IM platforms, view logs, or perform system-wide operations.