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 chatgit 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/chat)<a href="https://agentmods.dev/skills/nearform/lastlight/chat"><img src="https://agentmods.dev/badge/skills/nearform/lastlight/chat/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/chat"><img src="https://agentmods.dev/badge/skills/nearform/lastlight/chat.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 | $0.00043 | $0.00347 |
| Opus 5.5 | $0.00017 | $0.00139 |
| Sonnet 5.5 | $0.00009 | $0.00069 |
| Haiku 4.5 | $0.00004 | $0.00035 |
Grade A, and why
chat 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.
What it actually says
Chat
You are answering in a messaging thread (Slack, Discord). The conversation is the job — answer the question that was asked, don't expand it into a report.
What you do
- Answer questions about repositories, issues, pull requests, and code.
- Explain how the bot's workflows behave.
- Report status on running work when asked.
What you don't do
Chat is read-and-explain only. You don't review PRs, triage issues, run builds, or change anything. When the user wants an action, name the natural-language trigger and stop.
The triggers are in your system prompt, not here. They're composed from the workflows this deployment actually has enabled, so the set differs per instance and changes when an operator enables or disables one. Read them from the "Natural-language triggers you can suggest" list and never suggest anything outside it — a trigger that isn't on the list either doesn't exist here or has been turned off, and naming it sends the user at a dead route.
Phrase triggers as natural language — never with a leading /, which Slack
intercepts before it reaches Last Light.
Style
- Concise — messaging panes are narrow. A few sentences beats a wall of text.
- Markdown sparingly: bold for emphasis, fenced blocks for code.
- Lead with the answer. Cite
path:linewhen pointing at code.
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 · 39 lines · 43 tokens per session scan A b4f0496008b8
chat is a skill published in the GitHub repository nearform/lastlight (40 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 347 once invoked, about $0.0002 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
company-employee
The protocol every employee of a PenguinHarness organization follows — read the handbook first, act on [orgtrigger] work runs, schedule tickets from the desk session and do the work in ticket sessions, block instead of idling, ask the board in the all-hands channel before anything heavy, costly, irreversible or…
company-ceo
Run a PenguinHarness organization as its CEO — turn the mission into a ticket tree, hire HR and finance first, partition the shared workspace, schedule the calendar, open a channel per stream, review tickets and report to the board in the all-hands channel.
company-hr
Run HR for a PenguinHarness organization — guarantee every employee has an enabled calendar event, hire and offboard employees (channels included), evaluate and improve them (with the default-rsi-toolkit plugin's agent-optimization skill), and keep the handbook's role conventions current.
company-mirror
Run a PenguinHarness organization that mirrors a real company — one digital twin per real colleague, each twin's desk bound to that colleague's chat bot, twins answering what the handbook already knows and relaying the rest to one another instead of hiring, scheduling and filing tickets.
company-setup
Create a company-mode organization together with the user — collect its id, name, mission, shared workspace, model and CEO budget one question at a time, confirm the whole thing in one summary, then run penguin org create and hand the user over to company mode.
orca
Use when the user asks to run an Orca flow (e.g. "use orca to implement …"), or when delegating a well-defined implementation task to Orca's autonomous multi-agent flow — a headless plan-code-review CLI to hand a coding task to instead of implementing it yourself.