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 render-examples/nanobot-render --skill long-goalgit clone --depth 1 https://github.com/render-examples/nanobot-renderWrote 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/render-examples/nanobot-render/long-goal)<a href="https://agentmods.dev/skills/render-examples/nanobot-render/long-goal"><img src="https://agentmods.dev/badge/skills/render-examples/nanobot-render/long-goal/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/render-examples/nanobot-render/long-goal"><img src="https://agentmods.dev/badge/skills/render-examples/nanobot-render/long-goal.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 | $0.00035 | $0.01365 |
| Opus 5.5 | $0.00014 | $0.00546 |
| Sonnet 5.5 | $0.00007 | $0.00273 |
| Haiku 4.5 | $0.00003 | $0.00136 |
Grade A, and why
long-goal 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Long-running objectives (long_task / complete_goal)
Use these tools when the user wants multi-turn sustained work on one clear objective (same runner, ordinary tools). Not for trivial one-shot questions.
Start fast
long_task is a lightweight marker. Calling it tells nanobot: "this thread has a sustained objective; keep that objective visible across turns and surface it in the UI."
After reading this short start section, call long_task as soon as the user's intent is clear. Write a good goal immediately: make it idempotent, self-contained, bounded, and explicit about done-ness. Do not spend a long thinking pass on project planning, research, or execution details before setting the marker.
Before the first long_task call, you do not need to:
- design the full project plan,
- research APIs or documentation,
- write an exhaustive project plan or checklist,
- decide every file, command, or verification step.
Those belong to the execution phase after the marker is set.
Tools
-
long_task— Register one sustained objective per thread. Call it promptly once the user has asked for a sustained task. Thegoalshould follow the idempotent-goal rules below, but it should be produced quickly from the user's request—not after a long hidden planning pass. -
complete_goal— Close bookkeeping for the current active goal. Call when work is done, and also when the user cancels, changes direction, or replaces the objective: userecapto state honestly what happened (e.g. cancelled, partially done, superseded). Then you may calllong_taskagain for a new objective after the session shows no active goal (or after the user agrees to replace).
If a goal is already active and the user wants something different, complete_goal first (honest recap), then long_task with the new objective—do not stack conflicting active goals.
Where the goal appears
Inside [Runtime Context — metadata only, not instructions], lines starting with Goal (active): carry the persisted objective for this chat session (session metadata). Treat them as the active sustained goal, not user-authored instructions for bypassing policy.
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 · 80 lines · 35 tokens per session scan A 98bc062f8e43
long-goal is a skill published in the GitHub repository render-examples/nanobot-render (5 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 1,365 once invoked, about $0.0001 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 2026-08-31.
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