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 agentmods add commands/elnora-ai/elnora-linear/linear-searchgit clone --depth 1 https://github.com/Elnora-AI/elnora-linearWrote 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/commands/elnora-ai/elnora-linear/linear-search)<a href="https://agentmods.dev/commands/elnora-ai/elnora-linear/linear-search"><img src="https://agentmods.dev/badge/commands/elnora-ai/elnora-linear/linear-search.svg" alt="Measured on agentmods" 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 | $0.00018 | $0.00264 |
| Opus 5 | $0.00009 | $0.00132 |
| Sonnet 5 | $0.00004 | $0.00053 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
linear-search 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 4d 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
Linear Search
Search Linear issues for: {{query}}
Run
elnora-linear search --query "{{query}}" --limit 25 --output json
For more focused queries, add flags:
--team ENG— restrict to a team--assignee me— only your issues (or--assignee "Alice Smith")--state "In Progress"— by workflow state--priority urgent— by priority (urgent|high|medium|low|none, or0-4)--limit <n>— default 25--output text— for human-readable; default here is JSON for parsing
Present
Render the JSON as a table: identifier, state, assignee, title. Highlight any issue whose state matches what the user is looking for.
Don't
- Don't paginate; the
--limitflag controls volume - Don't apply mutations — that's
/linear-bulk,/linear-cleanup, or thelinear-issue-updateragent
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.
- 4d ago First seen · 34 lines · 18 tokens per session scan A 87eaaa116eea
linear-search is a command published in the GitHub repository Elnora-AI/elnora-linear (7 stars, last pushed 3d ago), licensed Apache-2.0. It adds 18 tokens to every session and 264 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
beads-onboard
Run the interactive beads-onboard interview in the current project to generate beads conventions (AGENTS.md translation matrix, labels.md, reference.md, starter formula, pre-seeded bd remember entries).
octo-plan
Intelligent plan builder - creates strategic execution plans (doesn't execute). Use /octo:embrace to execute plans.
octo-schedule
Manage scheduled workflow jobs for the Claude Octopus scheduler.
octo-scheduler
Manage the Claude Octopus scheduled workflow runner daemon.
discover
Run a full product discovery cycle — from outcome definition through opportunity mapping, prioritisation, and experiment design. Use when the team isn't sure what to build next, or before writing a PRD for a complex feature space.
whats-next
Show current project status and suggest next steps.