Borrowing it
Nothing to install: this file belongs to WaniWani-AI/sdk. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/WaniWani-AI/sdk/main/.agents/skills/linear-oneshot/SKILL.mdgit clone --depth 1 https://github.com/WaniWani-AI/sdkWrote 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/waniwani-ai/sdk/linear-oneshot)<a href="https://agentmods.dev/skills/waniwani-ai/sdk/linear-oneshot"><img src="https://agentmods.dev/badge/skills/waniwani-ai/sdk/linear-oneshot/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/waniwani-ai/sdk/linear-oneshot"><img src="https://agentmods.dev/badge/skills/waniwani-ai/sdk/linear-oneshot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 90 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00089 | $0.02506 |
| Opus 5 | $0.00044 | $0.01253 |
| Sonnet 5 | $0.00018 | $0.00501 |
| Haiku 4.5 | $0.00009 | $0.00251 |
Grade A, and why
linear-oneshot 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 10d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Linear One-Shot
Surfaces one-shottable Linear tickets — small, well-defined, safe for an AI agent to implement in a single autonomous run — and scopes them into an executable brief so anyone can pick one up and run an agent on it.
Two operations:
- Triage — scan a backlog, classify each ticket (candidate / needs-qualify / reject), report. "triage one-shots", "find easy tickets in repo_app".
- Qualify — take one ticket, read its code, produce a scoped brief + a ready / needs-human verdict. "qualify WAN-326".
A third stage, execute, is a documented hand-off (see the end) — this skill does not write code.
Default posture is read-only. Triage and qualify report in chat. Every Linear write (labels, comments) is opt-in and only happens after you explicitly confirm a shown draft — never silently.
Step 0 — Preflight
- Linear MCP — required. You need Linear tools (
list_issues,get_issue,list_issue_labels, and for writessave_issue/save_comment/create_issue_label). If they are not available, STOP and tell the user: "This skill needs the Linear connector, which isn't available in this session. Add it in your Claude connectors / MCP setup (Settings → Connectors → Linear, orclaude mcp addthe Linear server), then re-run." Do not proceed without it. - GitHub CLI (
gh) — optional. Only used by qualify to read a ticket's files when you are not inside a checkout of that ticket's repo. Ifgh auth statusfails, qualify still works from inside the relevant repo; out-of-repo it degrades to "I can't read the code — run me from inside the repo, or authenticategh."
Then route by intent: a backlog/triage request → Operation A; a single ticket id ("qualify WAN-123") → Operation B.
Operation A — Triage
- Pick one repo. The executor that will eventually implement these tickets runs in a single repo, so triage is per-repo. Default to
repo_app(label) unless the user names another (repo_sdk,repo_cli,repo_website). Confirm the repo if ambiguous. - List the backlog.
list_issueswithteam: Product,state: backlog(and/orunstarted), the chosenrepo_*label,includeArchived: false. Page through if needed. Large lists may exceed the tool's output budget — if so, classify in batches. - Read enough to judge. Linear truncates long descriptions in list results. For any ticket that looks like a plausible candidate, fetch the full description with
get_issuebefore deciding — a hidden line ("needs a migration", "new copy") flips the verdict. Do not green-light a ticket whose description you only half-read. - Score every ticket with the Rubric (below). Bucket into ✅ candidate / 🟡 needs-qualify / ❌ reject. Bias hard toward reject — a missed candidate costs nothing; a false candidate burns an agent run and erodes trust.
- Report in chat: a one-line summary with counts, then three grouped tables (ticket id, title, one-line rationale citing the specific flag, confidence). It is a completely valid and honest result for a backlog to return zero candidates — say so plainly; never manufacture candidates to fill the table.
- (Optional) Apply — only on explicit request, e.g. "tag the candidates". Show the exact labels/comments you will write and ask: "Apply these to Linear? (
oneshot:candidate+ a one-line rationale comment on each)". On approval: ensure the label group exists (see Writes), thensave_issueto addoneshot:candidateandsave_commentfor the rationale. Confirm each as it lands. Nothing is written before approval.
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.
- 10d ago First seen · 102 lines · 89 tokens per session scan A dd995ba95d50
linear-oneshot is a skill published in the GitHub repository WaniWani-AI/sdk (17 stars, last pushed 3d ago), licensed MIT. It adds 89 tokens to every session and 2,506 once invoked, about $0.0004 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 2026-08-30.
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