sdk: Skill for Claude Code

.agents/skills/linear-oneshot/SKILL.md

linear-oneshot is a skill for Claude Code, Codex from WaniWani-AI/sdk. It costs 89 tokens per session (2,506 once invoked), scanned A, original, MIT.

A Linear backlog tool that finds small, clearly defined tickets suitable for one autonomous coding run. It also reads a selected ticket and produces an implementation brief and readiness decision.

In plain words
What is it for?
It scans tickets, classifies candidates, qualifies individual issues, and can propose labels or comments only after explicit approval; it does not write code.
Why use it?
It helps separate safe, well-scoped tasks from tickets that need more clarification or human work before an agent starts coding.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is WaniWani-AI/sdk's own configuration. It tells Claude Code and Codex how to work on sdk itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything sdk configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/WaniWani-AI/sdk/main/.agents/skills/linear-oneshot/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/WaniWani-AI/sdk

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for linear-oneshot

README.md
[![agentmods](https://agentmods.dev/badge/skills/waniwani-ai/sdk/linear-oneshot/github.svg)](https://agentmods.dev/skills/waniwani-ai/sdk/linear-oneshot)
Your own site
<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.

agentmods 80×15 button for linear-oneshot

Your own site · 80×15
<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>
Per session 89 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,506 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 10d ago against content hash dd995ba95d50, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

.agents/skills/linear-oneshot/SKILL.md · 102 lines

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 writes save_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, or claude mcp add the 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. If gh auth status fails, 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 authenticate gh."

Then route by intent: a backlog/triage request → Operation A; a single ticket id ("qualify WAN-123") → Operation B.

Operation A — Triage

  1. 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.
  2. List the backlog. list_issues with team: Product, state: backlog (and/or unstarted), the chosen repo_* label, includeArchived: false. Page through if needed. Large lists may exceed the tool's output budget — if so, classify in batches.
  3. 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_issue before deciding — a hidden line ("needs a migration", "new copy") flips the verdict. Do not green-light a ticket whose description you only half-read.
  4. 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.
  5. 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.
  6. (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), then save_issue to add oneshot:candidate and save_comment for the rationale. Confirm each as it lands. Nothing is written before approval.

Read the full file on GitHub · 102 lines

Changes

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.

  1. 10d ago First seen · 102 lines · 89 tokens per session scan A dd995ba95d50

Subscribe to this mod's changes

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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