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 Lykhoyda/rn-dev-agent --skill kano-backloggit clone --depth 1 https://github.com/Lykhoyda/rn-dev-agentWrote 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/lykhoyda/rn-dev-agent/kano-backlog)<a href="https://agentmods.dev/skills/lykhoyda/rn-dev-agent/kano-backlog"><img src="https://agentmods.dev/badge/skills/lykhoyda/rn-dev-agent/kano-backlog/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/lykhoyda/rn-dev-agent/kano-backlog"><img src="https://agentmods.dev/badge/skills/lykhoyda/rn-dev-agent/kano-backlog.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 39 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 | $0.00215 | $0.02378 |
| Opus 5.5 | $0.00086 | $0.00951 |
| Sonnet 5.5 | $0.00043 | $0.00476 |
| Haiku 4.5 | $0.00021 | $0.00238 |
Grade A, and why
kano-backlog 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kano Backlog Refinement (GitHub Issues)
Turn a noisy GitHub Issues backlog into a Kano-prioritized plan and a single, defensible "do this next" pick. The Kano model is the right lens because backlog priority is not one-dimensional: an unmet Must-be (a bug, a broken basic) quietly burns satisfaction every day, while an Attractive delighter costs nothing when absent. Sorting by Kano category before sorting by votes or age prevents the classic failure of shipping shiny features on top of broken basics.
Read references/kano-model.md for the five categories, the functional/
dysfunctional question pair, the full evaluation table, and the heuristic signals.
Load it before categorizing — the body below assumes you know the categories.
When to use
The user wants to know what to work on next, wants the backlog cleaned up /
groomed / refined / triaged, wants issues prioritized, or names the Kano model.
You operate on GitHub Issues via the gh CLI.
What this skill does and does not change
Write-back is automatic (no confirmation prompt) but deliberately scoped to additive, reversible edits:
- ✅ Auto-applied:
kano:*category labels andpriority:now|next|laterlabels. - ⚠️ Recommended, never auto-done: closing issues (e.g. Reverse or stale Indifferent items) and editing/deleting issue bodies. Closing is hard to reverse and signals intent to a human audience, so surface it as a recommended action with rationale and let the user run it. This boundary holds even though the user opted into "fully automatic" — automation covers labeling, not destructive or outward-facing actions.
Every label change is logged in the final report so the run is auditable.
Workflow
1. Resolve the target repo
Confirm which repo's backlog you're refining before touching anything — labeling the wrong repo is annoying to undo.
- If the user named a repo, use
--repo owner/nameon everyghcall. - Otherwise detect the current one:
gh repo view --json nameWithOwner -q .nameWithOwner. - State the resolved repo in your first message ("Refining the backlog for
owner/name…") so a wrong target is caught immediately.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 214 lines · 215 tokens per session scan A 7eb780a80d73
kano-backlog is a skill published in the GitHub repository Lykhoyda/rn-dev-agent (20 stars, last pushed today), licensed MIT. It adds 215 tokens to every session and 2,378 once invoked, about $0.0009 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.
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