Kandev is a self-hostable workspace for organizing software tasks and running multiple AI coding agents in parallel, with Kanban views, change review, and pull request support. Developers use it to coordinate agents from different providers and inspect their work in one place. The catalogue entries extend its agent workflows and development environment.
Borrowing it
Nothing to install: this file belongs to kdlbs/kandev. 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/kdlbs/kandev/main/.agents/skills/pr/SKILL.mdgit clone --depth 1 https://github.com/kdlbs/kandevWrote 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/kdlbs/kandev/pr)<a href="https://agentmods.dev/skills/kdlbs/kandev/pr"><img src="https://agentmods.dev/badge/skills/kdlbs/kandev/pr/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/kdlbs/kandev/pr"><img src="https://agentmods.dev/badge/skills/kdlbs/kandev/pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Prompt Injection · line 99 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Data Exfiltration · line 351 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00029 | $0.06276 |
| Opus 5.5 | $0.00012 | $0.02510 |
| Sonnet 5.5 | $0.00006 | $0.01255 |
| Haiku 4.5 | $0.00003 | $0.00628 |
Grade A, and why
pr scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 14d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
> **GitLab tool selection:** The GitLab flow prefers `glab` CLI when available; otherwise it shells `curl` against the REST v4 API using `$GITLAB_TOKEN` (which the agent runtime injects from the user's secrets store). The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 14d ago Changed · +44 lines e51fea5562a0
- 21d ago Changed · +15 lines ee517c4fe9e2
- 1mo ago Changed · +19 lines cdb7b154987f
- 1mo ago Changed · +18 lines 2edb349a4568
- 1mo ago Changed · +9 lines 774073cf1e71
- 1mo ago First seen · 350 lines · 29 tokens per session scan A a2e791128572
pr is a skill published in the GitHub repository kdlbs/kandev (1,023 stars, last pushed today), licensed AGPL-3.0. It adds 29 tokens to every session and 6,276 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
github-pr-workflow
Prepare a GitHub pull request from a feature branch — branch hygiene, commit shape, title/body, verification notes, screenshots for UI work, and replies to review comments.
review-open-pull-requests
List and analyze all open pull requests across GitHub repositories. Shows review status, CI/CD check results, age, and reviewers. Use when triaging PRs, checking team velocity, or identifying stale reviews that need attention.
merge-queue
Process the Refinery merge queue - collect agent work, detect and resolve conflicts, merge in dependency order, and verify integration.
requesting-code-review
Use when completing tasks, implementing major features, or before merging to verify work meets requirements.
github
GitHub operations via gh CLI: issues, PRs, CI runs, code review, API queries. Use when: (1) checking PR status or CI, (2) creating/commenting on issues, (3) listing/filtering PRs or issues, (4) viewing run logs. NOT for: complex web UI interactions requiring manual browser flows (use browser tooling when available)…
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.