Test gap detection, coverage analysis, and automated test generation
5 on GitHubby ruvnetv0.3.4MITupdated 5 days agoupstream
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Plugin ruflo-testgen from marketplace moaa in ahmadhammoud56/MOAA. Commands read from the repo's manifest — 5 days ago.
What it installs
Skills (3)
tdd-repair
Test-Driven Repair: given a failing test, spawn a bounded headless `claude -p` (Read/Edit/Bash only, --max-budget-usd capped) that makes the test pass without modifying it. Use when a failing test should drive the fix to the code under test.
tdd-workflow
TDD London School workflow -- mock-first, outside-in test development
test-gaps
Detect missing test coverage and generate test suggestions. Use when the user asks about coverage gaps, untested code, or what tests to write next; also after adding a feature to find what still needs tests.
Slash commands (1)
/ruflo-testgen:testgen
Generate tests for a file or module using coverage analysis and TDD patterns
Subagents (1)
tester
Specialized testing agent -- writes comprehensive tests using TDD London School
A four-tier memory ladder for Claude Code where promotion is earned by recurrence, not asserted by confidence. L0 raw transcripts are never injected; L1 candidate atoms are gitignored and recalled on lexical relevance per prompt; L2 project context loads each session start; L3 stable persona is always loaded. L1 to L2 needs three distinct sessions spanning two distinct calendar days (a stated claim needs two; a verified claim is the only single-observation path); L2 to L3 needs two projects and thirty days. Two gates refuse rather than guess: a claim altered by the redaction pass never promotes on evidence alone, and a contradicted claim freezes until a human resolves it. Three hooks run the loop unattended and every one fails open. Nothing reaches a committed CLAUDE.md without an explicit human adopt. Ships 5 stdlib scripts, 3 hooks, a cs-memory-curator agent, /cs-memory, a JSON schema, and a 69-check validator. Concept from TencentCloud/TencentDB-Agent-Memory (MIT); no upstream code included.
Audit a project's npm, PyPI, and Go dependencies for supply-chain risk: version-matched advisories for direct dependencies and the full lockfile tree, abandoned upstreams, npm publisher concentration, and install scripts
Ships pull requests to open-source projects you don't maintain — CONTRIBUTING compliance, PR-size/scope checks, minimal-diff implementation, AI-disclosure PR descriptions, and rebase or bot-review replies. Use for 提 PR / 上游 PR / 贡献代码, "open a PR", "fix this upstream", "rebase against main", "respond to the bot review". Not for reviewing PRs as maintainer (use github-review-pr) or general GitHub CRUD (use github-ops).
Assess compliance with the EU General-Purpose AI (GPAI) Code of Practice under the AI Act (Regulation (EU) 2024/1689), Articles 51-56. Covers GPAI model identification (Art. 3(63)), systemic risk model designation (Art. 3(65), Art. 51, 10^25 FLOP training compute threshold), upstream provider obligations under Art. 53 (transparency, technical documentation, copyright policy, training data summary), additional systemic risk obligations under Art. 55 (model evaluation, adversarial testing, AI Office incident notification, cybersecurity), systemic-risk notification and Commission designation under Art. 52, downstream Art. 25 quasi-provider duties, and Art. 53(2) open-source exemption. Built for in-house counsel and AI governance teams reviewing LLM or foundation model deployments, drafting GPAI compliance plans, or assessing systemic risk thresholds. DACH: BaFin, BSI, BNetzA. Pairs with the EU AI Act System Classifier, High-Risk Readiness, FRIA, and Serious Incident Reporting skills.
Research-augmented fork of obra/superpowers. Ships 13 workflow skills (brainstorming, planning, code review, debugging, verification, worktrees, parallel agents) standalone, with no dependency on the upstream superpowers plugin.
13 skills
jamditis/claude-skills-journalism 452
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