Dev & Engineering

RenderCV Issue Solver

Picks up a GitHub issue on the rendercv repo, learns the codebase, ships a test-first fix, and opens a pull request.

51/ 100
Use with care

Useful, but reliability, evidence or controls still have material gaps.

See how it was scored ↓
Works as-is in
Codex · Claude Code
Stars
★ 18k
Last updated
7mo ago
License
MIT
github-issuespull-requestspytesttest-first
+4git-workflowgithub-clipythonbug-fixing

What does this skill do, and when should you use it?

This skill turns 'solve an open-source issue' into a repeatable eight-step workflow: select an issue, understand the code, branch, reproduce the bug, implement the fix, verify, commit and push, then open a PR. It mandates a failing test before any bug fix and requires every verification command to pass, with coverage held at 100%. It is purpose-built for the rendercv/rendercv repository and relies on two sibling skill files in the same repo for development and testing context.

Reads GitHub issues via gh issue list and gh issue view, auto-selecting high-priority ones (skipping wontfix/question labels, preferring bugs and smaller scopes); creates a claude/issue-<number> branch from origin/main; for bugs, writes a failing test first and confirms it goes red with uv run --frozen --all-extras pytest; implements the fix following the rendercv-development-context and rendercv-testing-context skills in the same repo; runs just format, just check, just test, and just test-coverage as full verification; stages only changed files, commits, and pushes; finally opens a PR to main with gh pr create and reports the root cause, approach, tests, coverage status, and PR link.

Good fit
  • Maintainers who want an AI agent to work through rendercv bug reports to repo standards, producing review-ready PRs
  • Contributors with a specific issue number who want the full test-first workflow plus a well-structured PR description
  • Teams enforcing a rule that every fix ships with a failing test and 100% coverage, using this skill to apply the discipline
  • Newcomers to the rendercv project using the workflow to learn the codebase structure and verification commands

How do you install this skill?

Before you use it
  • The skill autonomously picks an issue, pushes a branch, and opens a PR to rendercv/rendercv without user confirmation — an external write operation; verify credential scope and prepare rollback (closing the PR) before use.
  • The skill depends on two context skills (development/testing context) not present in the evidence; their existence and quality are unverified.
  • The workflow relies entirely on github.com and gh CLI reachability, which may be unavailable from mainland-China networks.
  • This is a static source review; no commands were executed, so all reliability conclusions are inference rather than measurement.
  • The publisher is not verified by the FollowSkills curated registry and is treated as unknown.
Before you start
Your agent needs
  • Shell / CLI
  • Network access
  • Local filesystem
Install first
  • GitHub CLI (gh)
  • git
  • uv
  • just task runner
  • pytest

The skill is part of the skill collection in the rendercv/rendercv repo (the repo README gives npx skills add rendercv/rendercv-skill as the collection install command). The skill file lives at .claude/skills/solve-rendercv-issue/SKILL.md; the source does not document standalone installation steps for this skill alone. The rendercv-development-context and rendercv-testing-context skills in the same repo are prerequisites.

Generic route: install into Claude Code manually (macOS / Linux)
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/rendercv/rendercv.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/.claude/skills/solve-rendercv-issue" ~/.claude/skills/
rm -rf "$tmp"

Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.

How do you use this skill?

Try saying

Once installed, send your agent any of these to trigger it:

  • Use the solve-rendercv-issue skill to resolve issue #123 in the rendercv repo.

In an environment with gh, git, uv, and just, prompt your agent with an issue number or URL, e.g.: 'Use the solve-rendercv-issue skill to resolve issue #123 in the rendercv repo.' With no number given, the skill lists open issues and picks the most impactful one automatically. When done, it reports the root cause, the fix approach, test changes, coverage status, and the PR link.

What are this skill's strengths and limitations?

Pros
  • Complete, rigorous workflow: explicit commands for every step from issue selection to PR creation
  • Enforces test-first development: no bug fix proceeds without a red failing test
  • High quality bar: format, check, test, and coverage must all pass, with coverage held at 100%
  • Well-structured PR descriptions with summary, change list, and test plan
Limitations
  • Works only against rendercv/rendercv; not directly reusable for other projects
  • Depends on two sibling skill files in the same repo for context, so it cannot run when copied in isolation
  • Requires gh, git, uv, and just preinstalled; the skill does not explain how to set these up
  • Pushes branches and opens PRs autonomously, so you must trust its automatic issue selection

How does this skill compare with similar options?

Side by side with related skills; every score comes from the same FSRS standard.

Skill FS score Stars Last updated License
RenderCV Issue Solver this page 51 · Use with care ★ 18k 7mo ago MIT
Poll GitHub Work 52 · Use with care ★ 30k 11d ago MIT
Codex Issue Digest ✓ OpenAI · Official 57 · Use with care ★ 129k 11d ago Apache-2.0
Commit, Push & PR Skill 54 · Use with care ★ 26k 11d ago MIT
RenderCV Typst Package Publisher 51 · Use with care ★ 18k 7mo ago MIT

The same repo bundles five sibling skills, including rendercv-development-context and rendercv-testing-context, which supply development and testing context; this skill is the end-to-end execution flow that references rather than replaces them. The source names no external alternatives.

How did FollowSkills review this skill?

FollowSkills review · FSRS-2.0
Use with care
51/ 100 5-point scale 2.6 / 5
1Trust13 / 25 · 2.6/5

The skill's instructions are non-malicious; permissions are proportionate to the task (gh issue reads, git branch, push, PR creation), with no credential theft or covert exfiltration; MIT license and traceable origin rendercv/rendercv. Deducted for: it autonomously picks an issue, creates a branch, pushes to origin, and opens a PR without any user-confirmation step — automated write operations with external effects lacking confirmation, isolation, or rollback guidance, and without stating assumptions about gh/git credentials and push targets.

2Reliability10 / 20 · 2.5/5

Instructions are self-consistent with clear steps (red test first, all just format/check/test gates before proceeding) and basic failure-path guidance. Deducted for: the referenced development/testing context skills are not present in evidence so key paths cannot be statically confirmed; no failure feedback is defined for missing gh auth, no suitable issue, or broken environment.

3Adaptability8 / 15 · 2.7/5

The target scenario is clear (resolve a GitHub issue and open a PR for the rendercv repo), with explicit trigger logic. Deducted for: narrow applicability limited to rendercv contributors with no declared non-fit boundary; hidden dependencies on gh CLI, uv, just, and reachability of github.com — a risk for mainland-China users that is not disclosed.

4Convention10 / 15 · 3.3/5

Well-structured, progressive disclosure, complete command examples, consistent naming (claude/issue-<n>); the repo has MIT license, version 2.8, changelog link, and a named maintainer. Deducted for: the skill itself has no version/update record; its two dependency skills are unverified; README notes skills are auto-generated, implying doc-drift maintenance risk.

5Effectiveness6 / 15 · 2.0/5

The workflow design is sound (failing test for bugs, full verification gates, structured PR body) and should yield a directly reviewable PR, giving clear marginal value. Deducted for: static review cannot verify output quality; success depends entirely on the absent context skills and the repo's test suite, so completeness and comparative benefit remain unevidenced.

6Verifiability4 / 10 · 2.0/5

The repo offers auditable material (CI badge, coverage badge, pytest/coverage config, promptfoo eval files), but these target the main rendercv code or a different skill, not this skill's key paths; no execution evidence exists in a static read, so only limited credit is given with no upward adjustment.

1 2 3 4 5 6

Open a dimension to read why it scored that way

Reviewed Sep 09, 2026 Reviewed revision 1d4b87bc427e Review evidence[1][2][3][4][5][6][7][8][9]

Evidence confidence:Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.

See the full review method →

FAQ

What permissions and environment does it need?
GitHub access to rendercv/rendercv (gh CLI authenticated with permission to push branches), plus git, uv, and the just task runner, and a local dev environment that can pass the full test suite.
What happens if I don't specify an issue?
The skill lists the 10 most recent open issues via gh, skips those labeled wontfix or question, prefers bugs and smaller-scoped tasks, and picks the most impactful one automatically.
What if verification fails?
The skill requires every one of format, check, test, and test-coverage to pass before committing; for bug fixes, it also refuses to start until the failing test is confirmed red.
Can it handle feature requests or only bugs?
Both, though automatic selection prefers bugs; every bug fix must first ship a reproducible failing test.

More skills from this repository

All from rendercv/rendercv

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