Agent skill

Diagnosing Bugs

by stevesolun in stevesolun/ctx

Diagnose hard bugs and performance regressions with reproducible evidence, focused hypotheses, and proportional instrumentation.

MITAuto-check passed

Install Diagnosing Bugs

skills CLI
$ npx skills add stevesolun/ctx --skill diagnosing-bugs -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install stevesolun/ctx diagnosing-bugs --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/stevesolun/ctx.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/diagnosing-bugs .claude/skills/diagnosing-bugs && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
diagnosing-bugs
GitHub stars
588
Token cost
~596 tokens
SKILL.md length
293 words
Files
4 (incl. scripts, references)
Skills in repo
26
Repo updated
First seen
Licence
MIT

At a glance

Diagnose hard bugs and performance regressions with reproducible evidence, focused hypotheses, and proportional instrumentation.

  • Something is broken
  • SKILL.md covers Establish a useful signal, Narrow and explain and Fix and verify
  • Runs Shell scripts from its folder
  • Intermittently wrong

What it does

Diagnosing Bugs is an agent skill from stevesolun/ctx. Diagnose hard bugs and performance regressions with reproducible evidence, focused hypotheses, and proportional instrumentation. Use when something is broken, failing, throwing, intermittently wrong, or unexpectedly slow.

Its SKILL.md is about 600 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/debugging-playbook.md` and `scripts/hitl-loop.template.sh`).

The repository describes itself as: CTX Fit finds the cheapest AI coding setup that reliably works on your repository, then applies the winner as a reviewable change. The licence is MIT.

When your agent uses it

  • Something is broken
  • Intermittently wrong
  • Unexpectedly slow

Example prompts

  • “/diagnosing-bugs”

Requirements

  • A Bash shell

What it can do on your machine

Read from SKILL.md and the folder at commit 11b582a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Shell), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Diagnosing Bugs loads about 596 tokens when it runs, and up to ~1.2k if it reads all its reference files. Until then it costs about 59 tokens; SKILL.md has 293 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~59
When it runs · the whole SKILL.md, loaded when a task matches
~596
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from stevesolun/ctx at commit 11b582a, republished under its MIT licence (© stevesolun). 293 words, ~596 tokens.

Download SKILL.mdSave it as .claude/skills/diagnosing-bugs/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
diagnosing-bugs
description
Diagnose hard bugs and performance regressions with reproducible evidence, focused hypotheses, and proportional instrumentation. Use when something is broken, failing, throwing, intermittently wrong, or unexpectedly slow.

Diagnosing Bugs

Start from evidence and adapt the depth of the investigation to the problem. Read relevant repository context and architectural decisions when they exist.

Establish a useful signal

Reproduce the reported symptom with the cheapest signal that distinguishes broken from fixed. A focused test or script is ideal when practical; logs, traces, snapshots, comparisons, or measured timings may be better for other failures. Tighten the loop by improving speed, specificity, and determinism.

If an exact reproduction is unavailable, continue with the strongest evidence available and state the limitation. Request an artifact, access, or temporary instrumentation only when it would materially improve the diagnosis.

For feedback-loop options and debugging tactics, load the debugging playbook as needed. For a rare manual reproduction, adapt scripts/hitl-loop.template.sh.

Narrow and explain

Minimize the reproducing scenario when doing so will shrink the search space. Form a small ranked set of falsifiable hypotheses from the evidence, then choose probes that best distinguish them. Share hypotheses with the user when their domain knowledge could redirect the investigation; do not make routine progress depend on a checkpoint.

Use targeted instrumentation at boundaries that separate plausible causes. Change as little as practical per probe. For performance regressions, establish a baseline and use profiling, query plans, or bisection before optimizing.

Fix and verify

Add a regression test when a stable seam can represent the real failure. If it cannot, explain the coverage gap rather than adding a misleading test. Apply the smallest justified fix, then rerun both the focused signal and relevant nearby checks.

Remove temporary instrumentation and artifacts unless the user wants to retain them. Report the observed cause, the evidence that supports it, what was verified, and any remaining uncertainty. Recommend architectural follow-up only when the diagnosis exposes a concrete recurring weakness.

© stevesolun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in .agents/skills/diagnosing-bugs of stevesolun/ctx.

  • SKILL.md
  • agents/openai.yaml
  • references/debugging-playbook.md
  • scripts/hitl-loop.template.sh

Open the folder on GitHubat commit 11b582a

Compare with similar skills

Diagnosing Bugs next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Diagnosing Bugs compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Diagnosing Bugs this skillstevesolun/ctx588—~596Automated safety check: PassMIT
Reproduce Bugudecode/plate17k—~2kAutomated safety check: NotesCustom licence
Bug ReproducePrismer-AI/PrismerCloud1.6k—~1.7kAutomated safety check: NotesMIT
Diagnosing Bugsgetsentry/sentry-react-native1.8k—~1.4kAutomated safety check: PassMIT
Diagnosing Bugsvinvcn/mattpocock-skills-zh-CN4.7k—~1.4kAutomated safety check: PassMIT
Diagnosing Bugsfossasia/eventyay-interpretation1.6k32 repos~2.1kAutomated safety check: PassApache-2.0

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Questions about Diagnosing Bugs

What does Diagnosing Bugs do?

Diagnose hard bugs and performance regressions with reproducible evidence, focused hypotheses, and proportional instrumentation. Diagnosing Bugs is an agent skill from stevesolun/ctx. Diagnose hard bugs and performance regressions with reproducible evidence, focused hypotheses, and proportional instrumentation.

When should I use Diagnosing Bugs?

Diagnosing Bugs fits situations like: something is broken; intermittently wrong; unexpectedly slow.

How do I install Diagnosing Bugs in Claude Code?

Run `npx skills add stevesolun/ctx --skill diagnosing-bugs -a claude-code`. Or copy the skill folder (.agents/skills/diagnosing-bugs in stevesolun/ctx) into .claude/skills/diagnosing-bugs in your project. Claude Code loads it when a task matches its description.

How do I install Diagnosing Bugs in Codex?

Run `npx skills add stevesolun/ctx --skill diagnosing-bugs -a codex`. Or copy the skill folder (.agents/skills/diagnosing-bugs in stevesolun/ctx) into .agents/skills/diagnosing-bugs in your project. Codex loads it when a task matches its description.

Can I use Diagnosing Bugs in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add stevesolun/ctx --skill diagnosing-bugs -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/diagnosing-bugs, .gemini/skills/diagnosing-bugs, .github/skills/diagnosing-bugs and .opencode/skills/diagnosing-bugs in your project.

What does Diagnosing Bugs need to run?

Going by SKILL.md and its folder, Diagnosing Bugs needs a shell for the scripts in its folder. Our summary lists: A Bash shell.

Does Diagnosing Bugs access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Diagnosing Bugs safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Diagnosing Bugs use?

Diagnosing Bugs is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Diagnosing Bugs use?

About 596 tokens (SKILL.md is roughly 2.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 586 tokens, read only when the agent opens those files.

What are the alternatives to Diagnosing Bugs?

Skills that share tags, products or a category with Diagnosing Bugs: Reproduce Bug (udecode/plate, 17k stars), Bug Reproduce (Prismer-AI/PrismerCloud, 1.6k stars), Diagnosing Bugs (getsentry/sentry-react-native, 1.8k stars) and Diagnosing Bugs (vinvcn/mattpocock-skills-zh-CN, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Diagnosing Bugs?

stevesolun (a GitHub user) maintains it in stevesolun/ctx, which has 588 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on October 4, 2026.

Source: stevesolun/ctx on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.