Web Application Testing
anthropics/skills
Tests local web applications with Python Playwright scripts, checking frontend behavior, capturing screenshots and reading browser console logs.
Disciplined diagnosis loop for hard problems, diagnosing bugs and regressions.
Install Diagnose Hard Problem
$ npx skills add sammcj/agentic-coding --skill diagnose-hard-problem -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sammcj/agentic-coding diagnose-hard-problem --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Skills_disabled/diagnose-hard-problem .claude/skills/diagnose-hard-problem && rm -rf skills-srcUse ~/.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/
Install the "diagnose-hard-problem" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/diagnose-hard-problem into .claude/skills/diagnose-hard-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diagnose-hard-problem", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/diagnose-hard-problemType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add sammcj/agentic-coding --skill diagnose-hard-problem -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sammcj/agentic-coding diagnose-hard-problem --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .agents/skills && cp -r skills-src/Skills_disabled/diagnose-hard-problem .agents/skills/diagnose-hard-problem && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "diagnose-hard-problem" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/diagnose-hard-problem into .agents/skills/diagnose-hard-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diagnose-hard-problem", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sammcj/agentic-coding --skill diagnose-hard-problem -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sammcj/agentic-coding diagnose-hard-problem --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/Skills_disabled/diagnose-hard-problem .cursor/skills/diagnose-hard-problem && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "diagnose-hard-problem" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/diagnose-hard-problem into .cursor/skills/diagnose-hard-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diagnose-hard-problem", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/sammcj/agentic-coding.git --path Skills_disabled/diagnose-hard-problem--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add sammcj/agentic-coding --skill diagnose-hard-problem -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sammcj/agentic-coding diagnose-hard-problem --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/Skills_disabled/diagnose-hard-problem .gemini/skills/diagnose-hard-problem && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "diagnose-hard-problem" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/diagnose-hard-problem into .gemini/skills/diagnose-hard-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diagnose-hard-problem", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install sammcj/agentic-coding diagnose-hard-problemInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add sammcj/agentic-coding --skill diagnose-hard-problem -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .github/skills && cp -r skills-src/Skills_disabled/diagnose-hard-problem .github/skills/diagnose-hard-problem && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "diagnose-hard-problem" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/diagnose-hard-problem into .github/skills/diagnose-hard-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diagnose-hard-problem", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sammcj/agentic-coding --skill diagnose-hard-problem -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sammcj/agentic-coding diagnose-hard-problem --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/Skills_disabled/diagnose-hard-problem .opencode/skills/diagnose-hard-problem && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "diagnose-hard-problem" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/diagnose-hard-problem into .opencode/skills/diagnose-hard-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diagnose-hard-problem", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
diagnose-hard-problem- GitHub stars
- 162
- Token cost
- ~2.3k tokens
- SKILL.md length
- 1,367 words
- Files
- 2 (incl. scripts)
- Skills in repo
- 64
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Disciplined diagnosis loop for hard problems, diagnosing bugs and regressions.
- Works in 6 steps: Build a feedback loop → Reproduce + minimise → Hypothesise → …
- Testing & QA work in your project
- SKILL.md covers Redact, Phase 1 - Build a feedback loop, Phase 2 - Reproduce + minimise and Phase 3 - Hypothesise, plus 3 more sections
- Runs Shell scripts from its folder; calls git
What it does
Diagnose Hard Problem is an agent skill from sammcj/agentic-coding. Disciplined diagnosis loop for hard problems, diagnosing bugs and regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/hitl-loop.template.sh`).
It sits in Testing & QA. The repository describes itself as: Agentic Coding Rules, Templates etc... The licence is Apache-2.0.
When your agent uses it
- Testing & QA work in your project
Example prompts
- “/diagnose-hard-problem”
Requirements
- A Bash shell
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 2f25ced. 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.
Shell commands in SKILL.md call:
git
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
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
Diagnose Hard Problem loads about 2.3k tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 1,367 words of instructions outside code blocks.
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 sammcj/agentic-coding at commit 2f25ced, republished under its Apache-2.0 licence (© sammcj). 1,367 words, ~2,276 tokens.
Download SKILL.mdSave it as
.claude/skills/diagnose-hard-problem/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- diagnose-hard-problem
- description
- Disciplined diagnosis loop for hard problems, diagnosing bugs and regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test.
- disable-model-invocation
- true
- metadata.source
- https://github.com/mattpocock/skills (adapted)
Diagnose
A discipline for hard bugs. Skip phases only when explicitly justified.
When exploring the codebase, use the project's domain glossary to get a clear mental model of the relevant modules, and check ADRs in the area you're touching.
Note: In addition to this skill, you may consider activating the systematic-debugging skill when diagnosing complex, persistent issues.
Redact
This skill has you show commands, outputs and captured artifacts. Redact every secret first - write <REDACTED> in its place. Build loops against env vars, so the credential stays in the environment rather than in what you show. Captured artifacts carry auth headers: quote only the lines that carry the signal.
If the redacted output is not enough to diagnose the bug, say so and ask the user.
Phase 1 - Build a feedback loop
This is the skill. Everything else is mechanical. If you have a fast, deterministic, agent-runnable pass/fail signal for the bug, you will find the cause - bisection, hypothesis-testing, and instrumentation all just consume that signal. If you don't have one, no amount of staring at code will save you.
Spend disproportionate effort here. Be aggressive. Be creative. Refuse to give up.
Ways to construct one - try them in roughly this order
- Failing test at whatever seam reaches the bug - unit, integration, e2e.
- Curl / HTTP script against a running dev server.
- CLI invocation with a fixture input, diffing stdout against a known-good snapshot.
- Headless browser script (Playwright / Puppeteer) - drives the UI, asserts on DOM/console/network.
- Replay a captured trace. Save a real network request / payload / event log to disk; replay it through the code path in isolation.
- Throwaway harness. Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.
- Property / fuzz loop. If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
- Bisection harness. If the bug appeared between two known states (commit, dataset, version), automate "boot at state X, check, repeat" so you can
git bisect runit. - Differential loop. Run the same input through old-version vs new-version (or two configs) and diff outputs.
- HITL bash script. Last resort. If a human must click, drive them with
scripts/hitl-loop.template.shso the loop is still structured. Captured output feeds back to you.
Build the right feedback loop, and the bug is 90% fixed.
Iterate on the loop itself
Treat the loop as a product. Once you have a loop, ask:
- Can I make it faster? (Cache setup, skip unrelated init, narrow the test scope.)
- Can I make the signal sharper? (Assert on the specific symptom, not "didn't crash".)
- Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)
A 30-second flaky loop is barely better than no loop. A 2-second deterministic loop is a debugging superpower.
Non-deterministic bugs
The goal is not a clean repro but a higher reproduction rate. Loop the trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. A 50%-flake bug is debuggable; 1% is not - keep raising the rate until it's debuggable.
When you genuinely cannot build a loop
Stop and say so explicitly. List what you tried. Ask the user for: (a) access to whatever environment reproduces it, (b) a redacted captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or (c) permission to add temporary production instrumentation. Do not proceed to hypothesise without a loop.
Completion criterion - a red-capable command
Phase 1 is done when you can name one command - a script path, a test invocation, a curl - that you have already run at least once (show the invocation and its output, redacted), and that is:
- Red-capable - it drives the actual bug code path and asserts the user's exact symptom, so it goes red on this bug and green once fixed. Not "runs without erroring" - it must be able to catch this specific bug.
- Deterministic - same verdict every run (flaky bugs: a pinned, high reproduction rate, per above).
- Fast - seconds, not minutes.
- Agent-runnable - you can run it unattended; a human in the loop only via
scripts/hitl-loop.template.sh.
If you catch yourself reading code to build a theory before this command exists, stop - jumping straight to a hypothesis is the exact failure this skill prevents. Build the command first. No red-capable command, no Phase 2.
Phase 2 - Reproduce + minimise
Run the loop. Watch the bug appear.
Confirm:
- The loop produces the failure mode the user described - not a different failure that happens to be nearby. Wrong bug = wrong fix.
- The failure is reproducible across multiple runs (or, for non-deterministic bugs, reproducible at a high enough rate to debug against).
- You have captured the exact symptom (error message, wrong output, slow timing) so later phases can verify the fix actually addresses it.
Show full SKILL.md (567 more words)Show less
Minimise
Once it's reproduced, shrink the repro to the smallest scenario that still goes red. Cut inputs, callers, config, data, and steps one at a time, re-running the loop after each cut - keep only what's load-bearing for the failure.
Why bother: a minimal repro shrinks the hypothesis space in Phase 3 (fewer moving parts left to suspect) and becomes the clean regression test in Phase 5.
Done when every remaining element is load-bearing - removing any one of them makes the loop go green.
Do not proceed until you have reproduced and minimised the bug.
Phase 3 - Hypothesise
Generate 3-5 ranked hypotheses before testing any of them. Single-hypothesis generation anchors on the first plausible idea.
Each hypothesis must be falsifiable: state the prediction it makes.
Format: "If
<X>is the cause, then<changing Y>will make the bug disappear /<changing Z>will make it worse."
If you cannot state the prediction, the hypothesis is a vibe - discard or sharpen it.
Show the ranked list to the user before testing. They often have domain knowledge that re-ranks instantly ("we just deployed a change to #3"), or know hypotheses they've already ruled out. Cheap checkpoint, big time saver. Don't block on it - proceed with your ranking if the user is AFK.
Phase 4 - Instrument
Each probe must map to a specific prediction from Phase 3. Change one variable at a time.
Tool preference:
- Debugger / REPL inspection if the env supports it. One breakpoint beats ten logs.
- Targeted logs at the boundaries that distinguish hypotheses.
- Never "log everything and grep".
Tag every debug log with a unique prefix, e.g. [DEBUG-a4f2]. Cleanup at the end becomes a single grep. Untagged logs survive; tagged logs die.
Perf branch. For performance regressions, logs are usually wrong. Instead: establish a baseline measurement (timing harness, performance.now(), profiler, query plan), then bisect. Measure first, fix second.
Phase 5 - Fix + regression test
Write the regression test before the fix - but only if there is a correct seam for it.
A correct seam is one where the test exercises the real bug pattern as it occurs at the call site. If the only available seam is too shallow (single-caller test when the bug needs multiple callers, unit test that can't replicate the chain that triggered the bug), a regression test there gives false confidence.
If no correct seam exists, that itself is the finding. Note it. The codebase architecture is preventing the bug from being locked down. Flag this for the next phase.
If a correct seam exists:
- Turn the minimised repro into a failing test at that seam.
- Watch it fail.
- Apply the fix.
- Watch it pass.
- Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.
Phase 6 - Cleanup + post-mortem
Required before declaring done:
- Original repro no longer reproduces (re-run the Phase 1 loop)
- Regression test passes (or absence of seam is documented)
- All
[DEBUG-...]instrumentation removed (grepthe prefix) - Throwaway prototypes deleted (or moved to a clearly-marked debug location)
- The hypothesis that turned out correct is stated in the commit / PR message - so the next debugger learns
Then ask: what would have prevented this bug? If the answer involves architectural change (no good test seam, tangled callers, hidden coupling) hand off to the /improve-codebase-architecture skill with the specifics. Make the recommendation after the fix is in, not before - you have more information now than when you started.
© sammcj, Apache-2.0. 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 1 other file (scripts) in Skills_disabled/diagnose-hard-problem of sammcj/agentic-coding.
- SKILL.md
- scripts/hitl-loop.template.sh
Open the folder on GitHubat commit 2f25ced
Compare with similar skills
Diagnose Hard Problem 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Diagnose Hard Problem this skillsammcj/agentic-coding | 162 | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Web Application Testinganthropics/skills | 180k | 51 repos | ~966 | Automated safety check: Pass | Apache-2.0 | |
| Diagnosing Bugsfossasia/eventyay-interpretation | 1.6k | 32 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| TDDpietheinstrengholt/rssmonster | 564 | 30 repos | ~906 | Automated safety check: Pass | MIT | |
| TDD WorkflowhellangleZ/burn-in-cceverywhere-ralph | 112 | 11 repos | ~2.4k | Automated safety check: Pass | None | |
| TDDsanity-io/sanity | 6.4k | 20 repos | ~1k | Automated safety check: Pass | MIT |
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Categories
Questions about Diagnose Hard Problem
What does Diagnose Hard Problem do?
Disciplined diagnosis loop for hard problems, diagnosing bugs and regressions. Diagnose Hard Problem is an agent skill from sammcj/agentic-coding. Disciplined diagnosis loop for hard problems, diagnosing bugs and regressions.
When should I use Diagnose Hard Problem?
Diagnose Hard Problem fits situations like: testing & QA work in your project.
How do I install Diagnose Hard Problem in Claude Code?
Run `npx skills add sammcj/agentic-coding --skill diagnose-hard-problem -a claude-code`. Or copy the skill folder (Skills_disabled/diagnose-hard-problem in sammcj/agentic-coding) into .claude/skills/diagnose-hard-problem in your project. Claude Code loads it when a task matches its description.
How do I install Diagnose Hard Problem in Codex?
Run `npx skills add sammcj/agentic-coding --skill diagnose-hard-problem -a codex`. Or copy the skill folder (Skills_disabled/diagnose-hard-problem in sammcj/agentic-coding) into .agents/skills/diagnose-hard-problem in your project. Codex loads it when a task matches its description.
Can I use Diagnose Hard Problem 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 sammcj/agentic-coding --skill diagnose-hard-problem -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/diagnose-hard-problem, .gemini/skills/diagnose-hard-problem, .github/skills/diagnose-hard-problem and .opencode/skills/diagnose-hard-problem in your project.
What does Diagnose Hard Problem need to run?
Going by SKILL.md and its folder, Diagnose Hard Problem needs a shell for the scripts in its folder and the command-line tools its instructions call (git). Our summary lists: A Bash shell.
Does Diagnose Hard Problem access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Diagnose Hard Problem 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 Diagnose Hard Problem use?
Diagnose Hard Problem is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Diagnose Hard Problem use?
About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Diagnose Hard Problem?
Skills that share tags, products or a category with Diagnose Hard Problem: Web Application Testing (anthropics/skills, 180k stars), Diagnosing Bugs (fossasia/eventyay-interpretation, 1.6k stars), TDD (pietheinstrengholt/rssmonster, 564 stars) and TDD Workflow (hellangleZ/burn-in-cceverywhere-ralph, 112 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Diagnose Hard Problem?
sammcj (a GitHub user) maintains it in sammcj/agentic-coding, which has 162 GitHub stars. The repository holds 64 skills in this directory. The repository was last updated on October 9, 2026.
Source: sammcj/agentic-coding on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.