Agent skill

Writing Defect Reports

by kajisho5 in kajisho5/ffmpeg-skill

Establish a finding before you publish it, and correct it after — headlines that overstate what actually reproduces at the layer a user sees, reporting code that no entry point can reach or that is…

MITAuto-check passedDevelopment

Install Writing Defect Reports

skills CLI
$ npx skills add kajisho5/ffmpeg-skill --skill writing-defect-reports -a claude-code

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

GitHub CLI
$ gh skill install kajisho5/ffmpeg-skill writing-defect-reports --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/kajisho5/ffmpeg-skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/defect-reports .claude/skills/writing-defect-reports && 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
writing-defect-reports
GitHub stars
1.9k
Token cost
~3k tokens
SKILL.md length
1,519 words
Files
1
Skills in repo
14
Repo updated
First seen
Licence
MIT

At a glance

Establish a finding before you publish it, and correct it after — headlines that overstate what actually reproduces at the layer a user sees, reporting code that no entry point can reach or that is…

  • Filing an issue
  • SKILL.md covers The anomaly is real; the…, Check the code is reachable…, Search the project's own… and Your earlier note is a claim,…, plus 6 more sections
  • Calls git
  • Writing the body of a PR

What it does

Writing Defect Reports is an agent skill from kajisho5/ffmpeg-skill. Establish a finding before you publish it, and correct it after — headlines that overstate what actually reproduces at the layer a user sees, reporting code that no entry point can reach or that is already dead, filing a caveat the project's own records already answered, re-verifying your prior notes against the tree instead of against the note, choosing the narrowest injection point that reproduces a failure without breaking the run first, capturing probe output a harness swallows, and withdrawing a published…

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. The licence is MIT.

When your agent uses it

  • Filing an issue
  • Writing the body of a PR
  • Code review that asserts a defect
  • Triaging someone elses report

Example prompts

  • “/writing-defect-reports”

What it can do on your machine

Read from SKILL.md and the folder at commit 1f7e7e3. 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

    Shell commands in SKILL.md call:

    • git

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

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    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

Writing Defect Reports loads about 3k tokens when it runs. Until then it costs about 193 tokens; SKILL.md has 1,519 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~193
When it runs · the whole SKILL.md, loaded when a task matches
~3k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from kajisho5/ffmpeg-skill at commit 1f7e7e3, republished under its MIT licence (© kajisho5). 1,519 words, ~3,041 tokens.

Download SKILL.mdSave it as .claude/skills/writing-defect-reports/SKILL.md (or your agent's skills folder).
name
writing-defect-reports
description
Establish a finding before you publish it, and correct it after — headlines that overstate what actually reproduces at the layer a user sees, reporting code that no entry point can reach or that is already dead, filing a caveat the project's own records already answered, re-verifying your prior notes against the tree instead of against the note, choosing the narrowest injection point that reproduces a failure without breaking the run first, capturing probe output a harness swallows, and withdrawing a published claim in the thread where you published it. Use when filing an issue, writing the body of a PR or code review that asserts a defect, triaging someone else's report, or deciding whether a suspicious observation is reportable at all.

Writing Defect Reports

A report is a claim, and it is read by people who will not re-derive it. The cost of an overstated one is not embarrassment — it is a maintainer spending an afternoon on a premise that does not hold, or a "fix" landing for a failure mode that never occurred. The techniques for finding a defect live in verifying-external-behavior (also in this repo's .claude/skills/). This skill is about the step between finding one and publishing it.

The rule underneath all of it: publish the claim you actually measured, at the layer you measured it.

The anomaly is real; the headline may not be

The common failure is not a fabricated bug. It is a genuine internal oddity promoted one layer too far.

You notice that a helper returns a degenerate value for a small sample, trace it into a scoring path, and write the report as "short inputs are wrongly flagged." Then you run real short inputs through the public entry point and the flag never sets — a downstream threshold absorbs the degenerate value, and the only visible effect is noise in a secondary per-feature list. The internal oddity is worth fixing; the headline was false, and a reviewer who tests it will say so.

Before writing the title, run the reproduction at the outermost layer the title names. Then choose one of three honest framings:

what you measuredhow to file it
reproduces end to endfile it as the user-visible symptom
reproduces internally, absorbed downstreamfile the internal defect, state the absorption
does not reproduce at alldo not file; record the probe and move on

The second row is a good report, not a weak one. "This helper returns a value it should not; today a threshold happens to mask it, so there is no user-visible symptom yet" tells a maintainer exactly how to prioritize. Silently keeping the dramatic title because the underlying issue is real is what burns credibility.

Check the code is reachable before calling it broken

Three shapes look like defects and are not — or are, but not the one you were about to describe:

A branch that cannot execute. An early return guarding a lookup that already returns the same value on the missing case; a condition a preceding gate already implies. This is dead code, and "dead code" is the accurate report. Filing it as a correctness bug, or naming a test as though it exercises that branch, misleads everyone downstream — confirm liveness by deleting the line and watching the suite, not by reading it.

A guard whose pattern only matches your fixture. A validity check written against a hand-typed sample can be structurally unable to fire against the real input: a single-line pattern against a generator that wraps its output across indented lines, an exact-string check against a source that varies whitespace. Verify the guard against a captured real input, not the fixture. The consequence matters for the fix, too: replacing synthetic fixtures with real captures deletes that guard's only coverage, so the fix and the fixture change belong in one change, not two.

A file nothing invokes. Repos accumulate scripts that reference paths the repo does not contain and toolchains it does not depend on. Before reporting one as broken, find the caller — the task runner, the workflow, the entry point. If there is none, the report is "this is dead, delete it," which is cheap and uncontroversial, rather than "the build is broken," which is wrong.

Search the project's own records before filing a caveat

The most avoidable report is the one the project already answered. Two measured figures that look inconsistent are usually inconsistent definitions, and the definition is usually written down: a claim ledger row, a docstring, a design note, a closed issue.

Grep for the term before writing "these two numbers do not appear to compute the same quantity." If a record pins the definition, cite it — the caveat you were about to publish reads as an open question the project already closed, and a maintainer has to re-close it.

The same discipline applies to numbers you are quoting. Figures in an older issue may not reproduce, because a dependency the value depends on is unpinned and the installed version has changed. Re-measure before restating, and record it with the command, output, and date — see verifying-external-behavior for why an undated measurement cannot be re-checked, and cross-surface-changes for choosing between two records that cover the same fact.

Quote the protocol next to the figure, not only in whatever artifact produced it: the split, the warmup, the seed, the version. Two documents quoting the same metric under different protocols read as a regression to anyone comparing them, and the reader has no way to tell that they are not comparable.

Your earlier note is a claim, not evidence

Working notes, scratch findings, and a prior comment on the same issue are secondary sources — including your own. They were true about a tree that has since moved, or they were wrong when written.

When a note and the code disagree, the code settles it. Re-derive from the integration branch directly rather than from a checkout that may be behind:

bash
git show origin/main:path/to/file.py | grep -n 'the_symbol'

If two notes contradict each other, do not average them and do not pick the more recent — re-check the tree and then correct the wrong note in place, saying that it was wrong. A knowledge base that records both readings without resolving them is worse than one that records neither, because the next reader will pick one at random.

Show full SKILL.md (597 more words)Show less

Pick the narrowest injection point that reproduces the failure

To exercise a failure path by hand you need to break something. Break the smallest possible thing, or the run dies before reaching the path you care about.

The classic miss is a blanket hook or a global monkeypatch:

bash
# Too broad — rejects EVERY commit, including the unrelated bookkeeping commit
# the process makes first. The run fails earlier than the path under test, and
# you have reproduced a different bug.
echo 'exit 1' > .git/hooks/pre-commit

# Narrow — fails exactly the operation whose failure you want to observe.
cat > .git/hooks/commit-msg <<'EOF'
grep -q 'sync from source-b' "$1" && exit 1
exit 0
EOF

The same rule holds elsewhere: fail one HTTP host rather than the network; make one file unreadable rather than the directory; raise from one call rather than patching the module. After the run, confirm it failed where you intended — otherwise you have measured your instrumentation.

Check where your probe's output actually goes. Test harnesses commonly replace the global logger or console object at setup, and verbosity flags do not undo that. If a probe prints nothing, append to a file outside the harness's reach and read it afterwards, rather than concluding the code path did not run.

Report the checks that model a real regression

When you back a report with mutation evidence, include only mutations that (a) plausibly model how an implementation would actually regress and (b) demonstrably turn a named test red. A mutation that nothing catches is worth reporting as a coverage gap; a mutation nobody would ever write is noise that makes the rest of the report look padded.

State which test each mutation trips, and which assertion inside it — a guard often turns out to be load-bearing at a different layer than the report assumed, and the assertion name is what reveals it.

A red gate is not an aside

Whether a red check is pre-existing is answered by running it on the base commit — see reproducing-ci-locally (also in this repo's .claude/skills/). What belongs here is where that answer goes in the write-up. A red check is never a parenthetical under a "done" claim: give it its own statement naming what is red, what makes it red, and whether you fixed it. And confirm green after the run finishes rather than writing "should be green" — a prediction stated as an outcome is the same defect as an overstated headline, one artifact over.

Withdraw published claims where you published them

A wrong claim that has been read does not become unwritten when you stop repeating it. If a caveat, a number, or a framing you published turns out to be wrong or already-answered:

  • Say so in the same thread, naming what was wrong and what is true instead.
  • Do not quietly delete it and re-file a corrected version elsewhere; readers who saw the first one are never routed to the second.
  • Keep it to the correction. A retraction is one or two sentences — what you claimed, what is actually the case, what changes as a result.

The same applies to a claim you inherited. If you repeat someone else's figure and it fails to reproduce, correcting it is part of your report, not a separate errand.

Checklist

Before publishing a defect report:
- [ ] Reproduction run at the outermost layer the title names; title matches
      what reproduced there, not what you found internally
- [ ] Absorbed-downstream findings filed as internal defects, with the absorption
      stated — not promoted to a user-visible symptom
- [ ] The code is reachable: an entry point calls it, and the branch is live
      (checked by deletion, not by reading)
- [ ] Guards verified against a captured real input, not the fixture that was
      written alongside them
- [ ] Project records (ledger, docstrings, design notes, closed issues) searched
      for a definition that already resolves the discrepancy
- [ ] Every quoted number re-measured, with version, command, and date; protocol
      stated next to the figure
- [ ] Prior notes re-verified against the integration branch, and any wrong note
      corrected in place
- [ ] Failure injected at the narrowest point; run confirmed to have failed where
      intended
- [ ] Mutation evidence limited to plausible regressions, each attributed to a
      named test and assertion
- [ ] No red check described as pre-existing under a "done" claim; green
      confirmed after the run finished, not predicted
- [ ] Any earlier wrong claim withdrawn in the thread where it was published

Note for this repository (ffmpeg-skill)

This is the same discipline behind this repo's 0.9.1/0.10.0 "honesty fix" pattern: cut.py's mode/keyframe_snapped/duration_delta_seconds fields, check.py's reason field, and render.py's check-stage exit code were all added because a prior report ("the cut is lossless," "the check passed," "the render succeeded") was true at one layer and silently false at the layer a caller actually reads results from — exactly the "genuine internal oddity promoted one layer too far" pattern this skill describes, just discovered after shipping rather than before. When investigating a new claim about this codebase, reproduce it with real media via tests/test_all.py's fixtures before reporting it, the same way test_cut_copy_keyframe_snap_reports_a_real_nonzero_delta measured an actual 1.24s divergence rather than assuming one.

Source: wdm0006/python-skills (MIT).

© kajisho5, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/defect-reports of kajisho5/ffmpeg-skill.

Open the folder on GitHubat commit 1f7e7e3

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Categories

Questions about Writing Defect Reports

What does Writing Defect Reports do?

Establish a finding before you publish it, and correct it after — headlines that overstate what actually reproduces at the layer a user sees, reporting code that no entry point can reach or that is…. Writing Defect Reports is an agent skill from kajisho5/ffmpeg-skill.

When should I use Writing Defect Reports?

Writing Defect Reports fits situations like: filing an issue; writing the body of a PR; code review that asserts a defect; triaging someone elses report.

How do I install Writing Defect Reports in Claude Code?

Run `npx skills add kajisho5/ffmpeg-skill --skill writing-defect-reports -a claude-code`. Or copy the skill folder (.claude/skills/defect-reports in kajisho5/ffmpeg-skill) into .claude/skills/writing-defect-reports in your project. Claude Code loads it when a task matches its description.

How do I install Writing Defect Reports in Codex?

Run `npx skills add kajisho5/ffmpeg-skill --skill writing-defect-reports -a codex`. Or copy the skill folder (.claude/skills/defect-reports in kajisho5/ffmpeg-skill) into .agents/skills/writing-defect-reports in your project. Codex loads it when a task matches its description.

Can I use Writing Defect Reports 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 kajisho5/ffmpeg-skill --skill writing-defect-reports -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/writing-defect-reports, .gemini/skills/writing-defect-reports, .github/skills/writing-defect-reports and .opencode/skills/writing-defect-reports in your project.

What does Writing Defect Reports need to run?

Going by SKILL.md and its folder, Writing Defect Reports needs the command-line tools its instructions call (git).

Does Writing Defect Reports access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Writing Defect Reports 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. Review the folder before installing.

What licence does Writing Defect Reports use?

Writing Defect Reports 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 Writing Defect Reports use?

About 3k tokens (SKILL.md is roughly 12k 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 Writing Defect Reports?

Skills that share tags, products or a category with Writing Defect Reports: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 297k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Writing Defect Reports?

kajisho5 (a GitHub user) maintains it in kajisho5/ffmpeg-skill, which has 1,909 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 10, 2026.

Source: kajisho5/ffmpeg-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.