Perplexity Web Search
davila7/claude-code-templates
Runs web-grounded searches through Perplexity's Sonar models over OpenRouter for current events, recent literature and cited facts beyond the model's training cutoff.
Verify the accuracy of claims and statements by extracting individual assertions, identifying authoritative sources, cross-referencing evidence, and assigning confidence-scored verdicts.
Install Fact Checking
$ npx skills add seb1n/awesome-ai-agent-skills --skill fact-checking -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills fact-checking --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/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/research-and-knowledge/fact-checking .claude/skills/fact-checking && 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 "fact-checking" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/research-and-knowledge/fact-checking into .claude/skills/fact-checking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fact-checking", 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/seb1n/awesome-ai-agent-skills/tree/main/research-and-knowledge/fact-checkingType 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 seb1n/awesome-ai-agent-skills --skill fact-checking -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills fact-checking --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/research-and-knowledge/fact-checking .agents/skills/fact-checking && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "fact-checking" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/research-and-knowledge/fact-checking into .agents/skills/fact-checking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fact-checking", 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 seb1n/awesome-ai-agent-skills --skill fact-checking -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills fact-checking --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/research-and-knowledge/fact-checking .cursor/skills/fact-checking && 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 "fact-checking" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/research-and-knowledge/fact-checking into .cursor/skills/fact-checking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fact-checking", 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/seb1n/awesome-ai-agent-skills.git --path research-and-knowledge/fact-checking--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 seb1n/awesome-ai-agent-skills --skill fact-checking -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills fact-checking --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/research-and-knowledge/fact-checking .gemini/skills/fact-checking && 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 "fact-checking" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/research-and-knowledge/fact-checking into .gemini/skills/fact-checking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fact-checking", 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 seb1n/awesome-ai-agent-skills fact-checkingInstalls 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 seb1n/awesome-ai-agent-skills --skill fact-checking -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/research-and-knowledge/fact-checking .github/skills/fact-checking && 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 "fact-checking" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/research-and-knowledge/fact-checking into .github/skills/fact-checking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fact-checking", 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 seb1n/awesome-ai-agent-skills --skill fact-checking -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills fact-checking --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/research-and-knowledge/fact-checking .opencode/skills/fact-checking && 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 "fact-checking" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/research-and-knowledge/fact-checking into .opencode/skills/fact-checking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fact-checking", 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
fact-checking- GitHub stars
- 206
- Token cost
- ~2.4k tokens
- SKILL.md length
- 1,245 words
- Files
- 1
- Skills in repo
- 92
- Repo updated
- First seen
- Licence
- MIT
At a glance
Verify the accuracy of claims and statements by extracting individual assertions, identifying authoritative sources, cross-referencing evidence, and assigning confidence-scored verdicts.
- Works in 6 steps: Extract Claims: Parse the input text and… → Classify Claim Types: Categorize each… → Identify Authoritative Sources: For each… → …
- The user requests fact checking
- SKILL.md covers Workflow, Usage, Examples and Best Practices, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Fact Checking is an agent skill from seb1n/awesome-ai-agent-skills. Verify the accuracy of claims and statements by extracting individual assertions, identifying authoritative sources, cross-referencing evidence, and assigning confidence-scored verdicts. Use when the user requests fact checking or provides relevant inputs for this workflow.
Its SKILL.md is about 2.4k 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 Research & Science, covering Fact-checking and source verification. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.
When your agent uses it
- The user requests fact checking
- Provides relevant inputs for this workflow
Example prompts
- “/fact-checking”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Extract Claims: Parse the input text and isolate individual, verifiable assertions. Each claim should be a single, self-contained…
- Classify Claim Types: Categorize each claim by type — statistical (involves numbers or data), historical (references past events)…
- Identify Authoritative Sources: For each claim, determine the most appropriate verification sources. Use primary sources whenever…
- Cross-Reference and Evaluate Evidence: Check each claim against at least two independent sources. Note whether sources corroborate…
- Assign Verdicts and Confidence Scores: For each claim, assign a verdict from the scale: True, Mostly True, Half True, Mostly False, False…
- Compile the Fact-Check Report: Present findings in a structured format: list each claim, its verdict, confidence score, supporting…
What it can do on your machine
Read from SKILL.md and the folder at commit 75865a5. 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
No scripts in the folder and no shell commands in SKILL.md.
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):
nnethercote.github.iobenchmarksgame-team.pages.debian.netlwn.netgit.kernel.orgsurvey.stackoverflow.co
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
Fact Checking loads about 2.4k tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 1,245 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 1,245 words, ~2,425 tokens.
Download SKILL.mdSave it as
.claude/skills/fact-checking/SKILL.md (or your agent's skills folder).- name
- fact-checking
- description
- Verify the accuracy of claims and statements by extracting individual assertions, identifying authoritative sources, cross-referencing evidence, and assigning confidence-scored verdicts. Use when the user requests fact checking or provides relevant inputs for this workflow.
- license
- MIT
- metadata.author
- awesome-ai-agent-skills
- metadata.version
- 1.0.0
Fact-Checking
This skill enables an AI agent to systematically verify claims and statements. Rather than offering a simple true/false judgment, the agent extracts discrete checkable claims from the input, identifies authoritative sources for each, cross-references evidence, and produces a structured verdict with a confidence score and supporting reasoning. The approach is designed to handle everything from single factual assertions to full articles containing dozens of claims.
Workflow
Extract Claims: Parse the input text and isolate individual, verifiable assertions. Each claim should be a single, self-contained statement that can be independently checked. Discard opinions, subjective judgments, and unfalsifiable statements, but note them as "not checkable" in the output.
Classify Claim Types: Categorize each claim by type — statistical (involves numbers or data), historical (references past events), scientific (references research findings), definitional (defines a term), or attribution (attributes a statement to a person or organization). The category guides where to look for verification.
Identify Authoritative Sources: For each claim, determine the most appropriate verification sources. Use primary sources whenever possible: official datasets for statistics, peer-reviewed papers for scientific claims, archived transcripts for quotations, and government records for legal or policy assertions. Supplement with reputable secondary sources like established fact-checking organizations (Snopes, PolitiFact, Full Fact).
Cross-Reference and Evaluate Evidence: Check each claim against at least two independent sources. Note whether sources corroborate, partially support, or contradict the claim. Assess source credibility by considering authority, recency, methodology, and potential bias.
Assign Verdicts and Confidence Scores: For each claim, assign a verdict from the scale: True, Mostly True, Half True, Mostly False, False, or Unverifiable. Accompany each verdict with a confidence score (0.0-1.0) reflecting the strength and consistency of available evidence, and a brief justification.
Compile the Fact-Check Report: Present findings in a structured format: list each claim, its verdict, confidence score, supporting evidence, and source links. Include an overall assessment summarizing the accuracy of the original text.
Usage
Provide the agent with a statement, paragraph, or full article to fact-check. You can optionally specify which claims to prioritize or request a particular output format.
Fact-check the following statement:
"Python is the most popular programming language in 2025 according to the TIOBE Index,
and it was created by James Gosling at Sun Microsystems in 1991."Examples
Example 1: Fact-Checking a Technical Blog Post
User Request:
Fact-check the key claims in this blog excerpt: "Rust has zero runtime overhead compared to C, making it the fastest systems language available. The Linux kernel adopted Rust as its primary development language in 2024, replacing C entirely. According to Stack Overflow's 2024 survey, Rust has been the most admired language for nine consecutive years."
Extracted Claims:
| # | Claim | Type |
|---|---|---|
| 1 | Rust has zero runtime overhead compared to C | Scientific/Technical |
| 2 | Rust is the fastest systems language available | Comparative |
| 3 | The Linux kernel adopted Rust as its primary language in 2024 | Historical |
| 4 | Rust replaced C entirely in the Linux kernel | Historical |
| 5 | Rust has been the most admired language for 9 consecutive years per Stack Overflow | Statistical |
Fact-Check Results:
| # | Verdict | Confidence | Evidence |
|---|---|---|---|
| 1 | Mostly True | 0.80 | Rust's zero-cost abstractions compile to machine code comparable to C. However, some runtime components (bounds checking, panic unwinding) introduce marginal overhead. Source: Rust Performance Book |
| 2 | Half True | 0.50 | Benchmarks show Rust and C performing within 0-5% of each other on most tasks, but neither consistently "wins." Fortran outperforms both in certain numerical workloads. Source: Benchmarks Game |
| 3 | Mostly False | 0.90 | Rust was accepted for driver and module development in Linux 6.1 (2022) and expanded in later releases, but it supplements C — it was never adopted as the "primary" language. Source: LWN.net kernel Rust coverage |
| 4 | False | 0.95 | C remains the dominant language of the Linux kernel. Rust is used in select new modules only. Source: kernel.org Git stats |
| 5 | Mostly True | 0.85 | Stack Overflow renamed "Most Loved" to "Most Admired" in 2023. Rust topped "Most Loved" from 2016-2022 (7 years) and "Most Admired" in 2023-2024 (2 years), totaling 9 years. Source: Stack Overflow Developer Survey Archives |
Overall Assessment: The blog post contains a mix of approximately true technical claims and significantly exaggerated statements about Rust's role in the Linux kernel. Readers should be cautious about the kernel-related claims.
Show full SKILL.md (575 more words)Show less
Example 2: Fact-Checking Statistical Claims
User Request:
Verify: "Global AI spending reached $200 billion in 2024, with 75% of Fortune 500 companies deploying generative AI in production. The average ROI for enterprise AI projects is 3.5x within the first year."
Extracted Claims:
| # | Claim | Type |
|---|---|---|
| 1 | Global AI spending reached $200 billion in 2024 | Statistical |
| 2 | 75% of Fortune 500 companies deployed generative AI in production | Statistical |
| 3 | Average ROI for enterprise AI projects is 3.5x in the first year | Statistical |
Fact-Check Results:
| # | Verdict | Confidence | Evidence |
|---|---|---|---|
| 1 | Mostly True | 0.75 | IDC estimated global AI spending at $184 billion for 2024, with Gartner projecting $196 billion. The $200 billion figure is within range of the higher estimates but not exact. Sources: IDC Worldwide AI Spending Guide (Oct 2024), Gartner AI Forecast (Nov 2024) |
| 2 | Half True | 0.60 | McKinsey's 2024 survey found 72% of organizations surveyed (not specifically Fortune 500) had adopted AI in some form, with 65% using generative AI. "In production" vs. "piloting" is a meaningful distinction the original claim does not make. Source: McKinsey Global AI Survey 2024 |
| 3 | Unverifiable | 0.30 | No credible large-scale study has published a generalizable "average ROI" figure for enterprise AI. Individual case studies vary wildly (0.5x to 10x+). BCG and MIT Sloan have cautioned against generalized ROI claims. Source: MIT Sloan Management Review (2024) |
Overall Assessment: The spending figure is approximately correct, the adoption statistic is in the right ballpark but imprecise, and the ROI claim lacks credible sourcing and should not be cited without qualification.
Best Practices
- Isolate each claim before verifying. Complex sentences often bundle multiple assertions. Splitting them ensures nothing is overlooked and verdicts remain precise.
- Prioritize primary sources over secondary reporting. A news article saying "a study found X" is less reliable than reading the study itself. Always trace claims to their origin.
- Account for context and framing. A technically true number can be misleading if taken out of context. Note when a claim is true but presented in a way that implies something false.
- Use the confidence score honestly. A score of 0.5 is not a failure — it reflects genuine ambiguity. Overconfident verdicts erode trust more than honest uncertainty.
- Check the date of the claim and the source. A claim that was true in 2020 may be false in 2025. Always verify that the evidence is temporally relevant to the assertion.
- Distinguish between "false" and "unverifiable." If no credible evidence exists either way, the verdict should be "Unverifiable," not "False."
Edge Cases
- Claims about the future: Predictions ("AI will replace 50% of jobs by 2030") cannot be fact-checked against evidence. Label them as "Predictive — not verifiable" and note the credibility of the source making the prediction.
- Rapidly changing statistics: If the claim involves a metric that updates frequently (e.g., cryptocurrency prices, COVID case counts), note the date the claim refers to and the date of verification, since the answer may differ.
- Satirical or hyperbolic content: If the source material is clearly satirical or uses deliberate exaggeration for rhetorical effect, note this context rather than issuing a literal "False" verdict.
- Claims with no authoritative source: Some niche or proprietary claims (e.g., internal company metrics) may have no publicly verifiable source. Label these "Unverifiable — no public source" and recommend the user request documentation from the claimant.
- Ambiguous wording: When a claim can be interpreted multiple ways (e.g., "most popular" could mean by usage, by survey, or by downloads), evaluate the most reasonable interpretation and note the ambiguity.
© seb1n, 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 research-and-knowledge/fact-checking of seb1n/awesome-ai-agent-skills.
Open the folder on GitHubat commit 75865a5
Compare with similar skills
Fact Checking 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 |
|---|---|---|---|---|---|---|
| Fact Checking this skillseb1n/awesome-ai-agent-skills | 206 | — | ~2.4k | Automated safety check: Pass | MIT | |
| Perplexity Web Searchdavila7/claude-code-templates | 32k | 12 repos | ~3.5k | Automated safety check: Notes | MIT | |
| Citation Verification GuideGalaxy-Dawn/claude-scholar | 5.7k | 3 repos | ~1.9k | Automated safety check: Pass | MIT | |
| Article Fact Checkerdigoal/blog | 8.6k | — | ~939 | Automated safety check: Pass | GPL-2.0 | |
| Deep Research Agent TeamImbad0202/academic-research-skills | 51k | — | ~13k | Automated safety check: Pass | Custom licence | |
| Docs Grounding Verifiermicrosoft/apm | 4k | — | ~1.9k | Automated safety check: Pass | MIT |
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三层审查模型,逐段逐句验证文章真伪、证据链与逻辑结构。Use when the user asks to fact-check, verify, audit, or evaluate the credibility of an article, essay, report, opinion piece, social-media post, or any written claim —…
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Related
Categories
Questions about Fact Checking
What does Fact Checking do?
Verify the accuracy of claims and statements by extracting individual assertions, identifying authoritative sources, cross-referencing evidence, and assigning confidence-scored verdicts. Fact Checking is an agent skill from seb1n/awesome-ai-agent-skills. Verify the accuracy of claims and statements by extracting individual assertions, identifying authoritative sources, cross-referencing evidence, and assigning confidence-scored verdicts.
When should I use Fact Checking?
Fact Checking fits situations like: the user requests fact checking; provides relevant inputs for this workflow.
How do I install Fact Checking in Claude Code?
Run `npx skills add seb1n/awesome-ai-agent-skills --skill fact-checking -a claude-code`. Or copy the skill folder (research-and-knowledge/fact-checking in seb1n/awesome-ai-agent-skills) into .claude/skills/fact-checking in your project. Claude Code loads it when a task matches its description.
How do I install Fact Checking in Codex?
Run `npx skills add seb1n/awesome-ai-agent-skills --skill fact-checking -a codex`. Or copy the skill folder (research-and-knowledge/fact-checking in seb1n/awesome-ai-agent-skills) into .agents/skills/fact-checking in your project. Codex loads it when a task matches its description.
Can I use Fact Checking 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 seb1n/awesome-ai-agent-skills --skill fact-checking -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fact-checking, .gemini/skills/fact-checking, .github/skills/fact-checking and .opencode/skills/fact-checking in your project.
What does Fact Checking need to run?
SKILL.md names no scripts, command-line tools or credentials: Fact Checking is instructions for the agent only. Our summary lists: Python 3.
Does Fact Checking access the network?
SKILL.md names 5 domains. As links in the text: nnethercote.github.io, benchmarksgame-team.pages.debian.net, lwn.net, git.kernel.org and survey.stackoverflow.co. This is read from the text; nothing was executed.
Is Fact Checking 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 Fact Checking use?
Fact Checking is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Fact Checking use?
About 2.4k tokens (SKILL.md is roughly 9.7k 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 Fact Checking?
Skills that share tags, products or a category with Fact Checking: Perplexity Web Search (davila7/claude-code-templates, 32k stars), Citation Verification Guide (Galaxy-Dawn/claude-scholar, 5.7k stars), Article Fact Checker (digoal/blog, 8.6k stars) and Deep Research Agent Team (Imbad0202/academic-research-skills, 51k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Fact Checking?
seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 92 skills in this directory. The repository was last updated on August 9, 2026.
Source: seb1n/awesome-ai-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.