self-review
Skill19 starsChanged 9 days agoMITView on GitHub
After drafting, reread the text as a native reader in its own language. If it still reads as machine-written, rewrite it. Works in any language. Sub-skill: load only when routed from the hook-everything entry skill, not for general writing or editing.
51 other projects use a version of this.
Adopted by
Most recently taken up by
- Fractera/Agent-Engineering-Infrastructuretheir own version, first seen Nov 8, 2026
- Fractera/Agentic-Engineering-Infrastructuretheir own version, first seen Nov 8, 2026
- aniketshukla1/sdlc-kittheir own version, first seen Nov 8, 2026
- plannotator/dev-skillstheir own version, first seen Nov 8, 2026
- crbnos/carbontheir own version, first seen Nov 7, 2026
- inkatze/planwrighttheir own version, first seen Nov 4, 2026
Used with
Projects with it also use latex-formatting, novelty-assessment and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
lingzhi227Skilllatex-formatting
Handle LaTeX formatting, templates, and styling for academic papers. Set up conference templates (ICML, ICLR, NeurIPS, AAAI, ACL), fix formatting issues, manage packages, and ensure venue-specific compliance. Use when the user needs to set up a paper template, fix LaTeX formatting, or prepare for submission.
Also in 4 projects that use this
lingzhi227Skillnovelty-assessment
Assess research idea novelty through systematic literature search. Multi-round search-evaluate loops with harsh critic persona. Binary novel/not-novel decision with justification. Use before committing to a research direction.
Also in 4 projects that use this
michaelboedingSkillslide-generation
Use this skill to create presentation slides from structured content. Triggers: "create slides", "generate presentation", "make powerpoint", "create pptx", "build slides", "presentation from content", "slide deck", "slides for" Outputs: PPTX files, Markdown slides, or HTML presentations. Used by: pitch-deck-agent, market-researcher-agent, and other agents needing slides.
Also in 4 projects that use this
juanlurgSkillpaper-to-code
Reads a research paper and generates an implementation notebook with equations, code, and a toy example. Use when the user wants to implement a paper, convert a paper to code, or reproduce a paper's algorithm.
Also in 4 projects that use this
OpenAISkillidea-generation
Use when triaging public-equity idea candidates. Do not use for final trade recommendations, pitches, memos, or models.
Also in 3 projects that use this
sunnyclinches44Skillexplain
Explains code using real-world analogies and ASCII diagrams
Also in 5 projects that use this
- +6 more from lingzhi227
Other versions51 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 51
- huggingface/diffusers★ 35ksame name
- zulip/zulip★ 26ksame name
- flashinfer-ai/flashinfer★ 6.9ksame name
- unkeyed/unkey★ 5.5ksame name
- crbnos/carbon★ 2.8ksame name
- verl-project/verl-omni★ 1.5ksame name
- K9i-0/ccpocket★ 1.1ksame name
- anyproto/anytype-swift★ 525same name
- hegeldev/hegel-rust★ 400same name
- lingzhi227/agent-research-skills★ 350same name
41 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository14 other files it gives its agents.
- AGENTS.mdAGENTS.md
- hook-everythingSkill
- anti-slopSkill
- audience-calibrationSkill
- creative-ideationSkill
- hook-compoundingSkill
- marketing-hooksSkill
- market-intelligenceSkill
- product-hooksSkill
- psychology-mapSkill
- trust-architectureSkill
- value-checkSkill
- visual-ux-hooksSkill
- writing-hooksSkill
The file
We split this skill into the rules it gives an agent
8 rules. 0 are common, 4 are distinctive, and 4 are very specific to this project.
0
Common
Most of the 42 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 18
Run
python3 tools/slop_scan.py <file> --fix-hints --hidden: when a shell is available. - Very specific: line 20
Repeat from step 2: Stop when the verdict is reads human and the score is <= 15/100, or after 3 rounds.
- Very specific: line 28
Do not invent personal detail to sound human: No fake anecdotes, no made-up "last Tuesday" stories, no deliberate typos.
- Distinctive: line 35
Run the checks for the language of the text.
- Distinctive: line 49
Every sentence has about the same length and the same temperature.
- Distinctive: line 54
Every point is hedged on both sides, and the text never takes a position.
- Distinctive: line 61
Ask them in the text's own language, then answer with a quoted span.
- Very specific: line 67
If I deleted the first and last sentences, would any fact be lost?
“Common” is measured: kept by at least 6 in 10 of the 42 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Self-Review Loop (Read It Cold, Rewrite If It Still Smells)
A draft that passes a word list can still read as machine-written. This skill adds a reader's pass at the end: read the text as a skeptical native speaker, say whether it still reads as AI, and fix it until it doesn't. It works in any language.
Be honest about the limit. A model judging its own text is an unreliable detector, so this pass never relies on a feeling alone. Every verdict needs quoted evidence, and the scanner has the last word on score.
The Loop (maximum 3 rounds)
- Switch to the reader's language and register. Judge the text in the language it is written in. Never translate it to English to judge it, because translation hides the calques and register slips you are looking for. Picture one real reader: a native speaker of that language who reads a lot and distrusts polished copy.
- Read it cold, start to finish, without editing. Then give one verdict:
reads human,unsure, orreads machine-made. - Back the verdict with evidence. Quote the 3 spans that feel most machine-made and name the tell for each (
uniform sentence weight,abstract noun as subject,see-saw concession,claim-example-moral paragraph,calque from English,no concrete anchor in 35 words). If you cannot quote a span, you have no verdict, so run the scan. - Run
python3 tools/slop_scan.py <file> --fix-hints --hiddenwhen a shell is available. Treat a score above 15 or any hidden-character hit as a failed round, whatever the verdict was. - Rewrite the flagged spans from scratch. Change the subject, the verb and the sentence shape. Swapping a synonym into the same skeleton fixes nothing (
anti-slopRule 72). - Repeat from step 2. Stop when the verdict is
reads humanand the score is<= 15/100, or after 3 rounds. After round 3, list what still bothers you and hand it over instead of looping again.
What Never Changes in a Rewrite
- Facts, numbers, names and dates stay exactly as they were. A rewrite that changes a figure is a new claim.
- Quoted text stays word for word (
anti-slopRule 42). - Do not invent personal detail to sound human. No fake anecdotes, no made-up "last Tuesday" stories, no deliberate typos.
value-checkitem 2 forbids fabricated receipts, and made-up texture counts. When a piece needs lived detail, leave a bracket for the person to fill:[your own example here]. - Keep the author's register. A formal legal memo should come back formal and plain, not chatty.
Per-Language Reader Checks
Run the checks for the language of the text. For languages not listed, use the Universal block.
| Language | Extra tells a native reader notices |
| English | Em-dash glue, "not just X, it's Y", two-word rhetorical questions, three-adjective stacks, a closing summary paragraph. |
| Vietnamese | Pronoun stuffing (bạn, của bạn, chúng tôi in every clause), việc + động từ nominalizations, không chỉ... mà còn... openers, stock phrases like trong bối cảnh. |
| Spanish | en el mundo actual openers, juega un papel fundamental, calques of English passive voice, subject pronouns where Spanish drops them. |
| French | dans un monde en constante évolution, il est important de noter que, English-style noun stacks, vous and nous repeated where French drops them. |
| German | in der heutigen schnelllebigen Welt, long nominal chains with -ung nouns, English word order in subordinate clauses. |
| Chinese / Japanese / Korean | Stock frames (在当今..., 〜において重要な役割を果たします, 오늘날...), literal translation of English connectors, stiff register that no one speaks, over-explicit subjects in Japanese and Korean. |
| Universal (any language) | See below. |
Universal block, for any language
- Every sentence has about the same length and the same temperature.
- Paragraphs share one shape: claim, two examples, a moral.
- Syntax follows English even though the words are not English (a calque of structure, not of vocabulary).
- Register is flat and over-polite from the first line to the last, with no slang, no shorthand, no mid-thought correction.
- Abstract nouns stand in for people and actions, and nothing in a 35-word stretch is concrete.
- Every point is hedged on both sides, and the text never takes a position.
- A list or heading structure imposed on something a person would have just said.
Quick Self-Test Questions
Ask them in the text's own language, then answer with a quoted span.
- Which sentence would a native speaker never say out loud to a colleague?
- Where does the text explain something nobody asked about?
- Which paragraph could be moved to a different article without anyone noticing?
- Where does the writer sound more careful than a person would be?
- If I deleted the first and last sentences, would any fact be lost? (
anti-slopRule 73)
Hand-Off
End the loop with one line for the user: the final verdict, the scanner score if it ran, and anything left unresolved. Then run skills/value-check.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

