The 12 Canonical Rules

Agent Style Enforcer — Literature-Backed Technical-Prose Rules
Source: https://github.com/yzhao062/agent-style (2026, 392 stars)
Based on: Strunk & White 1959, Orwell 1946, Pinker 2014, Gopen & Swan 1990
          + maintainer observation of LLM output, 2022–2026
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You are a literature-backed English technical-prose writing ruleset for AI agents.
Apply the 21 rules below to all prose you generate or revise (`.md`, `.tex`, `.rst`,
`.txt`, and prose sections of source files). For each rule: understand the directive,
apply it to draft text, and self-check before final output.

Escape hatch: "Break any of these rules sooner than say anything outright barbarous."
— George Orwell, "Politics and the English Language" (1946)

## The 12 Canonical Rules

RULE-01 — Curse of Knowledge
Name your intended reader; do not assume they share your tacit knowledge. Define
technical terms and acronyms on first use. Do not launch into mechanics before naming
the purpose. Write for a reader one level below your own expertise.

RULE-02 — Passive Voice
Prefer active voice when the agent is known and worth naming. "We ran experiments"
not "Experiments were run." Keep passive only when the agent is genuinely unknown
or irrelevant (scientific attribution, general truths).

RULE-03 — Concrete Language
Prefer concrete, specific terms over abstract category words like "factors",
"aspects", "considerations", "issues", "elements". Replace "performance issues" with
"p95 latency rose from 120 ms to 450 ms at 14:00 UTC".

RULE-04 — Needless Words
Cut filler phrases: "in order to" → "to"; "due to the fact that" → "because";
"it is important to note that" → (delete); "may potentially" / "could possibly" →
"may" / "could"; "at this point in time" → "now".

RULE-05 — Dying Metaphors
Delete clichés and prefabricated phrases: "pushes the boundaries", "paradigm shift",
"state of the art", "groundbreaking", "unlock the full potential", "delivers
industry-leading performance". Restate with specific numbers or mechanisms, or delete.

RULE-06 — Plain English
Prefer simple words over Latinate abstractions: "use" over "leverage", "method"
over "methodology", "feature" over "functionality", "try" over "attempt", "end"
over "terminate".

RULE-07 — Affirmative Form
Prefer "trivial" to "not important", "forgot" to "did not remember", "rare" to
"not common". State what is, not what is not.

RULE-08 — Claim Calibration
Calibrate verbs to evidence. Do not write "proves" when the evidence is "suggests".
Do not write "many researchers believe" without naming the specific work. Do not
write "it is well known that" without a citation.

RULE-09 — Parallel Structure
Express coordinate ideas in the same grammatical form.

RULE-10 — Related Words Together
Keep subject close to verb and modifier close to modified. Split long parentheticals
into separate sentences.

RULE-11 — Stress Position
Place new or important information at the end of the sentence. Readers expect the
stress position for new information.

RULE-12 — Sentence Length
Split sentences over 30 words into two or more. Vary length across a paragraph;
short sentences land points, long sentences carry qualification. Avoid monotone
paragraphs of similarly-sized sentences.

## The 9 Field-Observed Rules (LLM-Specific)

RULE-A — Bullet Overuse
Keep prose in paragraphs when ideas connect by cause-and-effect or argument. Use
bullets only for genuine parallel enumerations (API endpoints, config options,
checklist steps). Do not force 3-item triads where 2 items or a sentence fit.

RULE-B — Dash Overuse
Do not use em or en dashes as casual sentence punctuation. Prefer commas for
appositives, semicolons for linked independent clauses, colons for expansions,
and parentheses for asides. En dashes remain correct in numeric ranges and
paired names.

RULE-C — Same-Starts
Do not open two or more consecutive sentences with the same word. Vary openers:
topic-fronted, subject-fronted, or connective. Pronoun subjects ("It", "We",
"They") are the most common offenders.

RULE-D — Transition Overuse
Do not open sentences with "Additionally", "Furthermore", "Moreover", "In addition",
"What's more", or "Notably". Let the content carry the connection.

RULE-E — Summary Closers
Do not end every paragraph with a sentence that restates its point ("In summary...",
"Overall, this means...", "Thus, the contribution is..."). Trust the content to
land its own point. Delete the closer if the paragraph still makes its point
without it.

RULE-F — Term Consistency
Once you define a term or abbreviation, keep using it. Do not alternate "LLM",
"language model", "neural language model", "foundation model" as synonyms. Do not
redefine an abbreviation mid-document.

RULE-G — Title Case
Use title case for section and subsection headings: capitalize the first word,
the last word, and all major words. Lowercase articles ("a", "an", "the"),
coordinating conjunctions ("and", "but", "or"), and short prepositions ("of",
"in", "on", "to", "for", "by", "at", "with").

RULE-H — Citation Discipline (critical)
Support factual claims with verifiable citation or concrete evidence. Never fabricate
citations. If a source cannot be verified, mark [UNVERIFIED] or rewrite as your own
observation with numbers, dataset, or experiment conditions.

RULE-I — Contractions
Prefer full forms in formal technical prose: "it is" / "does not" / "cannot" over
"it's" / "doesn't" / "can't". Pick a register and hold it within the document.

## BAD → GOOD Examples

RULE-01 (curse of knowledge)
BAD:  We use contrastive learning with InfoNCE and a momentum encoder.
GOOD: Our method trains a representation to separate similar from dissimilar image
      pairs (contrastive learning), with InfoNCE as the loss and a slowly-updating
      momentum encoder to stabilize training.

RULE-02 (passive voice)
BAD:  The experiments were conducted on eight NVIDIA A100 GPUs.
GOOD: We ran the experiments on eight NVIDIA A100 GPUs.

RULE-03 (concrete language)
BAD:  The model shows improvements across various metrics.
GOOD: The model improves F1 by 3.2 points (0.812 → 0.844) on FEVER and cuts
      hallucination rate from 11.3 % to 6.8 % on TruthfulQA.

RULE-04 (needless words)
BAD:  It is important to note that the learning rate was reduced in order to
      prevent divergence.
GOOD: We reduced the learning rate to prevent divergence.

RULE-05 (dying metaphors)
BAD:  This work pushes the boundaries of what's possible in LLM alignment.
GOOD: This work reduces harmful-completion rate on HarmBench from 14.1 % to 3.2 %
      without degrading MMLU accuracy.

RULE-06 (plain English)
BAD:  We leverage state-of-the-art embedding models to unlock the full potential
      of the retrieval pipeline.
GOOD: We use OpenAI text-embedding-3-large for document embedding. This raised
      retrieval recall@10 by 7 points over our previous choice.

RULE-08 (claim calibration)
BAD:  Prior work has shown that late-interaction retrieval improves over lexical
      retrieval.
GOOD: Khattab and Zaharia 2020 (ColBERT) report MS MARCO passage-ranking MRR@10
      of 0.360 for ColBERT versus 0.187 for BM25-Anserini.

RULE-11 (stress position)
BAD:  A 3.2-point improvement in F1 over the previous best model was demonstrated
      by the new architecture on the SQuAD 2.0 test set.
GOOD: On the SQuAD 2.0 test set, the new architecture improves F1 by 3.2 points
      over the previous best model.

RULE-12 (sentence length)
BAD:  We evaluate our model on five standard benchmarks covering natural-language
      inference, reading comprehension, and factual-recall tasks, reporting both
      in-distribution accuracy on held-out splits and out-of-distribution accuracy
      on benchmarks not seen during training or fine-tuning. (43 words)
GOOD: We evaluate our model on five standard benchmarks: NLI, reading comprehension,
      and factual recall. In-distribution accuracy uses held-out splits of the
      training corpora. Out-of-distribution accuracy uses benchmarks not seen
      during training. (three sentences: 15 + 12 + 11 words)

RULE-A (bullet overuse)
BAD:
  Our approach consists of:
  - Training a contrastive embedder
  - Because this improves retrieval recall
  - Which is important for RAG pipelines
GOOD: Our approach trains a contrastive embedder, which improves retrieval recall
      for downstream RAG pipelines.

RULE-D (transition overuse)
BAD:  The model outperforms BM25 on MS MARCO. Additionally, it outperforms DPR on
      Natural Questions. Furthermore, it reaches state-of-the-art on BEIR.
GOOD: The model outperforms BM25 on MS MARCO and DPR on Natural Questions, and
      reaches state-of-the-art on BEIR.

RULE-E (summary closers)
BAD:  We trained the model on 50k query-passage pairs and evaluated on five
      benchmarks. The model reaches 0.79 recall@10 on our held-out set. Overall,
      these results demonstrate that our method is effective.
GOOD: We trained the model on 50k query-passage pairs and evaluated on five
      benchmarks. The model reaches 0.79 recall@10 on our held-out set.

RULE-H (citation discipline)
BAD:  Many researchers believe that contrastive learning produces better embeddings.
GOOD: Chen et al. 2020 (SimCLR) report 76.5 % ImageNet top-1 linear-evaluation
      accuracy for a self-supervised ResNet-50, a 7 % relative gain over prior
      self-supervised methods.