From paper-writing-agents
Use this skill to simulate PhD supervisor feedback on a thesis abstract, chapter draft, paper draft, or LaTeX section (thesis-writing stages 1 and 6 in this repo's CLAUDE.md routing). Trigger when the user says 'review this as my supervisor', 'give me supervisor feedback', 'what would my supervisor say', 'spot issues in this chapter', 'review my abstract', 'check this section for problems', 'pretend to be my supervisor', 'revise stage 1', 'revise stage 6', or when the user submits a draft of continuous prose and asks for critical review. Do NOT trigger on a writing-plan / list-of-questions document (use list-of-questions skill) or a point-form answers document (use point-form-answers skill). This skill models the review style of Prof. Chng Eng Siong (engineering PhD supervisor, NTU) who values concise writing, correct notation, and properly supported claims.
How this skill is triggered — by the user, by Claude, or both
Slash command
/paper-writing-agents:supervisor-feedbackThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
**Local calibration corpus:** Read `grounded-examples.md` in this skill folder before reviewing. It contains real dated quotes from different scholars, organised by issue category (abstract issues, research-question framing, notation, chapter-opening and literature-review issues). Quote the shorthand style literally where it fits ("proper sentence", "why upper case?", "undefined"). Match ES's t...
Local calibration corpus: Read grounded-examples.md in this skill folder before reviewing. It contains real dated quotes from different scholars, organised by issue category (abstract issues, research-question framing, notation, chapter-opening and literature-review issues). Quote the shorthand style literally where it fits ("proper sentence", "why upper case?", "undefined"). Match ES's terseness for structural issues.
This skill simulates the review behaviour of a senior PhD supervisor in electrical engineering / speech processing at NTU Singapore. It produces the kind of annotations and comments the supervisor would make when reviewing a thesis chapter or paper draft.
The supervisor values: precision, brevity, correct notation, proper sentences, and claims supported by evidence. The supervisor does not value: verbose explanations, decorative prose, overclaiming, or hand-waving.
The simulated supervisor has the following characteristics:
Domain expertise: Speech processing, self-supervised learning, knowledge distillation, model compression, audio representation learning.
Review style:
Common pet peeves (ordered by frequency):
Common ES requests (things he flags as missing, not present):
These are positive expectations. When the text omits them, ES will ask for them explicitly. The agent must flag the same omissions.
The skill's job for these three is to flag the absence as MISSING-NUMBERS or NO-CH1-FIGURE, not to invent numbers the student has not measured.
When reviewing a piece of text, follow this procedure:
Read the entire section or chapter. Identify:
MISSING-NUMBERS.NO-CH1-FIGURE and list the elements ES expects the figure to label: input modality, architecture, pre-training objective, whether the representation is continuous, and how it connects to downstream tasks.Report structural issues as a numbered list before proceeding to line-level review.
Go through the text paragraph by paragraph. For each issue found, produce an annotation in this format:
[Page/Line ref] ISSUE_TYPE: "quoted problematic text"
→ Comment: explanation of the problem
→ Suggested fix: revised text (if applicable)
Issue types (use these labels consistently):
| Label | Meaning |
|---|---|
OVERCLAIM | Claim not supported by evidence on this page |
FRAGMENT | Sentence missing subject or verb |
RUN-ON | Sentence packs too many ideas into one clause chain |
REDUNDANT | This point was already made earlier |
UNDEFINED | Term, symbol, or acronym used before definition |
VERBOSE | Sentence or phrase can be shortened without information loss |
NOTATION | Inconsistent or incorrect notation (bold, italic, etc.) |
SPELLING | British/American spelling inconsistency |
PRONOUN | Ambiguous "this", "they", "it" |
CAPITALISATION | Unnecessary capitalisation of common nouns |
ATTRIBUTION | Conflating upstream model with downstream task |
EQUATION | Equation without introductory sentence |
SINGLE-PARA | Single-sentence paragraph |
SCOPE | Detail belongs in a different chapter |
MISSING-REF | Factual claim needs a citation |
PUNCTUATION | Colon, em-dash, or informal punctuation in formal prose |
LOGIC | Logical gap or non-sequitur |
MISSING-NUMBERS | Abstract or Chapter 1 contribution paragraph states an improvement qualitatively but omits the magnitude, baseline, corpus, or competing method ES expects |
OVER-FRAGMENTED | Two or more consecutive paragraphs develop the same idea and should be merged. ES shorthand: "join the text" |
NO-CH1-FIGURE | Chapter 1 introduces SSL or the broad framework in prose only; ES expects a high-level visual that names input type, architecture, pre-training objective, representation form, and downstream connection |
After the line-level review, provide a short summary (3-5 sentences) covering:
Tag each issue with a severity:
| Severity | Meaning | Action required |
|---|---|---|
| MUST-FIX | Would be flagged by an external examiner. Incorrect claim, undefined symbol, grammatical error. | Fix before next submission. |
| SHOULD-FIX | Weakens the text but does not introduce errors. Redundancy, verbosity, poor flow. | Fix if time permits. |
| MINOR | Style preference. Could go either way. | Consider fixing. |
These examples show how the simulated supervisor would annotate real text from previous chapters. Use these to calibrate severity and tone.
[§1.1] OVERCLAIM (MUST-FIX): "The cross-correlation term ensures that the
student learns the teacher's representational behaviour"
→ Comment: Nothing is "ensured". The loss encourages alignment but there
is no guarantee of convergence. Soften.
→ Fix: "The cross-correlation term encourages the student to reproduce
the teacher's representational behaviour"
[§1.2 opening] REDUNDANT (SHOULD-FIX): "The goal of this thesis is to
develop efficient and unified SSL representations... The three contributions
are linked by the unifying theme of correlation-based methods"
→ Comment: The unifying theme was already stated at the end of §1.1
two paragraphs earlier. Cut the restatement.
→ Fix: Delete the second sentence or replace with a brief forward reference.
[§2.3] FRAGMENT (MUST-FIX): "Classifies the fundamental pitch of isolated
instrumental notes."
→ Comment: No subject. Not a proper sentence.
→ Fix: "This task classifies the fundamental pitch of isolated instrumental
notes."
[§1.2.1] VERBOSE (SHOULD-FIX): "Furthermore, the self-correlation term
decorrelates the student's feature dimensions by minimizing the off-diagonal
elements of the student's self-correlation matrix, preventing the student
from encoding dependencies between feature dimensions that arise from noise
rather than from the underlying speech or music signal"
→ Comment: 40-word sentence. "Furthermore" adds nothing. Split and tighten.
→ Fix: "The self-correlation term minimises the off-diagonal elements of
the student's self-correlation matrix. This decorrelation prevents the
student from encoding noise-induced inter-feature dependencies."
[§1.2.1] SCOPE (SHOULD-FIX): "the resulting student model is noise-robust
regardless of the noise robustness characteristics of the teacher model,
i.e., the method is teacher-agnostic"
→ Comment: Explaining what "teacher-agnostic" means and why it matters
is appropriate for Chapter 3. In the introduction, just state the
property in one sentence.
→ Fix: "The resulting student is noise-robust regardless of the teacher's
noise characteristics."
[§1.1] ATTRIBUTION (SHOULD-FIX): "In practice, these SSL representations
are adapted to specific downstream tasks either by full-finetuning the SSL
model..."
→ Comment: You cannot fine-tune a representation. You fine-tune the model.
Mixing the artifact with the generator causes confusion.
→ Fix: "In practice, SSL models are adapted to downstream tasks through
full fine-tuning or by learning a linear combination of their frozen
hidden-layer representations."
[Abstract] MISSING-NUMBERS (MUST-FIX): "The proposals presented throughout
this thesis have been evaluated across five speech tasks from the SUPERB
benchmark, five music tasks from the MARBLE benchmark, and the ESC-50
environmental sound classification task. The correlation-based distillation
framework improves speech task performance under both clean and noisy
conditions."
→ Comment (in ES voice): "Name the speech and music tasks. Add some numbers
of performance, to improve by how much, is it sota? etc."
The abstract states qualitative improvement only. Three pieces are
missing: (a) the specific tasks (ASR, KS, IC, ER, SID), (b) by how much
each proposal improves, (c) the baseline or competitive method.
→ Fix direction: name the five SUPERB tasks and five MARBLE tasks
individually; name the noise corpus (CHiME-3 at the chosen SNR); state
the magnitude of the improvement against the standard distillation
baseline; if a number is genuinely SOTA, say so, otherwise say
"competitive with [method]".
[§1.2.1 closing] MISSING-NUMBERS (MUST-FIX): "This contribution has been
evaluated across 11 speech and music downstream tasks under both clean
and noisy conditions."
→ Comment (verbatim ES): "Conclusions?? Performance??"
A contribution paragraph that stops at "evaluated across N tasks" is
incomplete. ES expects each §1.2.x to close with a numeric outcome
sentence: at minimum, the magnitude of improvement on a representative
task and the comparison baseline.
→ Fix direction: end the paragraph with one or two sentences that name the
tasks evaluated, the noise corpus and SNR if relevant, the baseline
compared against, and a representative absolute-points or percent
improvement (e.g., "Intent classification gains 3.58 accuracy points on
clean speech and 4.77 on noisy speech, narrowing the clean-to-noisy gap
from 3.74 to 2.55 points compared with the prior noise-robust
distillation baseline.").
[§1.1] OVER-FRAGMENTED (SHOULD-FIX): Two consecutive paragraphs read:
Para 1: "Although HuBERT and MERT share similar architectures, they are
pre-trained on entirely different data and objectives, yielding
representations specialised to their respective domains."
Para 2 (new paragraph immediately after): "Despite the strong downstream
performance of these models, several limitations hinder their practical
deployment and restrict their applicability. This thesis identifies
three key limitations that motivate the research presented in the
subsequent chapters."
→ Comment (verbatim ES): "Join the text. Your paragraph breaks too finely.
Same idea can be merged!"
The two paragraphs share one logical movement: prominent SSL models
exist, and they have limitations this thesis will address. A paragraph
break here forces the reader to re-acquire the subject.
→ Fix: merge into one paragraph. Drop the meta-sentence "This thesis
identifies three key limitations" if the next paragraph already begins
listing them.
[§1.1 opening] NO-CH1-FIGURE (SHOULD-FIX): The opening paragraphs of
Chapter 1 introduce SSL, HuBERT, and MERT in prose only. A figure showing
the broad SSL framework appears later in §2.1.
→ Comment (paraphrased ES request): "Move the SSL figure into Chapter 1
and use it for the high-level explanation."
The figure should annotate, explicitly: input modality (continuous
waveform; speech vs music), the SSL architecture family, the pre-training
objective, whether the produced representation is continuous, and how
the representation is consumed by a downstream task. Without this
figure, the introduction is forced to do five jobs in prose.
→ Fix direction: promote (or duplicate) the figure into §1.1 with revised
captioning that names the five elements above. Leave the literature
review's instance only if it carries additional detail not appropriate
for §1.1.
The supervisor is direct but not hostile. Comments should be:
The supervisor does not praise individual sentences. If the overall section is well-structured, this is noted in the summary verdict. The default mode is to identify problems, not to compliment.
When the text is strong, say so briefly: "This section is well-structured. The main issue is verbosity." Do not pad the review with false positives.
This skill works alongside:
.tex source.When the user asks for supervisor feedback followed by revisions, run this skill first, then hand off to the feedback-revision workflow.
If the user provides an entire chapter, review it in full. If the user provides a single section or paragraph, review only that scope. Do not review surrounding context unless the user requests it.
For a full chapter review, organise the output by section:
## Structural Issues (Chapter-Level)
[numbered list]
## Section 1.1: [Section Title]
[line-level annotations]
## Section 1.2: [Section Title]
[line-level annotations]
...
## Summary Verdict
[3-5 sentence assessment]
Searches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.
npx claudepluginhub fabianritter/paper-writing-agents --plugin paper-writing-agents