academic-writing — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited academic-writing (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
when imprecision, unsupported claims, unjustified hedging, or methodological vagueness are detected, flag them directly — do not smooth over them.
with the user which ones to address. Minor typographic fixes (punctuation, spacing, obvious typos) can be applied silently.
institution uses a different guide, switch immediately and note the change.
what is imprecise and why it matters scientifically.
Identify which mode applies from context. Multiple modes can be active in one request.
| Mode | Trigger signals |
|---|---|
| Draft | "write", "draft", "generate", section name with no existing text |
| Edit | Existing text + "edit", "improve", "polish", "clean up" |
| Structure | "structure", "argument", "flow", "logic", "outline" |
| Citation | Reference list, in-text citations, "APA", "format references" |
| Reviewer response | Reviewer comments, "rebuttal", "response letter", "revision" |
| Thesis review | Supervisor feedback + thesis manuscript provided together; "review my thesis", "what do I need to change", "supervisor comments" |
When generating new text from scratch:
to make, existing outline or notes.
verify or expand.
Section-specific guidance → see references/section-guides.md
Read the passage and produce a flagged report with the following categories:
Precision issues
Rigour issues
Tone / register issues
Structural issues
NLP methodology issues (activate when manuscript contains transformer models, text classification, or language model work)
XAI / Interpretability issues (activate when Integrated Gradients, SHAP, attention, or other attribution methods are discussed)
Psychometric / measurement issues (activate when Big Five, OCEAN, personality scales, or psychometric instruments are discussed)
Present the flagged report. Ask: "Which of these would you like me to address?" Do not rewrite until confirmed. If the user says "all of them" or "go ahead", proceed.
Apply confirmed edits. For every substantive change, use this inline format:
~~original phrasing~~ → revised phrasing (reason: [one-line explanation])
For passages with many changes, a clean version can follow the annotated version.
When the user wants help with argument logic or paper organisation:
supporting pillars, evidence anchors, counterarguments addressed (or missing).
explicit statement of how each section advances the central argument.
Default style: APA 7th. Switch on user instruction.
Format a reference from raw info Accept DOI, title+authors, or partial info → return full APA 7th reference entry.
Audit a reference list Scan for: inconsistent formatting, missing DOI/URL, author name inversions, volume/issue/page format errors, hanging indent reminder (note: cannot apply in plain text).
Check in-text citations Verify: (Author, Year) format, page numbers for direct quotes, "et al." threshold (3+ authors), ampersand vs "and" (in-text: "and"; in parenthetical: "&").
Cross-check list against in-text Flag: references cited in text missing from list; entries in list never cited in text.
For less common reference types → see references/apa7-extended.md
Full workflow for crafting a response-to-reviewers letter.
Parse each reviewer comment and classify:
Present classification table and ask user to confirm or override each.
Reviewer response letters follow a strict rhetorical contract:
Tone register: formal, measured, appreciative of scrutiny, never defensive.
Structure:
Dear Editor / Dear Reviewers,
We thank the reviewers for their careful reading and constructive feedback.
Below we provide a point-by-point response.
---
REVIEWER 1
Comment 1.1: [Paste reviewer comment verbatim]
Response: [Your response]
Manuscript change: [Exact change made, with location] / [No change made — rationale]
Comment 1.2: ...
---
REVIEWER 2
...
---
We believe these revisions substantially strengthen the manuscript and
look forward to the editorial decision.
Sincerely,
[Authors]For declining a comment diplomatically:
"We appreciate the reviewer's suggestion. However, [X] falls outside the scope of the current study, which is delimited to [Y]. We have clarified this in the limitations section (p. X)."
For partial concession:
"The reviewer raises an important point. We agree that [conceded part]. We respectfully maintain, however, that [defended part], because [methodological rationale]. We have revised the text to make this distinction clearer (p. X)."
For correcting a misreading:
"We thank the reviewer for this close reading. We believe this may reflect an ambiguity in our original phrasing. We have revised the relevant passage (p. X) to read: [revised text]."
Activated when the user provides both supervisor feedback and a thesis manuscript. The goal is a single prioritised change plan — not a narrative critique.
Accept inputs in any combination:
If only one input is provided, ask for the other before proceeding. Do not generate a change plan from feedback alone — location anchoring requires the manuscript.
For .tex files: treat \section{}, \subsection{}, \label{}, and \chapter{} tags as location anchors. Reference them directly in the change table (e.g., \section{Discussion}, line ~340).
For PDFs: use chapter titles, section headings, and page numbers as location anchors.
Read all supervisor comments. For each point:
or a structural change affecting multiple chapters?
Clarify — argument or concept needs clearer expressionAdd — missing content, citation, analysis, or sectionCut — redundant, off-topic, or overlong materialRestructure — order or logic needs reorganisingFix — factual error, APA violation, formatting issue, typoExpand — underdeveloped section that needs more depththe change must be made. If a comment is ambiguous about location, flag it explicitly.
Output a prioritised table with this structure:
| # | Priority | Type | Location | Supervisor comment (summary) | What to do |
|---|---|---|---|---|---|
| 1 | High | Restructure | Ch. 3, §3.2 (p. 47) | "The argument jumps from X to Y without justification" | Add a transitional paragraph explaining the logical link between X and Y before the current paragraph opening "In this study…" |
| 2 | High | Fix | Ch. 4, §4.1 (p. 61) | "Causal language on correlational data" | Replace "caused" with "was associated with" throughout §4.1; audit full Results section for similar instances |
| … | … | … | … | … | … |
Priority rules:
High — affects the core argument, a mandatory section, or is explicitly flagged as critical by the supervisorMedium — improves clarity or rigour but does not change the thesis's core claimsLow — stylistic, formatting, or minor APA fixesSort by Priority (High → Medium → Low), then by chapter order within each priority tier.
After completing the table, run a quick passive scan of the manuscript for the issues listed in Flags to always raise (below). If any are found that the supervisor did not already address, append them as a separate section: "Additional issues not in supervisor feedback" — same table format, marked Unreviewed in the Priority column.
After delivering the table, offer:
"I can work through any row in this table with you — draft the revised text, rewrite the passage, or expand a section. Just point to the row number."
This connects the Thesis Review mode back to Draft and Edit modes for execution.
These issues are surfaced proactively whenever detected, even if the user only asked for a narrow task:
| Issue | Why it matters |
|---|---|
| Causal language on correlational data | Reviewers will reject or request major revision |
| p-value reported without effect size | APA and most journals now require both |
| "Significant" used non-statistically | Ambiguous; replace with "substantial", "notable", etc. |
| Undefined abbreviations on first use | Style violation and readability issue |
| Self-plagiarism risk (text close to prior work) | Flag for user to check, do not rewrite without instruction |
| Missing limitations section | Nearly universal reviewer expectation |
Raise these whenever the manuscript involves transformer models, personality measurement, or XAI:
| Issue | Why it matters |
|---|---|
| IG baseline not specified or justified | Reviewers familiar with XAI will flag this immediately; choice affects attribution values |
| Attribution ≠ causation conflation | Claiming IG highlights "explain" personality is overclaiming — use "associated with" or "predictive of" |
| No model reproducibility info (seed, version, hardware) | Required for replication; increasingly expected at PLOS ONE, Psych Methods |
| Big Five labels from self-report treated as ground truth | Acknowledge measurement error in labels; discuss implications for model validity |
| OCEAN traits analysed independently without acknowledging intercorrelations | N and E, O and C are correlated; inflated false positives across 5 models |
| Pennebaker paradigm generalisation | Essays from forced introspection may not generalise to natural language; must be acknowledged |
| Construct validity gap between NLP features and psychological constructs | Linguistic patterns predict scores ≠ linguistic patterns are personality; reviewers at EJPA/PAID will push back hard |
| Effect sizes for NLP models not contextualised | r = .30 in personality prediction is meaningful; must be compared to established benchmarks (e.g., self-other agreement literature) |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.