polish-language — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited polish-language (Agent Skill) and scored it 91/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 1 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 1 flagged
The text {match} is the classic direct prompt-injection phrasing. Placed in a skill body that the agent reads as trusted instructions, it tries to make the agent abandon its prior rules and follow whatever comes next — a full system-prompt override.
ignore/disregard/forget … previous instructions sentence.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.
Tighten a manuscript's mechanical language consistency and clarity before circulation or submission — the copy-editor pass content-focused skills skip. The author is often a non-native (ESL) English writer, so clarity edits keep the formal academic register and never touch facts. Manuscript edits are in English.
This skill never rewrites scientific claims, changes numeric values, edits citations, or judges study quality; it standardizes house style and improves sentence-level clarity, with explicit user approval for every edit. Out of scope: AI-tell removal (humanize, which does not do general copy-editing), drafting or restructuring (write-paper), reporting-guideline items (check-reporting), AI-search optimization (academic-aio), reference formatting and citation integrity (manage-refs, verify-refs), and translation.
Input: a manuscript or section (Markdown / plain text). Output: (1) the deterministic consistency report, and (2) — only after a user gate — a clarity-polished revision with a change log limited to style.
Run the bundled linter — it reports, never edits:
python3 "${CLAUDE_SKILL_DIR}/scripts/lint_consistency.py" path/to/manuscript.md
# add --strict to exit non-zero when any issue is found (CI / pre-submission gate)It flags eight families, each with line numbers and a per-category + total count:
used-but-never-defined (define-once discipline).
side against the document's dominant variant.
5-10 → 5–10).P/p case; impossible P = 0.000.5mg → 5 mg).separator (3.681) that the body writes with a comma (3,681).
Present the report to the user. The linter output is the source of truth for what is mechanically wrong: do not invent further "issues" from memory, and label anything else you notice as an editorial suggestion, not a linter finding. The fixed rules do not settle every grammar or journal preference, so triage flags in context.
Text baked into a figure never reaches Phase 1, so a UK word typed into a PowerPoint panel or plotting script ships in a US manuscript unseen. Scan the figure sources (no OCR):
python3 "${CLAUDE_SKILL_DIR}/scripts/lint_figure_locale.py" --manuscript path/to/manuscript.md --figures-dir figures/
# --spelling us|uk forces the target; otherwise it reads a `spelling:` front-matter field,
# then falls back to the body's own US/UK majority. --strict exits non-zero on any drift.It reads <a:t> runs inside *.pptx slide XML and the text of *.py / *.R plotting scripts, with the same US↔UK families as Phase 1. FIGURE_LOCALE_DRIFT is Minor — copy-edit the source before the raster is re-exported. A missing figures directory is not an error; it exits 0 with nothing judged.
Walk the user through the report. Some flags are author choices (a journal may mandate UK spelling, or digits for all numbers). User approval is required before any edit — confirm per category which to apply and which to keep. Record the decisions; do not auto-apply.
For each approved category, apply the deterministic fix with Edit:
P/p and fix P = 0.000 to the reported inequality,Re-run lint_consistency.py after editing — the count should drop to the issues the user chose to keep. This re-run is the verification gate; never claim a fix without it.
If the user requests a clarity pass, improve readability sentence by sentence while preserving meaning, register, numbers, and citations:
Show each proposed change as a before/after diff and get user review before writing. Numbers, p-values, effect sizes, units, citations, and claims are copied verbatim; an edit that would change any of them is out of scope — skip it. Never merge, add, or drop a scientific claim, number, or reference. If a sentence's meaning is even slightly uncertain, leave it and ask; do not invent domain facts to smooth a sentence. Every applied change must trace to a linter flag or a user-approved clarity suggestion in the change log.
Deterministic and network-free (synthetic manuscript with seeded defects + expected/report.txt):
bash "${CLAUDE_SKILL_DIR}/scripts/lint_challenge/verify.sh" # PASS = 11 seeded issues across 8 categories + 2 clean controls~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.