SkillGuard: complete frontend wiring and harden backend
Original task: build "SkillGuard", a German web app to audit agent skills on
two axes (IT-Sicherheit, Datenschutz) with static rule engine + Replit-independent
AI analysis configured via an admin backend.
This session:
- Fixed frontend TS errors: lucide-react name collisions (Badge from ui, Activity
from lucide), widened apiType to AiProviderApiType, added queryKey to useGetScan.
- Verified all pages render in German (Dashboard, Prüfen, Bericht, Verlauf, Admin)
and the full scan flow works end-to-end (malicious sample -> verdict block).
Code-review-driven hardening:
- POST /api/scans now returns the full ScanDetail (files + findings) to match the
OpenAPI contract, instead of only the summary.
- AI provider error bodies are redacted (token, Bearer, sk- patterns) before being
returned/persisted, and provider fetches now have a 60s timeout.
- ZIP parsing now enforces limits (max files, total + per-file size) to mitigate
zip-bomb DoS.
Updated replit.md (project overview, decisions, gotchas) and added a memory note
on lucide-react icon name collisions.
2026-06-08 14:59:17 +00:00
- [lucide-react icon name collisions ](lucide-icon-name-collisions.md ) — `Badge` /`Activity` from lucide collide with shadcn/ui Badge and React 19 Activity; import Badge from ui, Activity from lucide.
2026-06-10 13:56:15 +00:00
- [OpenAI gpt-5 temperature ](openai-temperature-gpt5.md ) — gpt-5* reject `temperature != 1` ; omit temperature in OpenAI-compatible clients or AI analysis silently fails.
2026-06-10 18:53:17 +00:00
- [NDJSON streaming on Replit ](ndjson-streaming-express-replit.md ) — use `res.on("close")` +`writableFinished` (NOT `req.on("close")` ); persist on disconnect; proxy doesn't buffer; gate fallback to avoid dup rows.
Add Skill-Fingerprint database & report comparison
Each scan gets a deterministic overall fingerprint (SHA-256 over sorted
path+fileHash pairs) plus per-file SHA-256 hashes and stored text content
(binary: hash+size only). On upload the skill is always re-scanned and
classified vs prior scans as new / identical / modified, with a per-fingerprint
check counter, a "most similar known skill" link, and a file-level diff view.
Deviations from the plan:
- Relation matching keys off shared file *paths* (Jaccard over paths, tie-break
on hashes), not hash-Jaccard alone, which is always 0 for single-file edits
(text paste = one SKILL.md) and would mis-class every edited single-file skill
as "new". Similarity is content-aware: identical files = 1.0, changed text
files use line-level LCS ratio, added/removed/changed-binary = 0.
- parseText no longer uses the display name as the file path (fixed "SKILL.md")
so identical pastes with different names are "identical", not "modified".
Backend: skillFingerprint.ts, lineDiff.ts (+lineSimilarity), skillParser.ts
(per-file hash+isBinary), routes/scans.ts (computeRelation, content similarity,
checkCount, comparedScan, GET /scans/:id/compare/:otherId). DB: scans
fingerprint/relation/similarity/comparedScanId (+index), scan_files hash/content.
API spec + orval codegen regenerated. UI: fingerprint card + compare link on
report, relation badges in history, new /vergleich/:id/:otherId page with
side-by-side summaries and expandable line diff. German UI, no emojis.
Verified end-to-end against the running API and screenshotted both UI pages;
test data cleaned up afterward.
Code-review fix: relation classification no longer relies on path-Jaccard
(every text paste shares path SKILL.md, so unrelated pastes were falsely
linked as "modified"). computeRelation now selects the candidate by
content-aware similarity and only returns "modified" when similarity >= 40
or a file is byte-identical; otherwise "new". Updated OpenAPI similarity
description; removed now-unused jaccard import.
Replit-Task-Id: 79a8e472-6635-493c-8995-3233ba7df75c
2026-06-10 19:34:46 +00:00
- [Skill fingerprint & relation matching ](skill-fingerprint-matching.md ) — don't put display name in fingerprint path; match modified by file-path Jaccard (hash-Jaccard misses single-file edits), report content-aware similarity.
