| name | wiki-knowledge-agent |
|---|---|
| description | Turn chat-pasted text/links into a verified, translated, searchable wiki knowledge base. When a user pastes a link or text (in ANY channel — Discord, Slack, CLI, etc.), fetch+verify it, classify it (useful vs ad), translate+summarize it into the target language, save it to the wiki, and optionally alert on time-sensitive information. Also acts as a RAG channel: agents search the wiki for prior knowledge. Use whenever the user pastes content for archiving, asks to save a link/bookmark, asks '알림 있어?', 'any alerts?', or asks the agent to look up knowledge saved in the wiki. |
| category | note-taking |
| triggers | ["save this", "save this link", "bookmark", "bookmark this", "add to wiki", "remember this", "archive this", "store this", "clip this", "note this", "paste this", "wiki", "knowledge base", "any alerts?", "check alerts", "notifications", "save for later", "북마크", "링크 저장", "링크 저장해줘", "저장해줘", "위키에 저장", "위키에 넣어줘", "붙여넣기 정리", "인박스", "알림 있어?", "알림 확인"] |
Wiki Knowledge Agent
Turn chat-pasted text/links into a verified, translated, searchable wiki knowledge base. Works on any agent platform (Hermes, Claude Code, Codex, Cursor) with zero external dependencies beyond curl + Python stdlib.
When to Use
- User pastes a link or text into any chat channel and expects it to be captured.
- User says "저장해줘", "링크 저장", "북마크", "save this", "add to wiki".
- User asks "알림 있어?" — read the local alert queue and report.
- An agent needs prior knowledge from the wiki (RAG usage — search
wiki_root). - A scheduled job runs the ingest/notify cycle.
Design Principles
- Platform-independent — works with a single model (no multi-agent setup required) or many.
- Zero external dependencies —
curl+ Python stdlib only. No webhook/app required for core function. - Local-first alerts — alerts always land in a local file + CLI output. Optional webhook/email/ntfy if configured.
- Any-channel input — a message arriving in any channel is eligible. The onboarding default channel is "the current chat".
- LLM does the reasoning — the scripts fetch/verify/structure; the model classifies ads, translates, and summarizes.
Configuration
Config file: ~/.config/wiki-knowledge-agent/config.yaml (default), overridable via WIKI_AGENT_CONFIG env var.
Run onboarding to create it interactively:
python3 scripts/onboarding.py
Example config (full reference: templates/config.example.yaml):
wiki_root: "~/wiki"
target_language: "ko"
translate: true
input:
sources: ["any"] # "any" = every channel/CLI; or a specific list e.g. ["discord", "slack"]
default_channel: "current" # channel used when no preference is set (onboarding default)
notify:
tier0: true # always: local file + CLI
webhook: "" # optional: Slack/Discord/Telegram webhook URL
email: "" # optional: SMTP DSN
ntfy_topic: "" # optional: ntfy.sh topic
alert_on:
security: true
account_expiry: true
project_risk: true
interest: false
Onboarding flow (interactive questions)
- Wiki root path → default
~/wiki - Input channels → choose from known platforms (discord/slack/weixin/telegram/cli…) or "any"; if the user doesn't pick, default is "current chat" (
default_channel: current). Content pasted into other channels is still judged and saved (never drop input because it arrived in the "wrong" channel). - Target language → default user's native language (e.g.
ko). - Alert channel → if the user has a webhook/email, capture it; otherwise default is the current chat (the agent reports alerts in the same conversation). Alerts can be added later anytime: just ask the model "알림 채널 추가해줘" — the LLM updates the config.
- Write
config.yamland print the summary.
Ingest Workflow (when a message arrives)
[input] pasted text or link in any channel
│
▼
1. VERIFY (script: scripts/ingest.py --verify <url-or-file>)
- Link: curl -s -I -L → HTTP status; 2xx/3xx = valid, 404/410/timeout = dead
- Text: mark as unverified text
│
▼
2. CLASSIFY (LLM, guided by references/ad-detection.md)
- 📢 Ad: self-promotion is the core intent, "try it" pitch, link-dump without substance
- 📚 Useful: real methodology/data/tool with transferable value
- Output: label + one-line reason
│
▼
3. PRE-SAVE FEEDBACK (LLM — before saving, add value from what you already know)
a) Project/context match: if the content relates to anything you know about
the user (their projects, goals, interests, past notes), say so in one line:
"📌 이거 <프로젝트>에 유용하겠네요" / "📌 This looks useful for <project>"
b) Date/event cue: if the content has a date-related element (holiday, birthday,
anniversary, deadline, event) and you know the relevant date, offer ONE question:
"📅 <이벤트>(<날짜>) <N>일 전에 알려줄까요?" / "📅 <event> is in <N> days — remind you?"
- If the user says yes → record it (e.g. add to config important_dates, or note it)
- If unsure of the exact date → don't guess; skip the offer
c) Keep it light: at most ONE feedback line + ONE date offer. Never block saving on it.
│
▼
4. TRANSLATE + SUMMARIZE (LLM)
- Foreign content → target_language
- ≤5-line summary + 3–5 key points
- If already in target_language, summarize only
│
▼
5. SAVE (script: scripts/ingest.py --save ...)
- <wiki_root>/knowledge/inbox/YYYY-MM-DD-<topic>.md
- Frontmatter: date, source_url, source_channel, classification (ad/useful), language
- Tag: related project or "일반"
│
▼
6. ALERT? (LLM decides against alert_on rules + script: scripts/ingest.py --notify)
- If alert-worthy → append to <wiki_root>/alerts/notifications.md
+ CLI output "[알림] …" + optional webhook/email
- Else → silent save; reply "✅ 인박스: <file>"
Note on reminders: the skill itself stays light — the PRE-SAVE FEEDBACK step is LLM reasoning only (no code). If the user wants automated day-before reminders, use your platform's native scheduling (cron / scheduled messages / agent reminders); this skill just makes the offer and records the date.
Ingest script usage
# Verify a URL (returns HTTP status)
python3 scripts/ingest.py --verify "https://example.com/article"
# Verify a pasted text (saved to temp file first, then verified as content)
python3 scripts/ingest.py --verify-content /tmp/pasted.txt
# Save a structured note (after LLM produced the markdown body)
python3 scripts/ingest.py --save /tmp/note.md --source-url "https://..." --channel "discord" --classification useful --language ko
# Append to the local alert queue
python3 scripts/ingest.py --notify "API key expiring 2026-09-01" --category security
# Read the alert queue (for "알림 있어?")
python3 scripts/ingest.py --alerts
RAG Usage (agents searching the wiki)
- Search knowledge:
search_files(pattern=..., path=<wiki_root>/knowledge) - Fast topic mapping: read
<wiki_root>/knowledge-base-map.md(auto-updated inventory) — topic → file - Recurring integration: a weekly cron can run the curation flow (see
wiki-inbox-curationpattern): verify → merge intoknowledge/<topic>.md→ move processed inbox files toinbox/archive/.
Alert Notifications
- Tier 0 (always): append
<wiki_root>/alerts/notifications.md+ print[알림] …to stdout. - Tier 1 (optional): webhook (Slack/Discord/Telegram), email (SMTP), or ntfy.sh topic — only if configured.
- Alerts are time-sensitive items: security issues, account/domain expiry, project risk, and (if enabled) user interests.
- To add an alert channel later: user says "알림 채널 추가해줘" → model reads config, asks for the webhook/email, updates config.yaml.
Pitfalls
- Never drop input because it came from a "non-default" channel — judge it and save; the channel is metadata, not a gate.
- Don't store full verbatim text — store the distilled note (summary + points), keeping the wiki lean.
- Verify before trusting — a 404 after redirects is a dead link; a ~500-byte stub is a bot-block page, not content.
- Ads are not automatically deleted — label them 📢 and save only the transferable insight (if any); keep provenance.
- Personal info / secrets in pasted content — strip identifiers before saving.
- Config path — respect
WIKI_AGENT_CONFIGenv var if set; never hardcode~/wikiinto scripts. - Alert queue growth — mark items read/archived after reporting (
--alerts --clearor move toalerts/archive/).
Files
scripts/onboarding.py— interactive config wizardscripts/ingest.py— verify / save / notify / alerts CLItemplates/config.example.yaml— full config referencereferences/ad-detection.md— ad-vs-useful classification rules with worked examplesREADME.md— public repo docs (EN/KR)