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Skillslov-search-twitter

lov-search-twitter

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Updated 10-16 00:01

About this skill

lov-search-twitter — find the original posts, and also explain which ones were not found

Organize people, accounts, keywords, X links, status IDs, or screenshots into a verifiable index of original posts and a compilation of verbatim text. The text must come from locatable evidence; search summaries, media paraphrases, and OCR must not be passed off as the original text.

Triggers

Activate when

  • "Help me find this person's original tweets / original X posts / deleted tweets."
  • "Compile the full text and screenshot evidence for these tweet links."
  • "Trace the original X posts back from screenshots reposted domestically."
  • “Recover deleted tweets”, “find the original X post”, or “compile a verbatim Twitter thread”.

Do not activate when

  • The user only wants to publish or schedule X content: hand it off to publishing or social media scheduling capabilities.
  • The user only wants to download the images or videos of a known post: hand it off to lov-media-crawler。
  • The user only wants to determine whether an accusation is true, rather than recover the original post: hand it off to lov-fact-check。
  • The user requests access to a private account, bypassing a login wall, paywall, or access control: refuse to bypass; this Skill only handles public or authorized content that the user has the right to access.

User Profile (cross-session)

Every generated Skill is connected to the shared user-profile/v1 contract in skill.yaml. Read the shared user, brand, workspace, preferences, and this Skill's skills.<skill_id> namespace at the start of every run. Keep the source portable: resolved personal values belong in the shared profile, never here.

When the user directly states a durable output-language, evidence-format, or workspace preference, persist it through scripts/profile_store.py and report the saved profile path. Put Skill-specific values under records.<field>; use user.<field> for shared values. Never persist cookies, tokens, proxy secrets, or browser sessions. See references/user-profile.md for the complete contract.

Skill Group Composition

Read references/skill-composition.md before deciding whether to invoke or extend an adjacent capability. Sibling Skills are optional artifact handoffs, not hidden runtime dependencies.

Evidence Contract

Read references/evidence-model.md before searching. Every claimed post must have a status ID or an explicit identity_unresolved marker, a source URL, a retrieval time, a provenance tier, and the preserved text or image hash.

The normal output class is verbatim:

  • Preserve spelling, punctuation, line breaks, names, numbers, emoji, links, and visible truncation markers exactly as the source exposes them.
  • Never silently translate, repair grammar, join a truncated card with a media paraphrase, or infer missing sentences.
  • Put translations, summaries, and editorial notes in separate labelled fields.
  • A Wayback CDX hit proves that a URL was captured, not that its text was saved.
  • “Known IDs recovered” is not “complete account history”. Only claim full coverage after an authorized timeline enumeration or a reconciled inventory.

Workflow (MANDATORY)

You MUST follow these steps in order.

Step 0: Resolve root and runtime

  • Use SKILL_DIR if provided; otherwise infer the installed Skill directory.
  • Verify $SKILL_DIR/scripts/search_twitter.py, $SKILL_DIR/references/evidence-model.md, and $SKILL_DIR/references/discovery-playbook.md before work.
  • Read the shared Profile. Current request overrides project context, Skill records, shared preferences, then safe defaults.
  • Use Python 3.9+ for deterministic extraction. OCR and browser rendering are optional evidence paths, not silent dependencies.

Step 1: Define scope and completion claim

Record the requested person, known/current/former handles, time range, languages, expected deliverable, and whether the user means:

  1. known-post recovery;
  2. best-effort public discovery; or
  3. complete authorized account history.

Do not ask for a technical implementation choice. If the user says "full text" but does not provide account authorization, use best-effort public discovery and state that account-history completeness cannot be proven.

Step 2: Build the candidate inventory

Use at least two discovery paths from references/discovery-playbook.md:

  • exact searches for handle, former handle, display name, distinctive quotes, status IDs, x.com/.../status/..., twitter.com/.../status/..., and t.co;
  • Chinese web and image search across news, Weibo, WeChat, Telegram, forums, repost pages, and screenshot-heavy results;
  • page source, embedded X cards, structured data, link redirects, and media articles;
  • Wayback CDX and Common Crawl URL indexes.

Extract candidate URLs/IDs from saved text without network access:

python3 "$SKILL_DIR/scripts/search_twitter.py" discover \
  --input search-results.txt --pretty > candidates.json

Maintain an inventory row for every candidate. Do not discard unavailable or duplicate-looking IDs until URL, timestamp, and text relationships are checked.

Step 3: Recover known posts

For each handle/status ID, query both public renderers concurrently, then fall back to exact Wayback captures and optionally Common Crawl:

python3 "$SKILL_DIR/scripts/search_twitter.py" recover HANDLE \
  --ids-file ids.txt --common-crawl --pretty > recovered.json

FxTwitter and VXTwitter agreement is a two-renderer-match, useful for detecting truncation or extraction errors. It is not proof that two independent authors published the same text. Prefer the longest exact variant only when normalized texts differ solely by URL expansion or presentation whitespace; otherwise keep both variants and mark conflict.

Step 4: Preserve screenshot and OCR evidence

For every screenshot, save the original bytes before cropping or annotating. Record source page URL, capture time, SHA-256, visible account/status clues, and whether it is an X card, repost screenshot, or ordinary media article. Register the file deterministically:

python3 "$SKILL_DIR/scripts/search_twitter.py" evidence screenshot.png \
  --kind screenshot_copy --source-url SOURCE_URL \
  --ocr-file screenshot.ocr.txt --pretty > screenshot-evidence.json

OCR is a derived transcription. Preserve it separately from verbatim_text, and mark ambiguous characters. A crop may improve readability but must refer to the hash of its uncropped parent when available.

Step 5: Reconcile and grade evidence

Assign exactly one primary provenance tier from references/evidence-model.md. Use the strongest evidence actually observed, retain weaker corroboration, and keep conflicts visible. Never promote a search snippet, translation, or media paraphrase to live_original or archived_original.

Step 6: Deliver the result

Produce:

  1. scope and retrieval date;
  2. candidate/original-post index with URL, ID, timestamp, status and provenance;
  3. a verbatim section containing only recoverable text;
  4. a screenshot evidence section with hashes and source pages;
  5. an explicit unrecovered/conflict section;
  6. a coverage statement such as “7 of 15 known IDs recovered”, never an unsupported “all posts recovered”.

Label 原文, OCR 转写, 媒体引文, 摘要, 翻译, and 编辑说明 separately. When a user asks for original text, a summary is not a substitute.

Step 7: Validate

  • Re-open every source URL or retained file used for the final claim.
  • Recompute screenshot hashes and verify output counts against the inventory.
  • Spot-check line breaks and truncation markers against rendered evidence.
  • Run the offline tests when the CLI changes:
python3 -m unittest discover -s "$SKILL_DIR/tests" -v
python3 "$SKILL_DIR/scripts/validate_skill.py" "$SKILL_DIR"

Report remaining gaps and access limitations with the finished files.

Authorization Boundary

  • The default workflow is zero-login and never reads browser cookies.
  • If complete active-account enumeration genuinely requires twscrape or an X session, explain why and obtain explicit authorization before using it.
  • Keep user-owned credentials in an OS credential store or current process environment. Never place them in Profile, source, reports, tests, or chat.
  • Do not evade suspensions, private-account controls, paywalls, rate limits, or platform protection.

Dependencies

  • Python 3.9+ standard library for scripts/search_twitter.py.
  • Network access for live renderers and web archives.
  • Web/image search or a browser for broad discovery and rendered screenshots.
  • Optional OCR engine for screenshot transcription.
  • Optional twscrape only after explicit authorization for account enumeration.