Results for “sift”

Use tsift for token-efficient repository navigation, code search and reading, call graphs, diffs, logs, tests, session context, and workspace memory. TRIGGER: exploring or changing a codebase with tsift installed, or working on the tsift repo. SKIP: plain file-level glob, non-code web search. VERSION CHECK: compare `tsift --version` against tsift-version below before trusting any command text copied from this file.

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Reads, searches and counts Claude Code session transcripts (the .jsonl files under ~/.claude/projects, subagents too) with the csift command-line tool, and ships hooks that re-inject compacted turns or record pending questions. Use when asked what a past or running session said, did, edited, spent or is doing now; when about to grep, jq or script over a transcript jsonl (a type=="user" filter overcounts the person's messages), count how many messages the person typed, tool calls or tokens, or claim something was never done or decided; to rebuild a lost file or plan from what a session wrote, extract pasted images, restore turns a compaction summary lost, find a subagent's own id when CLAUDE_CODE_SESSION_ID names the parent, wait on or message another session or subagent; or when csift prints "unknown label selector", "show needs an address", "looks like a session/agent id, not a project path" or "0 matches — a DEFINITIVE absence". Not for semantic or embedding search: csift matches regular expressions only.

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Design a fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection. Use when students need to verify AI claims and citations.

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Annotate and filter VCF variants with SnpEff and SnpSift. SnpEff predicts functional effects (HIGH/MODERATE/LOW/MODIFIER), genes, transcripts, AA changes, HGVS; SnpSift filters and adds ClinVar/dbSNP. Java CLI with Python subprocess integration. Use ANNOVAR for multi-database annotation; Ensembl VEP for REST API; SnpEff for fast CLI with pre-built genomes.

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Design a fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection. Use when students need to verify AI claims and citations.

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UI Component Editor workflow. Use when the user wants to implement a React component from a screenshot in an SDD project, iterating visually with live preview.

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Remote sync workflow for Story Driven Development. Use this skill whenever the user asks to pull from remote, push changes to SDD Flow, enrich drafts, update local state from remote, publish local updates, process remote drafts, or run a remote worker job (enrich or sync). Also trigger on phrases like "sdd pull", "sdd push", "sync with SDD Flow", "enrich this CR", "push the remote updates", "pull the latest specs".

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Story Driven Development workflow. Use this skill whenever working in a project that has `.sdd/config.yaml`, or when the user mentions SDD, sdd sync, story driven development, spec-driven development, change requests, bugs, or asks to implement a feature described in `product/` or `system/`. Also trigger on phrases like "sync the docs", "mark synced", "apply this CR", "fix this bug" when an SDD project is detected.

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Analyze Windows Registry files to uncover user activity, installed software, USB connections, and automatic startup entries. Organize these traces into investigation summaries and timelines to help examine suspicious activity and possible malware persistence.

• Investigating suspicious automatic startup entries
• Reconstructing user activity around an incident
• Checking USB device connection traces

Investigate where or when a photo was taken using visible clues, metadata, reverse image search, maps, and image comparisons. Results include coordinates, an error radius, evidence images, and confidence levels that distinguish verified findings from uncertain candidates.

• Investigating where a photo was taken
• Estimating capture time from sunlight and shadows
• Comparing location candidates against maps and street views

Design a source evaluation protocol using lateral reading and credibility checks for digital information. Use when students need to evaluate websites, online sources, or social media claims.

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Verify sources, claims, images, video, documents, interviews, and synthetic media with SIFT and a durable evidence trail. Use before relying on uncertain material.

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Design a source evaluation protocol using lateral reading and credibility checks for digital information. Use when students need to evaluate websites, online sources, or social media claims.

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Evaluating online information for credibility, accuracy, and context. Covers the SIFT method (Stop, Investigate, Find, Trace), lateral reading, reverse image search, primary source verification, and common misinformation tactics. Use when assessing the trustworthiness of a web page, article, video, social media post, or any digital claim before relying on it or sharing it.

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