OnourImpram

Use when a complete research workflow — its instructions, skills, data boundaries, and tool connections — must be assessed for portability across AI agents or environments, so each component is explicitly mapped before migration rather than assumed to transfer.

Use when a Claude Code session loops, drifts from scope, claims a fix that CI rejects, hits context, PATH, or permission walls, or fails in a way another retry will not solve and a root cause diagnosis is needed instead.

Use when AI contribution metadata must match the actual workflow, when model alias and dated identifiers need auditing, when verified and fabricated citation counts feed a release decision, or when a venue's AI disclosure statement needs evidence behind it.

Use when a draft in Turkish, English, or both reads as AI-generated and needs revision that removes machine writing patterns and translation calques while preserving meaning, citations, statistics, and the author's voice.

Use when a reference list needs APA 7 cleanup, when DOIs must be confirmed real against Crossref or PubMed, when the user asks whether AI-suggested citations actually exist, or when a manuscript needs a citation hygiene pass before submission or defense.

Use when authorship order, CRediT contribution roles, or AI-assistance attribution must be recorded transparently for a manuscript, when an author dispute needs an evidence trail, or before a contributorship statement is required at submission; this skill maintains the human-attribution ledger of who contributed what and in which role (authorship order, CRediT taxonomy, revision responsibilities), which is distinct from the disclosure-metadata audit owned by ai-disclosure-auditor.

Use when paired tr.md and en.md files need a parity verdict, when titles, frontmatter, headings, or DOI sets may have drifted between languages, or when a bilingual document is approaching release and its translation coverage needs an audit.

Use when authoring a new manuscript, thesis chapter, or abstract from scratch in Turkish and English in parallel — building the claim skeleton first, then drafting Turkish and re-authoring English — not for auditing parity between existing versions.

Use when the user asks a factual question whose answer might live in the vault. Invokes mneme_search. Production `mneme_search` is FTS5 BM25. The experimental feature-hashed lexical-vector backend is not wired into MCP. KG enrichment is gated to summarize or timeline when full-profile graph state is active.

Use when the user asks a factual question whose answer might live in the vault. Invokes mneme_search. Production `mneme_search` is FTS5 BM25. The experimental feature-hashed lexical-vector backend is not wired into MCP. KG enrichment is gated to summarize or timeline when full-profile graph state is active.

Use when the user starts a new task that may have prior vault context worth surfacing. Invokes the mneme_prime MCP tool to build a token-budgeted preamble of recent sessions and topic matches.

Use when the user starts a new task that may have prior vault context worth surfacing. Invokes the mneme_prime MCP tool to build a token-budgeted preamble of recent sessions and topic matches.

Use when the user starts a new task that may have prior vault context worth surfacing. Invokes mneme_prime to build a token-budgeted preamble of recent sessions and topic matches.

Use when the user asks a factual question whose answer might live in the vault. Invokes mneme_search (FTS5 BM25, optionally fused with dense and KG backends in standard and full profiles).

Use VocationOS for evidence grounded career discovery, review, documents, pipeline, approvals, audit, interviews, and offers. Activate when an agent needs to inspect or coordinate VocationOS through its daemon or MCP boundary.