Fetching from the wire…
Skills2026-07-08 · source-backed
Have it write and execute a small script whose stdout is the only thing that comes back. One execution replaces several read and grep calls, and the raw bytes never cost you context. It's the single highest-leverage habit for keeping long sessions cheap. (context-mode)
Each link below shares sources, entities, or timing with this story.
The term stabilized across Martin Fowler's site, Addy Osmani, LangChain ("Agent = Model + Harness") and an arXiv exploratory study, converging on a five-layer definition: tool orchestration, verification loops, context and memory, guardrails, observability (Martin Fowler). The...
The "Think in Code" rule took 47 Read() calls totaling 700KB down to one script returning 3.6KB (GitHub). You can apply this today with nothing installed, just by changing how you phrase multi-file questions.
MCP server that virtualizes agent context windows by sandboxing tool call outputs. Claims 98% context reduction (986KB to 62KB). SQLite+FTS5 with BM25 ranking. Every Playwright snapshot costs 56KB; twenty GitHub issues cost 59KB — this solves fundamental scaling. GitHub
mksglu/context-mode (19,893 stars, 1,430 forks, ELv2) is an MCP server attacking context burn four ways: sandboxing tool output so raw data never enters the window, tracking every file edit, git operation, task, and error in SQLite with FTS5/BM25 retrieval after compaction, en...
Every story above points at the same bottleneck. Agents are smart enough to reason and blind at retrieval. They grep whole files, string-match, and bloat their own context until they lose the plot. Serena fixes the actual problem. Serena is an MCP toolkit that wraps a Language...
Before treating an agent task as complete, spawn a reviewer in a clean context that sees only the diff and your acceptance criteria, not the reasoning that produced the change. It evaluates the result on its own terms and catches the cross-cutting gaps the original session rat...
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