The agent's trail is finally being treated as an asset.

Microsoft's open-source (MIT) optimizer that treats a skill document — the natural-language instructions you hand a frozen agent — as a trainable artifact, tuned with the machinery of model training but never touching the weights. An optimizer model reads scored agent rollouts, proposes bounded add/delete/replace edits to the skill file, and accepts a change only when it improves a held-out validation score. The output is a compact 300–2,000-token skill you deploy with zero extra inference cost. It runs against OpenAI, Azure, Claude, Qwen, MiniMax, Codex, and Copilot, in direct chat, the Codex CLI, or Claude Code. `pip install skillopt`. This is close to the performance-review pick that never shipped in yesterday's onboarding edition: score what the agent did, then make it better at the job. Reach for it when you've been hand-tuning a prompt or a `SKILL.md` by feel and want a validation gate deciding what actually helps. Delete the afternoon of A/B-ing prompt tweaks in a spreadsheet. Tradeoffs: this is an established rocket, not a launch — created in May, 11,553 stars, trending again today — and the headline gains (+23.5 points in direct chat, +19.1 inside Claude Code on GPT-5.5, best-or-tied on all 52 evaluated cells) come from the project's own benchmarks; the optimization loop itself burns real tokens before the cheap deployable skill falls out the end.

A dependency-free Python library (Apache-2.0) that turns every agent action — tool calls, model calls, data access, approvals — into one record in an append-only, hash-chained log. Each record's SHA-256 hash folds in the previous record's hash plus this record's canonicalized content (RFC 8785), so altering any row breaks the chain and verification catches it. The pitch, from a founder who spent years at Vanta doing compliance, is that a customer can verify your agent's audit trail without trusting you. Instrument an existing agent in one line — `trace(run_my_agent, profile="my-agent", log="audit.jsonl")` — then `halo report audit.jsonl -o report.html`. Adapters ship for OpenTelemetry, LangChain, LiteLLM, Langfuse, Claude Code, and the Claude Agent SDK. Try it with `uvx --from halo-record halo demo --serve`. This is yesterday's MakerChecker audit log pulled out as a standalone primitive: not the whole segregation-of-duties stack, just the tamper-evident record, ~4,300 auditable lines, no network calls unless you opt into an external witness. Reach for it the moment an agent touches something you'll later have to prove you handled correctly — the GitLost leak is the argument for it. Delete the plain JSON logs you hope nobody edited. Tradeoffs: created June 11, 45 stars — a fresh bet; and the honest limitation is in the README — hash-chaining proves *integrity* (nothing was changed after the fact), not *completeness* (an operator can still delete records or never write them). Witnesses close that gap; without one, this proves the log wasn't doctored, not that it's the whole story.

A TypeScript library (MIT) that compresses a long agent session deterministically, with zero LLM calls. Instead of summarizing old turns with another model or truncating them outright, it skeletonizes each past tool call into a one-line summary (`$ cmd → ok`), stashes exact values — UUIDs, file paths, IDs — in a "Coordinate Closet" block, and freezes the folded prefix so it stays byte-identical across turns and the provider's prompt cache stays hot. When the agent re-touches a folded path or identifier, the full content pages back in. Wrap your message history in a `FoldSession` and call your model as usual; the core engine has zero runtime dependencies. Reach for it when a coding agent runs for hundreds of turns and either blows the context window or racks up a bill re-sending history every step. Delete the summarize-with-a-cheaper-model pass — the one that costs its own tokens and quietly drops the one file path you needed. Tradeoffs: 58 stars, created June 16 — a fresh bet extracted from a production multi-agent system, with 380+ deterministic tests but not yet on npm (install from the source tarball). Its numbers (~90% of input tokens served from cache, 60% cheaper than truncation, 70% cheaper than LLM summarization) are the author's from production, not independently reproduced — but this is the rare context tool whose behavior you can actually unit-test, because nothing about the fold is stochastic.

A CLI (MIT) built so an agent edits `.docx` files without ever seeing raw OOXML: it exposes plain verbs — `insert`, `edit`, `replace`, `tables`, `render` — over a readable Markdown view, and crucially it speaks Word's review layer. Agents can add `comments` (reply, resolve), toggle `track-changes` on or off, and accept or reject individual edits, so the file lands in Word's native review pane for a person to approve. It installs as an Agent Skill for Claude Code, Codex, and Pi via `npx skills add kklimuk/docx-cli`, or as a standalone binary with one curl. Where yesterday's OfficeCLI was the whole monolithic Office suite in one C# binary, this is the sharp single-format counter: Word only, done well, with the review workflow as a first-class feature rather than an afterthought. Reach for it when the agent's deliverable is a document a human has to sign off on. Delete the python-docx glue that never survived a tracked-changes round trip. Tradeoffs: 108 stars, created in May — young, and the npm path wants Bun ≥ 1.3 (the standalone binary sidesteps that). There's no undo — it overwrites in place, so git is your history — and the A/B numbers that sell it (Haiku solving 4.3 of 6 tasks with it versus 0.7 without, ~2.4x fewer tokens) are the author's own, graded on Word's real rendering rather than programmatic asserts.

Off the thread but worth knowing: **Shellular** (shellular.dev) is a phone app that runs Claude Code, Codex, and OpenCode on your actual machine over an end-to-end-encrypted relay — `npx shellular`, scan a QR, and your dev box is in your pocket; the relay's open-source, the app isn't, and it's reportedly already at $24k/mo, which is the away-from-desk interface (see last week's CodeMote) turning into a business. And **ai-job-search** (github.com/MadsLorentzen/ai-job-search, MIT) was the pool's biggest mover — 13,362 stars, 2,514 today — a Claude Code framework you fork, fill with your profile, and let the agent evaluate postings, tailor CVs, and draft cover letters; it's for job-seekers, not builders, but it's a clean template for "fork this repo, the agent does the rest" as a distribution model.