{
  "schema_version": "1.1",
  "id": "weekly-2026-08-10",
  "slug": "2026-08-10",
  "issue_number": "005",
  "title": "feed7 Weekly #005",
  "description": "Six practical upgrades for stronger agent workflows, sharper evals, safer tools, and better context.",
  "published_at": "2026-08-10T00:00:00.000Z",
  "modified_at": "2026-08-09T18:07:17.651Z",
  "url": "https://feed7.dev/weekly/2026-08-10",
  "formats": {
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    "json": "https://feed7.dev/weekly/2026-08-10.json",
    "markdown": "https://feed7.dev/weekly/2026-08-10.md"
  },
  "selection": {
    "rule": "Six source-backed signals and one distraction to leave out.",
    "mode": "ai",
    "ignore_item_id": "auto-b274d6785e"
  },
  "items": [
    {
      "schema_version": "1.1",
      "id": "auto-2e74d0228d",
      "slug": "addyosmani-agent-skills-2e74d0228d",
      "url": "https://feed7.dev/p/addyosmani-agent-skills-2e74d0228d",
      "title": "addyosmani/agent-skills",
      "why_included": "Turn specs, TDD, review, and shipping into evidence-gated workflows, but adopt only the skills that fit your repository.",
      "summary": "This pack turns common engineering practices into portable coding-agent workflows for specs, TDD, review and shipping. Its useful idea is to require evidence at each gate, not merely better prompts.",
      "practical_implication": "Treat the pack as a menu of enforceable workflows: begin with TDD, debugging or code review, then add broader lifecycle automation after checking how each skill fits your repository. Codex can load it as a native plugin from CLI v0.122+.",
      "agent_context": "The repository contains **24 Markdown skills**, four specialist personas and **8 lifecycle commands** spanning definition, planning, implementation, verification, review and release. The open skills CLI supports installation across **70+ agents**.\n\nTreat the pack as a menu of enforceable workflows: begin with TDD, debugging or code review, then add broader lifecycle automation after checking how each skill fits your repository. Codex can load it as a native plugin from **CLI v0.122+**.\n\nInstalling an individual skill may omit shared files under the repository-level references directory, leaving supplementary checklists unavailable. The workflows also encode strong process opinions, so teams should review their gates and defaults instead of adopting all 24 blindly.",
      "source": {
        "name": "GitHub",
        "url": "https://github.com/addyosmani/agent-skills",
        "published_at": "2026-08-09T00:00:00.000Z"
      },
      "source_class": "tool",
      "content_type": "GitHub Repo",
      "layer": "agent",
      "domains": [
        "coding"
      ],
      "topics": [
        "skills",
        "coding-agents",
        "harness-engineering"
      ],
      "verification": {
        "status": "needs_review",
        "label": "Needs Review",
        "method": "unverified",
        "verified_at": null
      },
      "uncertainty": [
        "Automatically selected from source material; feed7 has not independently tested the claim."
      ],
      "connected_context": null,
      "lifecycle": "New",
      "published_at": "2026-08-09T00:00:00.000Z",
      "modified_at": "2026-08-09T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
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        "markdown": "https://feed7.dev/p/addyosmani-agent-skills-2e74d0228d.md"
      }
    },
    {
      "schema_version": "1.1",
      "id": "auto-9e82a687ae",
      "slug": "benchmarking-coding-agents-on-new-vs-legacy-codebases-de-9e82a687ae",
      "url": "https://feed7.dev/p/benchmarking-coding-agents-on-new-vs-legacy-codebases-de-9e82a687ae",
      "title": "Benchmarking Coding Agents on New vs Legacy Codebases — Denys Linkov, Wisedocs",
      "why_included": "Judge coding agents with explicit requirements and end-to-end tests, since fast output may be incomplete scaffolding.",
      "summary": "A production refactor shows why coding-agent evaluations need acceptance criteria and end-to-end verification: fast output can still be incomplete scaffolding.",
      "practical_implication": "Benchmark agents against explicit requirements, runnable end-to-end tests, deployment constraints, and hidden assumptions—not elapsed time or lines changed. Wisedocs also found a monorepo simpler for verification and sandbox setup across its former 10+ repositories.",
      "agent_context": "An early O3-assisted task took **3 hours** and produced 10 major mistakes; newer Sonnet 4.6 solved it after one extra iteration and Opus 4.8 nearly one-shot it. A broader GPT-5.5 attempt finished in **10m 22s** but mostly wrote scaffolding.\n\nBenchmark agents against explicit requirements, runnable end-to-end tests, deployment constraints, and hidden assumptions—not elapsed time or lines changed. Wisedocs also found a monorepo simpler for verification and sandbox setup across its former **10+ repositories**.\n\nThese are task-specific observations from one refactor, not controlled cross-model results. Human review remained part of the project, and only **15 of 17 requirements** were met during the migration.",
      "source": {
        "name": "AI Engineer",
        "url": "https://www.youtube.com/watch?v=7vn4WpqNpck",
        "published_at": "2026-08-08T00:00:00.000Z"
      },
      "source_class": "video",
      "content_type": "Video",
      "layer": "benchmark",
      "domains": [
        "coding"
      ],
      "topics": [
        "agent-evals",
        "agent-reliability",
        "coding-agents"
      ],
      "verification": {
        "status": "source_linked",
        "label": "Source Linked",
        "method": "source_feed",
        "verified_at": null
      },
      "uncertainty": [],
      "connected_context": null,
      "lifecycle": "New",
      "published_at": "2026-08-08T00:00:00.000Z",
      "modified_at": "2026-08-08T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
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        "markdown": "https://feed7.dev/p/benchmarking-coding-agents-on-new-vs-legacy-codebases-de-9e82a687ae.md"
      }
    },
    {
      "schema_version": "1.1",
      "id": "auto-91ef3aae9d",
      "slug": "learning-when-to-trust-via-selective-context-preference--91ef3aae9d",
      "url": "https://feed7.dev/p/learning-when-to-trust-via-selective-context-preference--91ef3aae9d",
      "title": "Learning When to Trust via Selective Context Preference Optimization",
      "why_included": "Test whether agents use good context while resisting misleading context, rather than appearing robust by ignoring evidence.",
      "summary": "MIST tests whether models use good context while resisting bad context, exposing agents that appear robust only because they ignore external evidence altogether.",
      "practical_implication": "Evaluate retrieval-augmented agents for selective trust, not only prompt-injection resistance. The proposed SCOPE method trains on matched preference pairs balanced across all four conditions so resistance does not come from ignoring useful context.",
      "agent_context": "MIST renders each reasoning item under **four matched conditions**: clean, misleading, correct-context, and irrelevant-context. Its **SC2W** metric counts cases where misleading context flips an otherwise correct answer to wrong.\n\nEvaluate retrieval-augmented agents for selective trust, not only prompt-injection resistance. The proposed **SCOPE** method trains on matched preference pairs balanced across all four conditions so resistance does not come from ignoring useful context.\n\nThe abstract reports reduced susceptibility on popular open models while preserving other-condition accuracy, but provides no numerical effect sizes here. Broader generalization beyond the benchmark's reasoning items remains an open question from the supplied material.",
      "source": {
        "name": "arXiv",
        "url": "https://arxiv.org/abs/2608.06377v1",
        "published_at": "2026-08-06T00:00:00.000Z"
      },
      "source_class": "blog_post",
      "content_type": "Paper",
      "layer": "benchmark",
      "domains": [
        "research"
      ],
      "topics": [
        "agent-evals",
        "agent-reliability",
        "context-engineering"
      ],
      "verification": {
        "status": "needs_review",
        "label": "Needs Review",
        "method": "unverified",
        "verified_at": null
      },
      "uncertainty": [
        "Automatically selected from source material; feed7 has not independently tested the claim."
      ],
      "connected_context": null,
      "lifecycle": "New",
      "published_at": "2026-08-06T00:00:00.000Z",
      "modified_at": "2026-08-06T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
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        "markdown": "https://feed7.dev/p/learning-when-to-trust-via-selective-context-preference--91ef3aae9d.md"
      }
    },
    {
      "schema_version": "1.1",
      "id": "auto-f5a1122448",
      "slug": "the-bitter-lesson-of-tool-calling-f5a1122448",
      "url": "https://feed7.dev/p/the-bitter-lesson-of-tool-calling-f5a1122448",
      "title": "The Bitter Lesson of Tool Calling",
      "why_included": "Test typed Python tool stubs: they matched or beat JSON calls in 11 of 14 models on BFCL v4.",
      "summary": "Across BFCL v4, models usually handled tools as typed Python calls at least as well as native JSON, suggesting code-based orchestration is worth testing for capable coding agents.",
      "practical_implication": "If your agents already write reliable code, test a typed-stub execution layer that lets one turn chain or parallelize calls. It also matched or exceeded JSON in 13 of 14 models under parallel fan-out and stayed stable in the reported context-rot condition.",
      "agent_context": "The study compares programmatic tool calling with native JSON calls across **14 models** on BFCL v4. Python-stub calls matched or beat JSON in **11 of 14 models**, while the GPT-5.6 family improved by **10.6%**.\n\nIf your agents already write reliable code, test a typed-stub execution layer that lets one turn chain or parallelize calls. It also matched or exceeded JSON in **13 of 14 models** under parallel fan-out and stayed stable in the reported context-rot condition.\n\nPerformance tracked model capability, so programmatic calls are not automatically better for every model. The evidence comes from one established function-calling benchmark; production safety, debugging, sandboxing, and task-level cost still need separate evaluation.",
      "source": {
        "name": "arXiv",
        "url": "https://arxiv.org/abs/2608.06370v1",
        "published_at": "2026-08-06T00:00:00.000Z"
      },
      "source_class": "blog_post",
      "content_type": "Paper",
      "layer": "benchmark",
      "domains": [
        "coding"
      ],
      "topics": [
        "tool-use",
        "agent-evals",
        "harness-engineering"
      ],
      "verification": {
        "status": "needs_review",
        "label": "Needs Review",
        "method": "unverified",
        "verified_at": null
      },
      "uncertainty": [
        "Automatically selected from source material; feed7 has not independently tested the claim."
      ],
      "connected_context": null,
      "lifecycle": "New",
      "published_at": "2026-08-06T00:00:00.000Z",
      "modified_at": "2026-08-06T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
        "html": "https://feed7.dev/p/the-bitter-lesson-of-tool-calling-f5a1122448",
        "json": "https://feed7.dev/p/the-bitter-lesson-of-tool-calling-f5a1122448.json",
        "markdown": "https://feed7.dev/p/the-bitter-lesson-of-tool-calling-f5a1122448.md"
      }
    },
    {
      "schema_version": "1.1",
      "id": "auto-b3425f8b3f",
      "slug": "how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f",
      "url": "https://feed7.dev/p/how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f",
      "title": "How Cursor Router chooses the right model for the task",
      "why_included": "Route models using real task outcomes, corrections, cache costs, confidence thresholds, and an explicit per-turn budget.",
      "summary": "Cursor Router learns task complexity and model fit from production behavior, showing why agent routing should include correction signals, cache costs, and per-task performance.",
      "practical_implication": "Builders implementing model routing should learn from real task outcomes, including corrections and task progression, rather than rely only on benchmarks. Include token use, cache misses, model-switching costs, confidence thresholds, and an explicit per-turn budget.",
      "agent_context": "Cursor routes each turn in two stages: Compass estimates complexity, then a production-derived taxonomy selects among eligible frontier models. **Auto Intelligence costs 68% less than Fable**, while **Auto Balance costs 41% less than Opus 4.8** and reports higher satisfaction.\n\nBuilders implementing model routing should learn from real task outcomes, including corrections and task progression, rather than rely only on benchmarks. Include token use, cache misses, model-switching costs, confidence thresholds, and an explicit per-turn budget.\n\nCursor's satisfaction measure is inferred from subsequent user behavior, so it is a proxy rather than a direct quality score. The reported results come from Cursor traffic and may not transfer to another workload, user population, or pricing mix.",
      "source": {
        "name": "Cursor",
        "url": "https://cursor.com/blog/how-cursor-router-works",
        "published_at": "2026-08-06T00:00:00.000Z"
      },
      "source_class": "blog_post",
      "content_type": "Engineering Post",
      "layer": "agent",
      "domains": [
        "coding",
        "data"
      ],
      "topics": [
        "model-selection",
        "harness-engineering",
        "agent-reliability"
      ],
      "verification": {
        "status": "official_source",
        "label": "Official Source",
        "method": "source_feed",
        "verified_at": null
      },
      "uncertainty": [],
      "connected_context": null,
      "lifecycle": "New",
      "published_at": "2026-08-06T00:00:00.000Z",
      "modified_at": "2026-08-06T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
        "html": "https://feed7.dev/p/how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f",
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        "markdown": "https://feed7.dev/p/how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f.md"
      }
    },
    {
      "schema_version": "1.1",
      "id": "auto-2dd1524bd8",
      "slug": "pause-workflows-for-approval-with-chat-sdk-2dd1524bd8",
      "url": "https://feed7.dev/p/pause-workflows-for-approval-with-chat-sdk-2dd1524bd8",
      "title": "Pause workflows for approval with Chat SDK",
      "why_included": "Add durable, verified human approval before agents deploy, alter production data, or take other consequential actions.",
      "summary": "Chat SDK can pause a workflow for a verified human decision and resume after seconds or days, without a custom approvals table, action handler, or polling loop.",
      "practical_implication": "Use this as a durable gate before an agent deploys, changes production data, or takes another consequential action. Scoped approvers, platform-signature checks, and the returned user identity reduce the custom authorization plumbing.",
      "agent_context": "Chat SDK’s new **chat/workflow** path adds requestApproval, which posts Approve and Deny controls and suspends a Workflow SDK run. The wait survives deploys and restarts, and Thread state crosses the workflow boundary automatically.\n\nUse this as a durable gate before an agent deploys, changes production data, or takes another consequential action. **Scoped approvers**, platform-signature checks, and the returned user identity reduce the custom authorization plumbing.\n\nThe material covers a binary approval card, timeout handling, and an edited-in-place audit outcome. It does not describe richer review flows, delegation, or how long approval records remain available.",
      "source": {
        "name": "Vercel",
        "url": "https://vercel.com/changelog/chat-sdk-durable-approvals",
        "published_at": "2026-08-06T00:00:00.000Z"
      },
      "source_class": "blog_post",
      "content_type": "Engineering Post",
      "layer": "tools",
      "domains": [
        "coding"
      ],
      "topics": [
        "agent-sdks",
        "tool-use",
        "agent-reliability"
      ],
      "verification": {
        "status": "official_source",
        "label": "Official Source",
        "method": "source_feed",
        "verified_at": null
      },
      "uncertainty": [],
      "connected_context": null,
      "lifecycle": "New",
      "published_at": "2026-08-06T00:00:00.000Z",
      "modified_at": "2026-08-06T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
        "html": "https://feed7.dev/p/pause-workflows-for-approval-with-chat-sdk-2dd1524bd8",
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        "markdown": "https://feed7.dev/p/pause-workflows-for-approval-with-chat-sdk-2dd1524bd8.md"
      }
    },
    {
      "schema_version": "1.1",
      "id": "auto-b274d6785e",
      "slug": "new-ways-to-learn-and-teach-with-chatgpt-work-and-codex-b274d6785e",
      "url": "https://feed7.dev/p/new-ways-to-learn-and-teach-with-chatgpt-work-and-codex-b274d6785e",
      "title": "New ways to learn and teach with ChatGPT Work and Codex",
      "why_included": "The announcement names education plugins but provides no plugin list, capabilities, pricing, availability, or integration details.",
      "summary": "OpenAI is adding education plugins to ChatGPT Work and Codex for teaching, learning, research, and building. The announcement provides no details on capabilities or access.",
      "practical_implication": "Builders in education may want to examine whether these plugins can supply agents with domain-specific workflows for research, teaching, or project work.",
      "agent_context": "OpenAI announced **education plugins** for **ChatGPT Work and Codex**, aimed at K–12 teachers, college educators, and students.\n\nBuilders in education may want to examine whether these plugins can supply agents with domain-specific workflows for research, teaching, or project work.\n\nThe supplied announcement names audiences and use cases but gives no plugin list, technical behavior, pricing, availability, or integration details.",
      "source": {
        "name": "OpenAI",
        "url": "https://openai.com/index/learn-teach-chatgpt-work-codex",
        "published_at": "2026-08-04T00:00:00.000Z"
      },
      "source_class": "blog_post",
      "content_type": "Official Release",
      "layer": "tools",
      "domains": [
        "research",
        "coding"
      ],
      "topics": [
        "skills",
        "coding-agents"
      ],
      "verification": {
        "status": "official_source",
        "label": "Official Source",
        "method": "source_feed",
        "verified_at": null
      },
      "uncertainty": [],
      "connected_context": null,
      "lifecycle": "New",
      "published_at": "2026-08-04T00:00:00.000Z",
      "modified_at": "2026-08-04T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
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    }
  ],
  "agent_instruction": "Use these items as source-backed context. Do not invent claims beyond linked material. Prefer practical implications for solo developer work. If sources conflict, call it out."
}