Paoding Creator Lab — Build a Testable Content Coach

    1

    Turn creator samples into an evidence-backed playbook and content coach, with sample-split checks and a structured experiment.

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    Paoding Creator Lab — Build a Testable Content Coach

    Example session with this skill installed

    Use the synthetic examples/manifest.json to plan an analysis group and candidate holdouts. Show the independent-unit count without displaying bodies. Explain why the split does not prove independent validation. Then prepare, but do not fill, a reviewer experiment record.

    • Read your context and instructions
    • Compiled the paoding creator lab

    Synthetic local partition demonstration

    Complete captured output of the sample-partition helper on ten fictional posts. This is an allocation check, not proof of unseen model context, an evaluated content coach or improved content performance.

    {
      "status": "CANDIDATE_SPLIT",
      "exit_code": 0,
      "manifest_sha256": "2bc03b89b411cfd05c0fd85d83f367416dfee3a95142bd207eb17a7c77194e7e",
      "submitted_files": 10,
      "independent_units": 10,
      "minimum_analysis_units": 8,
      "shortfall": 0,
      "selection_order": "latest_date",
      "analysis_groups": [
        [
          "S01"
        ],
        [
          "S02"
        ],
        [
          "S03"
        ],
        [
          "S04"
        ],
        [
          "S05"
        ],
        [
          "S06"
        ],
        [
          "S07"
        ],
        [
          "S08"
        ]
      ],
      "candidate_holdout_groups": [
        [
          "S10"
        ],
        [
          "S09"
        ]
      ],
      "samples": [
        {
          "id": "S01",
          "date": "2026-09-01",
          "seen": true,
          "sha256": "e871fd986c7464bf4039eb4d0caa4642ce15762b9623c9fd8934ed5cecc6d5df"
        },
        {
          "id": "S02",
          "date": "2026-09-02",
          "seen": true,
          "sha256": "4646f0f009a9afbb771d5fff9ac7f9f1c3c1be4625433cc012757c7802eb3d47"
        },
        {
          "id": "S03",
          "date": "2026-09-03",
          "seen": true,
          "sha256": "46187aa23aa0723bb53c3fc1bffdad3c33952708fe2151e27405967bcb17db17"
        },
        {
          "id": "S04",
          "date": "2026-09-04",
          "seen": true,
          "sha256": "daac022607c8c79fa603908b147c1bce08a42694a1758c9e1a2cd56da568fc05"
        },
        {
          "id": "S05",
          "date": "2026-09-05",
          "seen": true,
          "sha256": "a4b69b0c41e8f68da946e59ff36199f27b03f17553f652404fe58eb75f87f46c"
        },
        {
          "id": "S06",
          "date": "2026-09-06",
          "seen": true,
          "sha256": "1e745a72f6609524eafca8b66cb23d52f0c06659e41bbd41d07fb5c638a30307"
        },
        {
          "id": "S07",
          "date": "2026-09-07",
          "seen": true,
          "sha256": "b2d5590590d081a834d8b3925a09d9d54d470a076669f38d7f562ede9b449e67"
        },
        {
          "id": "S08",
          "date": "2026-09-08",
          "seen": true,
          "sha256": "d7a011275db2e073d78dc5cabfd71e20516cfbb44048197f111e18a437a94d24"
        },
        {
          "id": "S09",
          "date": "2026-09-09",
          "seen": false,
          "sha256": "1ab8558633547f3f614eb1b1fc7682115cb493dda3feb54bad5a121c0f390525"
        },
        {
          "id": "S10",
          "date": "2026-09-10",
          "seen": false,
          "sha256": "92fe4c691b160c14ff57ca2f21b8296c7b0cbec816b47adabd04acc4495fd587"
        }
      ],
      "caveat": "Allocation uses declared exposure and duplicate groups. It does not prove unseen context, semantic independence, readable screenshots or coach effectiveness."
    }
    

    Eight independent units are assigned to analysis. S10 and S09 are candidate holdouts under the declared exposure labels. Keep those bodies out of the analysis context; inspect grouping and exposure before treating the allocation as a test plan.

    Experiment record awaiting reviewers

    • Coach version: NOT_YET_FROZEN
    • Common task: NOT_YET_SELECTED
    • Baseline context: NOT_YET_ESTABLISHED
    • Reviewers: NOT_YET_ASSIGNED
    • Scores: NOT_REVIEWED
    • Conclusion: No coach-effectiveness claim is supported by this demonstration.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    You have read a creator's best posts, but “good hooks” and “clear voice” are still too vague to use. Paoding Creator Lab turns actual samples into specific, source-linked choices you can test in your own work.

    Analyze topics, structure, expression and reasoning. Keep each finding tied to supporting posts, counterexamples and a clear scope. Then draft a content coach that uses supported strategies without impersonating the creator or inventing your experience.

    What this package adds

    • An English workflow built on the MIT-licensed Paoding project, with its attribution retained.
    • A small local sample-partition tool: checks IDs and files, groups exact/declared near duplicates, respects declared exposure and outputs hashes and candidate allocations without printing bodies.
    • A frozen-version experiment record with common-task conditions and empty reviewer fields.
    • Ten fictional posts for a runnable partition demonstration.

    The public upstream already contains the four-layer method and evidence/validation principles. This product's proposed value is the focused English workflow, added partition automation and experiment artifacts. It is not exclusive ownership of that public method or an endorsement by LearnPrompt.

    Use it with at least eight independent readable posts or transcripts from yourself or a selected creator. A smaller batch produces an intake request. Missing engagement permits structure analysis, not an explanation of reach.

    Keep the limits clear. Candidate holdouts depend on honest exposure labels and grouping; the tool cannot prove that a model has never seen them. An independent baseline requires genuinely separate, authorized contexts. No scores means awaiting review. No API scraping, browser-cookie access, media downloading, paid collection, background monitoring or promised viral growth is included.

    The hand-analysis workflow can run in a text-capable agent. The partition helper additionally needs Python and access to the package resource. Cross-agent and Agensi delivery compatibility remain unverified; confirm resource support before claiming the executable path is available.

    Submission transparency

    This is a disclosed derivative with new tooling, not a renamed proprietary copy. MIT attribution is included. The optional upstream paid collectors are deliberately absent. Please assess whether the added product value meets Agensi's originality policy; no approval is assumed. Mechanical helper tests and author-reviewed templates do not demonstrate content performance or independent model behavior.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Text-capable SKILL.md host
    Optional: Python 3.10+, terminal and local package-resource access

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