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Compare two versions of an LLM-directed document - an original (teacher) and a candidate (student) - across a transfer set and return a per-case…
Renders the /assess report from the deterministic run-context.json and the layer scorecard - the scorecard, the verbatim cross-layer findings, lying signals, and the mandatory Top 3 Actions. TRIGGER when the /assess orchestrator reaches the report-writing step; not a standalone
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Renders the /assess report from the deterministic run-context.json and the layer scorecard - the scorecard, the verbatim cross-layer findings, lying signals, and the mandatory Top 3 Actions. TRIGGER when the /assess orchestrator reaches the report-writing step; not a standalone
name: assess-findings description: Renders the /assess report from the deterministic run-context.json and the layer scorecard - the scorecard, the verbatim cross-layer findings, lying signals, and the mandatory Top 3 Actions. TRIGGER when the /assess orchestrator reaches the report-writing step; not a standalone user command.
The report-writing half of `/assess`. The deterministic core has written `.assess/run-context.json` (the data bus) and the `assess-layer-scorer` agent has returned the 0-8 scorecard. Your job is to assemble `.assess/assess-report.md`: the scorecard, the snapshots, the **verbatim** cross-layer findings, the lying signals, and the Top 3 Actions.
The deterministic parts are not yours to invent - you paste them. You write the prose *around* a findings section you cannot omit or reorder. This is the deterministic-core-writes-data / LLM-writes-prose split that makes the report reproducible regardless of which model drives the run.
The layers above each measure one axis. The deterministic core also crosses those axes against each other and emits ten named findings - the "where to look" signals no single layer surfaces. Read them once, after the per-layer scans:
jq '.derived_findings, .attention, .keyhole_summary, .prescribed_actions' "$REPO_ROOT/.assess/run-context.json"
`derived_findings` is a fixed-order list of ten `{name, paths, action}` objects - all ten always present, `paths` may be empty. Omit a finding from the report when its `paths` is empty. Each pairs an axis-crossing with the action it implies:
`attention` ranks the few units landing in the most *negative* findings (`refactor_boundary` never counts) - the "look here first" list, each row carrying its `findings` and `score`. Lead the report's findings with the top of this list.
**Copy `findings_markdown` verbatim.** `run-context.json` carries a pre-rendered `findings_markdown` string - the deterministic findings section (the ten findings with their paths and actions, then the attention list). Paste it into the report **verbatim, inside the `🔎 Cross-layer findings` fold below** - do not paraphrase, summarise, reorder, or drop findings. You write framing prose (the "why these matter here") *inside* the block, directly under its `## Cross-Layer Findings (Keyhole Readiness)` heading - the block already opens with that heading, so **never add your own heading above or around it**; the heading must appear exactly once in the report. The section itself is the deterministic core
A Claude Code plugin - and a set of standalone skills for any AI assistant: skills, agents, and commands for AI-native development. In Claude Code it runs locally against your own codebase using whichever model you already pay for.
Repo: bjcoombs/ai-native-toolkit
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