Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is a dense, actionable reference with executable commands, DuckDB-specific SQL patterns, and a well-structured progressive-disclosure layout pointing to real reference files. The only meaningful gap is the absence of an explicit verification step after batch data fetches into the persistent database.
Suggestions
Add an explicit verification checkpoint after each batch fetch (e.g. run `sql "SELECT count(*) FROM request_logs WHERE app_id = <id> AND timestamp >= '<since>'"` to confirm rows loaded and match the intended scope) before proceeding to analysis.
In the Investigation Workflow, turn the 'Iterate if needed' step into a concrete feedback loop: if a query returns zero rows or unexpected data, list the specific corrective actions (widen/fix the time range, check filters against the endpoints/consumers lookup, re-run the fetch).
For destructive operations like `reset-db`, add a one-line validation/guard note (e.g. confirm the target db path and that no other in-progress investigation depends on the existing tables) before the command is run.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and every section earns its place: the Key Concepts define Apitally-specific terms (consumer_id vs identifier, path vs url) Claude would not know, and the SQL patterns cover non-obvious DuckDB syntax (list comprehensions over STRUCT arrays, AT TIME ZONE for TIMESTAMPTZ, JSON path operators) rather than padding with general knowledge. | 3 / 3 |
Actionability | It provides fully executable, copy-paste-ready `npx @apitally/cli ...` commands and complete SQL examples with real field names and filter JSON, satisfying the level-3 'fully executable code/commands; copy-paste ready' anchor rather than pseudocode. | 3 / 3 |
Workflow Clarity | The 6-step Investigation Workflow is clearly sequenced and includes an auth-error feedback loop and a CRITICAL scope-filtering guard, but the batch/database operations (fetching up to 1,000,000 rows into a persistent DuckDB) lack an explicit post-fetch verification checkpoint, which the rubric's judging guideline says caps this dimension at 2. | 2 / 3 |
Progressive Disclosure | The body is a clear overview that points to three well-signaled, one-level-deep references (commands.md, duckdb_tables.md, duckdb_json_functions.md), all of which exist in references/; detailed flags/schemas are appropriately split out rather than inlined, matching the level-3 anchor. | 3 / 3 |
Total | 11 / 12 Passed |