BD growth-log
Write growth log entries that extract reusable patterns from completed work — root cause, transferable rule, and a recognizable signal — instead of diary-style event narration, with a 4-8 sentence template and merge-duplicates discipline. Use when capturing what was learned after a complex task, debugging session, failure, or rollback, when reviewing progress over a period, or when a delivery gate asks what was learned.
This skill teaches an AI to write growth logs—distilled work summaries instead of event diaries. Promises to extract root cause, transferable rule, and recognition signal, with a 4–8 sentence template and duplicate-merge discipline.
One file with description and examples. Checks show: syntax passes, loads on major platforms (Claude, Cursor, DeepSeek, Mistral) without errors. Quality rated 78, but process only 40—the template works, yet the files lack actual duplicate-merge logic, and no examples show how an AI should distinguish a transferable rule from a one-off case. Broken references signal incomplete documentation.
Write growth log entries that extract reusable patterns from completed work — root cause, transferable rule, and a recognizable signal — instead of…
As a process D 40/100 · Unfinished process — References files that are not bundled: ../path/to/related-entry.md
How to improve
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: ../path/to/related-entry.md
Process rating: all ten parameters 40/100
- 0Tools and files. 1 referenced file(s) missing: ../path/to/related-entry.md
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 100Steps. 23 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1621 tokens
- 100Progress reporting. Reports progress
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 423: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 23 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.