BD ima-kb-organizer
IMA 知识库自动分类整理与 RAG 检索增强生成技能。扫描 IMA 知识库内容，按自定义规则自动分类，生成分类索引文档（Word + Markdown），支持定期自动整理，以及基于分类索引的精准 RAG 文档生成。当用户需要整理 IMA 知识库、定期分类知识库内容、使用 IMA 知识库资料辅助撰写文档、或设置知识库自动分类整理流程时触发此技能。关键词：IMA、知识库整理、分类索引、RAG、定期扫描、检索增强生成。
IMA 知识库自动分类整理与 RAG 检索增强生成技能。扫描 IMA 知识库内容，按自定义规则自动分类，生成分类索引文档（Word + Markdown），支持定期自动整理，以及基于分类索引的精准 RAG 文档生成。当用户需要整理 IMA 知识库、定期分类知识库内容、使用 IMA…
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-agent-memory-dump2026-07-29-12-12-31/.workbuddy/memory/2026-07-29.mdAgent memory / workspace files bundled with the skill (2) — likely a workspace dump with personal data or tokens2026-07-29-12-12-31/.workbuddy/memory/2026-07-29.md, 2026-07-29-12-12-31/.workbuddy/memory/MEMORY.md
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 46/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 47 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1110 tokens
- 100Running it twice. No mutating operations
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 209: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 47 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.