BD cross-session-memory
给 AI agent/bot 做跨会话持久记忆的分层混合方案。当用户要"给 bot 加记忆""让 agent 记住之前的事""跨会话上下文持久化""记忆去重/断链/陈旧检查"，或要搭一个有长期记忆的 agent 时使用。方案=markdown 真理源(人可读可手改) + SQLite 派生索引(选择性召回) + 生命周期检查(断链/重复/陈旧)，纯标准库零依赖。
给 AI agent/bot 做跨会话持久记忆的分层混合方案。当用户要"给 bot 加记忆""让 agent 记住之前的事""跨会话上下文持久化""记忆去重/断链/陈旧检查"，或要搭一个有长期记忆的 agent 时使用。方案=markdown 真理源(人可读可手改) + SQLite 派生索引(选择性召回) +…
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-dumpexamples/sample-memory/MEMORY.mdAgent memory / workspace files bundled with the skill (1) — likely a workspace dump with personal data or tokensexamples/sample-memory/MEMORY.md
Files scanned: 11. 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")
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. 14 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 374 tokens
- 100Running it twice. No mutating operations
- low The response is described with custom markup (4 tags): a typed call is more reliable
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
- +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
- +5Description quotes 4 example trigger phrases
- +3Description length 182: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 14 items
- +4Has examples (1 code blocks)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.