CF ima-knowledge-base
腾讯IMA知识库技能。封装IMA智能知识库的完整接口，支持OpenAPI和Cookie两种认证方式。功能包括知识库管理、笔记CRUD、RAG问答、文件夹操作、订阅知识库管理等。
腾讯IMA知识库技能。封装IMA智能知识库的完整接口，支持OpenAPI和Cookie两种认证方式。功能包括知识库管理、笔记CRUD、RAG问答、文件夹操作、订阅知识库管理等。
As a process F 33/100 · Will not run — References files that are not bundled: .+?
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 2
-
high Exfiltration
intent-browser-credential-storeskill-card.md:20Accesses a browser credential / cookie storeRisk: The skill handles live IMA login cookies that can grant access to a user's knowledge-base data. <br>
Medium and low: 1
-
low Secrets in code
secret-high-entropy-tokentest_connection.py:32High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)api_key = os.getenv("IMA_API_KEY", "6KH6…aPv+pV89DJl/Asd+upvL…9CA==")fixturequoted
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") - warning
missing-refreference to a missing file: .+?
Process rating: all ten parameters 33/100
- 0Tools and files. 1 referenced file(s) missing: .+?
- 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
- 30Running it twice. 1 mutating operations with no state check
- 100Steps. 18 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1296 tokens
- low 10 top-level sections: this looks like several domains in one skill
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)
- +3Description length 88: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 31 headings
- +3Step-by-step instructions: 18 items
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.