SKILLEMALL.ai

BD se-semantic-graph

软件工程语义图谱——把项目全知识域(客户画像/需求/成本/架构/分层/模块运行逻辑/历史决策)落进 axolotl 图库,修 bug/加功能/重构时沿跨域语义边定向查询精确上下文,根治上下文爆炸与注意力分散。触发词:项目语义图谱、修 bug 前查上下文、这段代码为什么存在、功能来源、为何这么设计、影响面查询。

ClawHub Agent Skills author: Sai v0.1.0 MIT-0 11 files body ≈ 1 242 tokens Open the sourceclawhub.ai analyzed 3 d ago

软件工程语义图谱——把项目全知识域(客户画像/需求/成本/架构/分层/模块运行逻辑/历史决策)落进 axolotl 图库,修 bug/加功能/重构时沿跨域语义边定向查询精确上下文,根治上下文爆炸与注意力分散。触发词:项目语义图谱、修 bug 前查上下文、这段代码为什么存在、功能来源、为何这么设计、影响面查询。

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
82/100
safety, quality, tests
Safety 60%
90
Quality 40%
71
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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

✓ No critical or high findings

Medium and low: 2
  • medium Dangerous commands cmd-install-from-url README.md:51
    Installs a package from an untrusted URL / archive (security demo / example)
    pip install --force-reinstall --no-deps target/wheels/axolotl_rs-*.whl
    demo
  • medium Dangerous commands cmd-install-from-url SKILL.md:30
    Installs a package from an untrusted URL / archive (security demo / example)
    pip install --force-reinstall --no-deps target/wheels/axolotl_rs-*.whl
    demo

Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "description_zh"

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. 43 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1242 tokens
  • 100Running it twice. No mutating operations
  • low 11 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 155: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 43 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: clean
This skill stores user-confirmed project knowledge in a local graph to guide software work, with no evidence of hidden exfiltration or destructive behavior.
LLM: benign (high) · VirusTotal: · 2 Sept 2026