BD aieos
AIEOS (AI Entity Object Specification) is a standardization framework designed to solve the "identity crisis" currently facing AI agents. Combined with Soul Documents, together they form a comprehensive blueprint for AI behavior. The goal is to establish a standardized data structure that defines exactly how an agent speaks, reacts, and remembers. This allows developers, and agents themselves, to construct specific personas with the portability to move across different ecosystems without losing their behavioral integrity. As we move toward a world of "Agentic Workflows," AIEOS ensures that agents maintain consistent traits regardless of the underlying model. By treating personality as a deployable asset rather than a fragile prompt, we are providing the "DNA kit" for the next generation of digital entities.
AIEOS (AI Entity Object Specification) is a standardization framework designed to solve the "identity crisis" currently facing AI agents.
As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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 · 0
✓ No critical or high findings
Files scanned: 3. 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 43/100
- 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
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 7 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 683 tokens
- 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)
- +3Description length 818: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +4Structure: 4 headings
- +3Step-by-step instructions: 7 items
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.