القائمة
EDSL agent reference - AgentList operations, trait manipulation, templates, codebooks, and instructions
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/expectedparrot/ep-skills --skill edsl-agent-referenceيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Generate a qualitative interview guide as an EDSL Survey with QuestionInterview, based on Geiecke & Jaravel (2026) methodology
Publish a completed study to GitHub - creates a repo with the analysis report as README.md, all charts, data, and a reproducibility footer linking to Expected Parrot
Generate a comprehensive, self-contained analysis report for a conjoint (choice-based) study with executive summary, methodology, part-worth utilities, segment analysis, and limitations
عرض SKILL.md
استنادا إلى تصنيف SOC المهني
| name | edsl-agent-reference |
| description | EDSL agent reference - AgentList operations, trait manipulation, templates, codebooks, and instructions |
| allowed-tools | Read, Glob, Bash(python:*) |
Consolidated reference for working with Agents and AgentLists in EDSL: list operations, trait manipulation, templates, codebooks, and instructions.
Operations for manipulating collections of agents.
agents = agents.give_names("respondent_id")
agents = agents.give_names("first_name", "last_name", remove_traits=False)
agents = agents.give_names("city", "id", separator="_")
agents = agents.give_uuid_names()
sample = agents.sample(n=10, seed=42)
shuffled = agents.shuffle(seed=42)
train, test = agents.split(frac_left=0.8, seed=42)
combined = agents1 + agents2
collapsed = agents.collapse()
from edsl import AgentDelta, AgentListDeltas
deltas = AgentListDeltas({
"Alice": AgentDelta({"age": 31, "status": "promoted"}),
"Bob": AgentDelta({"age": 26})
})
updated_agents = agents.apply_deltas(deltas)
dataset = agents.to_dataset()
scenarios = agents.to_scenario_list()
df = agents.to_pandas()
agent = agents[0]
subset = agents[0:5]
first = agents.first()
last = agents.last()
agent = agents.at(3)
for agent in agents:
print(agent.traits)
results = survey.by(agents).run()
results = agents.to(survey).run()
| Operation | Method |
|---|---|
| Name from traits | agents.give_names("trait") |
| UUID names | agents.give_uuid_names() |
| Sample | agents.sample(n=10, seed=42) |
| Shuffle | agents.shuffle(seed=42) |
| Split | agents.split(frac_left=0.8) |
| Combine | agents1 + agents2 |
| Collapse | agents.collapse() |
| Apply deltas | agents.apply_deltas(deltas) |
| To Dataset | agents.to_dataset() |
| To ScenarioList | agents.to_scenario_list() |
| To DataFrame | agents.to_pandas() |
| First/Last | agents.first(), agents.last() |
All trait operations return new instances (immutable pattern).
# Single agent
agent = agent.add_trait("weight", 150)
agent = agent.add_trait({"weight": 150, "height": 5.5})
# AgentList - single value for all agents
agents = agents.add_trait("status", value="participant")
# AgentList - different values per agent (must match length)
agents = agents.add_trait("score", values=[85, 90, 78, 92])
agent = agent.update_trait("age", 31)
# Single agent
agent = agent.drop("temporary_id")
agent = agent.drop("temp1", "temp2")
# AgentList
agents = agents.drop("temporary_id")
agents = agents.drop("temp1", "temp2", "temp3")
agent = agent.keep("age", "occupation")
agents = agents.keep("age", "occupation")
agents = agents.select("age", "occupation") # Alias
agent = agent.rename("old_name", "new_name")
agent = agent.rename({"old1": "new1", "old2": "new2"})
agents = agents.rename("old_name", "new_name")
agents = agents.translate_traits({
"gender": {1: "male", 2: "female", 3: "other"},
"education": {1: "high school", 2: "bachelor", 3: "graduate"}
})
agents = agents.numberify()
young_agents = agents.filter("age < 30")
doctors = agents.filter("occupation == 'doctor'")
young_doctors = agents.filter("age < 30 and occupation == 'doctor'")
alice = agents.filter("name == 'Alice'")
# Remove agents with None/NaN values
clean_agents = agents.filter_na()
clean_agents = agents.filter_na(["age", "income"])
| Operation | Single Agent | AgentList |
|---|---|---|
| Add trait | agent.add_trait("key", value) | agents.add_trait("key", values=[...]) |
| Update trait | agent.update_trait("key", value) | N/A |
| Drop trait | agent.drop("key") | agents.drop("key") |
| Keep traits | agent.keep("k1", "k2") | agents.keep("k1", "k2") |
| Rename trait | agent.rename("old", "new") | agents.rename("old", "new") |
| Filter | N/A | agents.filter("age > 30") |
| Filter NA | N/A | agents.filter_na() |
| Translate | agent.translate_traits({...}) | agents.translate_traits({...}) |
| Numberify | N/A | agents.numberify() |
Controls how agent traits appear in LLM prompts.
Without a template, traits are shown as a dictionary:
Your traits: {'age': 30, 'occupation': 'doctor'}
With a codebook but no custom template:
Your traits:
Age in years: 30
Current profession: doctor
Templates use Jinja2 syntax with access to trait values:
from edsl import Agent, AgentList
agent = Agent(
traits={"age": 30, "occupation": "doctor", "city": "Boston"},
traits_presentation_template="You are a {{age}}-year-old {{occupation}} living in {{city}}."
)
agents = agents.set_traits_presentation_template(
"You are a {{age}}-year-old {{occupation}} living in {{city}}."
)
Inside templates, you can reference:
{{age}}, {{occupation}}{{traits}}{{codebook}}Codebooks map trait keys to human-readable descriptions.
agent = Agent(
traits={"age": 30, "occ": "MD"},
codebook={"age": "Age in years", "occ": "Occupation code"}
)
agents = agents.set_codebook({
"age": "Age in years",
"income": "Annual income in USD",
"edu": "Highest education level"
})
agent = Agent(
traits={"age": 30},
instruction="Answer honestly based on your life experience."
)
agents = agents.set_instruction("Answer as if you were this person.")
agents = agents.add_instructions("Answer honestly and thoughtfully.")
Dynamic traits are computed at question-answering time:
def dynamic_func(question):
if "income" in question.question_text.lower():
return {"disclosure_level": "private"}
return {"disclosure_level": "public"}
agent = Agent(
traits={"age": 30},
dynamic_traits_function=dynamic_func
)
# Map questions to relevant traits
agents = agents.set_dynamic_traits_from_question_map({
"hometown_question": ["hometown", "state"],
"food_question": ["favorite_food", "dietary_restrictions"]
})
| Task | Single Agent | AgentList |
|---|---|---|
| Set codebook | Agent(..., codebook={...}) | agents.set_codebook({...}) |
| Set instruction | Agent(..., instruction="...") | agents.set_instruction("...") |
| Set template | Agent(..., traits_presentation_template="...") | agents.set_traits_presentation_template("...") |
| View codebook | agent.codebook | agents.codebook |
| View instruction | agent.instruction | agents.instruction |
| View template | agent.traits_presentation_template | agents.traits_presentation_template |