enterprise-information-search-manus-familytools-fullinstr-v2-skill

name	enterprise-information-search-manus-familytools-fullinstr-v2-skill
description	Family-scoped SkillLearnBench skill for enterprise-information-search using latest full-instruction v2 family injection.
allowed-tools	["aggregate_records","data_catalog","fuzzy_search_records","json_record_query","list_dir","read_file","read_table","run_python","search_text","write_file","write_json"]
metadata	{"benchmark":"SkillLearnBench","generator":"Manus","baseline":"manus_familytools_fullinstr_v2","generation_constraints":"slb_no_instance_memory_familytools_v2","allowed_gold_input":"familytools_full_instruction_v2","skill_injection_scope":"family_scoped","date":"2026-05-23"}

Skill: Enterprise Information Search

When to use this skill

Use this skill for enterprise-information-search tasks in SkillLearnBench. It helps inventory enterprise data, identify requested organizations, join metadata with source records, and write an evidence-backed result file.

Visible input and artifact inventory

• Inputs: Data directory at /root/DATA (JSON metadata/products) and question file at /root/question.txt.
• Output: Answer file at /root/answer.json formatted as a dictionary.

Execution procedure for the current task

1. Inventory: Read instructions and inventory files in /root/DATA.
2. Extract: Get output paths, schemas, and constraints from visible files.
3. Process: Use allowed tools to read questions and data. Join metadata with records.
4. Format: Create the artifact with deterministic transformations. Ensure answer is a list (even for single items) and tokens is a positive number.
5. Check: Run sanity checks using visible files. Repair mismatches.
6. Finalize: Submit only when the artifact exists and matches the instruction shape.

Family tool routing and useful placeholder snippets

• list_dir: Explore /root/DATA.
• read_file: Read /root/question.txt.
• json_record_query / read_table: Query JSON data.
• fuzzy_search_records / search_text: Find specific info.
• aggregate_records: Combine data sources.
• write_json: Write to /root/answer.json.
• run_python: Complex data manipulation.

Validation checks before final submission

• Output exists at /root/answer.json.
• Output is a valid JSON dictionary.
• Every question key is present.
• Each value is a dict with answer (list) and tokens (number).

Common failure modes and repair actions

• Missing File: Verify path in write_json.
• Bad Format: Ensure output is a dict and answers are lists.
• Missing Questions: Iterate over all input questions.
• Bad Token Type: Cast tokens to int/float.

Finalizer capsule

To restore the minimum required artifact shape near max-step:

import json
def finalize(qs, ans, toks, path):
    res = {q: {"answer": ans.get(q, []), "tokens": toks.get(q, 0)} for q in qs}
    with open(path, 'w') as f: json.dump(res, f)