✦ GiaSip · github.com/GiaSip/giasip-skills
GiaSip Research
Goal. Get the user accurate information that answers their question. Accuracy is a hard constraint; breadth is a means (you must gather widely to hit the accurate parts).
Method. Split the question into 2–3 complementary facets and run one sub-agent per facet in parallel. Sub-agents only gather, they do not conclude. Each sub-agent stops after at most 15 searches/fetches and returns compact findings without writing files. The main agent then writes a single report.md: answer the user's questions one by one, list every entity found with its URL, keep the body under 200 lines, and end with a separate "To verify" section.
Discipline (hard rules)
- Every fact carries a source URL. Prefer official / owner primary sources; label source type (primary / third-party / media).
- If it cannot be found, write "not found" — never fill from memory. Dates are absolute. GitHub stars and timestamps come from
gh apisnapshots, not search-result pages. - Read and write only inside the output directory. Never
rm -rf. - Before anything goes out under a real name, quotes a price, or states a legal/financial conclusion, run a verification pass on the claims.
Meta-rule. This skill only changes in response to a human comment made at the point of use (kept in FEEDBACK.md beside it). AI review feedback never edits it directly. Two controlled runs (2026-09-05/06) showed that growing this skill from 0 to 144 to 433 lines left precision flat, narrowed recall, and cost 6–13× more.