본문으로 건너뛰기
cccskills
무료GitHub에 공개

research-completion

Finish an authorized security research task with a durable solved, abstained, or blocked outcome. Use when evidence is sufficient, a stop condition is reached, a tool budget is nearly exhausted, or the agent must commit its best supported answer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md3.2 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Research Completion

Finish deliberately. A provider stopping or a prose summary is not a committed research outcome.

Before finishing

  1. Check the current objective and any public success criteria.
  2. Resolve or explicitly preserve blockers. Do not claim that an approval-gated action ran.
  3. Close or mark remaining task branches appropriately. Do not start a broad new branch merely to deepen an already sufficient result.
  4. Save all evidence supporting the outcome — plus failed-attempt diagnostics — through security-artifact-manifest / security-artifact-access and collect the resulting artifact IDs. Verify every ID: if a tool returns no artifactId, persistence failed — retry or finish blocked; never proceed as if the evidence is saved. Check persistedContentSource: an artifact persisted from manifest text alone (no workspace file content) is a summary stub, not the evidence bytes — treat it as unsaved and do not cite it.
  5. Call update_research_state with the final branch dispositions, artifact-backed evidence summaries, best candidate answer, and any remaining blocker. This is an upsert: include changed entries, not a transcript rewrite. Never invent an artifact ID.
  6. Choose one outcome:
    • solved: a concrete answer — including a well-supported negative result such as "X is not vulnerable to Y" — is supported by durable evidence;
    • abstained: available evidence does not support an answer either way; state the null answer, what was attempted, and the limiting evidence gap;
    • blocked: authorization, approval, target, tool, or runtime state prevents completion.

Commit the outcome

Call finish_research exactly once with the best supported outcome. Solved outcomes need a concrete answer and evidence artifact IDs. Blocked outcomes need a concise remaining-uncertainty explanation. Confidence is a calibrated value from 0 to 1, not a benchmark score.

If the tool rejects the call, repair only the named fields or blocker and retry within the reported limit. Do not evade the gate in prose or begin unrelated exploration. If repairs are exhausted, stop as blocked.

A prose conclusion, empty response, or thought-only stop is not completion. The controller records it as a malformed terminal attempt and may issue one bounded recovery/wrap-up turn. Respond with the typed tool call; do not repeat the prose summary.

Hidden benchmark answers and scorer feedback are never research inputs. The terminal call records what the research agent committed; a separate product review or deterministic oracle decides whether it succeeded.

Near the budget limit

At a wrap-up checkpoint, choose among submitting the best supported result, abstaining, or performing one decisive remaining check. Before that check, upsert update_research_state with the current plan of record so an interruption leaves a durable resumable pause rather than a lost plan. Preserve open uncertainty and evidence provenance. Never spend the final budget on a new broad branch.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Plan and use user-consented local camera and microphone observations for an explicitly authorized physical target. Use when a capture can establish a bounded physical-state observation such as whether a test fixture changed state, an instrument reading changed, or equipment noise changed relatively.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

justsml/ExploitHunter.app142026년 10월 17일 업데이트

Plan vulnerability research using conventional bug-hunting and pentest stage names, specialty playbooks, and a research-hypothesis-experiment loop. Use when designing a bug bounty workflow, selecting tools and strategies, building a decision tree, or turning a target/scope into a staged hunting plan.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

justsml/ExploitHunter.app142026년 10월 17일 업데이트

Passive code-review lane for authorized repositories, local checkouts, uploaded source archives, and diffs. Use when the security research agent needs deterministic candidate discovery before expensive AI investigation, scoped PR-style review, append-only evidence records, or independent revalidation of code findings.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

justsml/ExploitHunter.app142026년 10월 17일 업데이트

Run independent, task-matched expert attempts, select a base, and synthesize the strongest result.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

justsml/ExploitHunter.app142026년 10월 17일 업데이트

Select established CTF and security-research tools for authorized artifact triage, web inspection, reversing, crypto, forensics, networking, and exploit validation. Use when a user provides a CTF-style tool catalog, asks which tools to use, or needs safe usage instructions for common offensive/security tools.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

justsml/ExploitHunter.app142026년 10월 17일 업데이트

diagnose

무료

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

justsml/ExploitHunter.app142026년 10월 17일 업데이트

justsml의 스킬 모두 보기

이 스킬의 문제 신고