一键导入
intent-analysis
Use to infer developer intent from diff, prompt, plan, commit, or PR context, including confidence, uncertainty, mismatch with spec, and risk notes.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
Use to infer developer intent from diff, prompt, plan, commit, or PR context, including confidence, uncertainty, mismatch with spec, and risk notes.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use to launch, supervise, or evaluate one stateless bounded subagent job with a context pack, allowed scope, forbidden scope, validation requirements, structured output contract, and state patch proposal.
Normalize, inspect, or clean up CHANGELOG.md files into versioned, categorized, user-readable release history. Use when the user asks to check,整理,標準化,normalize, rewrite, or prepare a changelog; before releases or tags; or when CHANGELOG.md contains loose notes, commit-log residue, AI conversation residue, TODOs, missing dates, ordering issues, or uncategorized entries.
Use to prepare bounded context packs for stateless subagents, selecting only required specs, diffs, artifacts, constraints, allowed scope, forbidden scope, validation requirements, and output contracts.
Use to turn ambiguity, validation gaps, breaking changes, survived mutations, or spec/test conflicts into human-readable decision options such as accept, update spec, reject, refine tests, or defer.
Use after Devil's Advocate Review to resolve numbered objections from Low to High before workflow atomic decomposition, producing spec patch requirements, human decisions, and pass or blocked gate status.
Use before finalizing a plan or spec to challenge hidden assumptions, edge cases, overdesign, architecture risk, compatibility gaps, migration cost, testing cost, and spec/implementation drift.
| name | intent-analysis |
| description | Use to infer developer intent from diff, prompt, plan, commit, or PR context, including confidence, uncertainty, mismatch with spec, and risk notes. |
Infer what a change is trying to accomplish and where intent is uncertain, so impact analysis and JIT testing target the right behavior.
Before inferring intent, collect deterministic evidence when local repo artifacts are available:
python agent-skills/diff-analysis/scripts/collect_git_diff_evidence.py --repo-root . --json
python agent-skills/impact-analysis/scripts/collect_impact_evidence.py --repo-root . --json
Use commit messages, PR text, user prompts, specs, and atomic item metadata as stated evidence when available. Use LLM judgement only to infer the likely intent, confidence, contradictions, spec alignment, and behavior drift risk. Do not ask the LLM to rediscover changed files, path categories, or git status when the local helpers can collect them.
Run the helper from Windows PowerShell or a POSIX shell (Linux/macOS) with the same command shape; use python3 when python is unavailable.
Stable prefix:
intent-analysisheavy-llm## Output.Dynamic run packet:
impact-analysis or decision ambiguity to decision-proposal.Check:
Use this report template:
### Intent Analysis Report
- Inferred Intent:
- Evidence:
- Confidence: high | medium | low
- Uncertainty:
- Spec Alignment:
- Behavior Drift Risk:
- Next Analysis Step: