Source Research Skill
Use this skill when the task is about:
- - discovering or recording new source pools;
- deciding whether a pool is worth continued investment;
- defining how to acquire information from a pool efficiently;
- filtering pools into high-quality sources;
- standardizing how source-research artifacts are stored;
- leaving reusable artifacts so future agents do not repeat the same analysis.
Core model
Treat source research as:
- 1. Three result layers: source pools / acquisition methods / filtered high-quality sources.
- Four execution stages: record pool / research methods / produce source results / automate monitoring.
Important: the four stages are not a strict sequence. A pool may stay manual, may have results before methods are documented, or may be recorded now and researched later.
Default operating rules
- 1. If you discover a new pool while doing another task, record it immediately.
- If a pool was already evaluated and rejected, preserve the rejection conclusion so future agents do not waste time re-evaluating it.
- If a pool is useful but not automated yet, manual collection is allowed; do not block on automation.
- If a pool repeatedly proves valuable, raise priority for methodology, engineering, and automation.
- Always try to leave at least one reusable artifact: pool update, method doc, result list, rejection note, or engineering design.
Read these references
Read these files before doing non-trivial source-research work:
- - INLINECODE0
- INLINECODE1
- INLINECODE2
- INLINECODE3
Storage contract
This skill is not only about how to use the framework. It also standardizes how these things should be stored:
- - source pool information;
- acquisition rules or programs;
- filtering rules or programs;
- high-quality source lists;
- high-quality information captured from those sources;
- rejection conclusions;
- information results and automation assets.
Follow the established pattern used by strong skills: keep the methodology in the skill, and keep the workspace data in a dedicated directory.
The canonical dedicated workspace directory for this skill is:
If it does not exist yet, initialize it with:
Canonical categories inside .source-research/:
- - INLINECODE7
- INLINECODE8
- INLINECODE9
- INLINECODE10
- INLINECODE11
- INLINECODE12
- INLINECODE13
Do not treat generic docs as the primary storage for these results. Generic docs may hold framework notes, but canonical source-research data should live in .source-research/.
Minimal workflow
A. New pool discovered
- - Add or update a pool file under
.source-research/source-pools/. - Mark a status such as: observed / worth deeper research / has high-quality results / suitable for engineering / not worth investment.
B. Existing pool revisited
- - Check existing pool notes and rejection conclusions first.
- If it was previously rejected, only reopen when there is genuinely new evidence.
C. Information needed now
- - Manual collection is acceptable.
- If repeated manual work appears, record that this pool should move toward reusable acquisition/filtering methods.
- Store useful captured information under
.source-research/high-quality-information/ when it is worth preserving.
D. Valuable pool confirmed
- acquisition method or program under
.source-research/acquisition/ or
.source-research/programs/;
- filtering method or program under
.source-research/filtering/ or
.source-research/programs/;
- high-quality source results under
.source-research/high-quality-sources/;
- engineering/automation design when justified.
Storage standard
When using this skill, do not leave the outcome only in chat. Normalize storage according to artifact type:
- - pool metadata and status ->
.source-research/source-pools/; - acquisition methods/programs ->
.source-research/acquisition/ or .source-research/programs/; - filtering methods/programs ->
.source-research/filtering/ or .source-research/programs/; - filtered high-quality source results ->
.source-research/high-quality-sources/; - high-quality information from those sources ->
.source-research/high-quality-information/; - rejection decisions ->
.source-research/rejections/; - engineering/automation work ->
.source-research/programs/.
Output standard
Do not end with only vague suggestions. Leave concrete artifacts in the workspace so another agent can continue from files rather than chat memory.
来源调研技能
当任务涉及以下内容时使用此技能:
- - 发现或记录新的来源池;
- 判断某个来源池是否值得持续投入;
- 定义如何高效地从来源池获取信息;
- 将来源池筛选为高质量来源;
- 标准化来源调研产物的存储方式;
- 留下可复用的产物,使未来的智能体无需重复相同的分析。
核心模型
将来源调研视为:
- 1. 三个结果层级:来源池 / 获取方法 / 筛选后的高质量来源。
- 四个执行阶段:记录来源池 / 研究方法 / 产出来源结果 / 自动化监控。
重要提示:四个阶段并非严格的顺序流程。某个来源池可能保持手动操作,可能在方法文档化之前就已产出结果,也可能先记录后续再研究。
默认操作规则
- 1. 如果在执行其他任务时发现新的来源池,立即记录。
- 如果某个来源池已被评估并拒绝,保留拒绝结论,避免未来的智能体浪费时间重新评估。
- 如果某个来源池有用但尚未自动化,允许手动收集;不要因自动化而阻塞。
- 如果某个来源池反复证明有价值,提升优先级以进行方法论、工程化和自动化。
- 始终尝试至少留下一个可复用产物:来源池更新、方法文档、结果列表、拒绝说明或工程设计。
阅读参考资料
在执行重要的来源调研工作前,请阅读以下文件:
- - references/framework.md
- references/artifacts.md
- references/storage.md
- references/organization.md
存储规范
此技能不仅涉及如何使用框架,还标准化了以下内容的存储方式:
- - 来源池信息;
- 获取规则或程序;
- 筛选规则或程序;
- 高质量来源列表;
- 从这些来源捕获的高质量信息;
- 拒绝结论;
- 信息结果和自动化资产。
遵循优秀技能的既定模式:将方法论保留在技能中,将工作区数据保留在专用目录中。
此技能的规范专用工作区目录为:
如果该目录尚不存在,请使用以下命令初始化:
- - python <技能目录>/scripts/initsourceresearch.py [工作区根目录]
.source-research/ 内的规范分类:
- - source-pools/
- acquisition/
- filtering/
- high-quality-sources/
- high-quality-information/
- rejections/
- programs/
不要将通用文档作为这些结果的主要存储位置。通用文档可以存放框架笔记,但规范的来源调研数据应存放在 .source-research/ 中。
最小工作流程
A. 发现新来源池
- - 在 .source-research/source-pools/ 下添加或更新来源池文件。
- 标记状态,例如:已观察 / 值得深入研究 / 已有高质量结果 / 适合工程化 / 不值得投入。
B. 重新审视现有来源池
- - 首先检查现有的来源池笔记和拒绝结论。
- 如果之前已被拒绝,只有在确实有新证据时才重新开启。
C. 当前需要信息
- - 手动收集是可接受的。
- 如果出现重复的手动工作,记录该来源池应转向可复用的获取/筛选方法。
- 当值得保存时,将有价值的捕获信息存储在 .source-research/high-quality-information/ 下。
D. 确认有价值的来源池
- 获取方法或程序,存放于 .source-research/acquisition/ 或 .source-research/programs/;
- 筛选方法或程序,存放于 .source-research/filtering/ 或 .source-research/programs/;
- 高质量来源结果,存放于 .source-research/high-quality-sources/;
- 在合理的情况下,添加工程化/自动化设计。
存储标准
使用此技能时,不要仅将结果留在对话中。根据产物类型规范化存储:
- - 来源池元数据和状态 -> .source-research/source-pools/;
- 获取方法/程序 -> .source-research/acquisition/ 或 .source-research/programs/;
- 筛选方法/程序 -> .source-research/filtering/ 或 .source-research/programs/;
- 筛选后的高质量来源结果 -> .source-research/high-quality-sources/;
- 来自这些来源的高质量信息 -> .source-research/high-quality-information/;
- 拒绝决策 -> .source-research/rejections/;
- 工程化/自动化工作 -> .source-research/programs/。
输出标准
不要仅以模糊的建议结束。在工作区中留下具体的产物,使其他智能体能够从文件而非对话记忆中继续工作。