Source Research

---

name: source-research description: "Build and maintain a reusable source-research system for discovering source pools, evaluating whether they are worth ongoing investment, defining efficient acquisition/filtering methods, recording rejection decisions, and producing high-quality source lists or notes. Use when the user mentions 信源, 信源池, 高质量信源, 信息源, 来源池, 作者池, account/blog/source curation, or wants a repeatable framework for finding and using high-quality information sources."

---

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. 2. 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. 2. If a pool was already evaluated and rejected, preserve the rejection conclusion so future agents do not waste time re-evaluating it. 3. If a pool is useful but not automated yet, manual collection is allowed; do not block on automation. 4. If a pool repeatedly proves valuable, raise priority for methodology, engineering, and automation. 5. 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:

references/framework.md

references/artifacts.md

references/storage.md

references/organization.md

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:

.source-research/

If it does not exist yet, initialize it with:

python /scripts/init_source_research.py [workspace-root]

Canonical categories inside .source-research/:

source-pools/

acquisition/

filtering/

high-quality-sources/

high-quality-information/

rejections/

programs/

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

Add or update:

- 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.