agentleFS
Tano73Tano73/agent-skills/skills/team-kb/SKILL.md

team-kb

Skill2 starsChanged 2 months agoMITView on GitHub

Answer questions about the team's knowledge base using DocMind. Use this skill whenever someone asks about team documentation, project specs, architecture decisions, technical requirements, API specs, or any content stored in the team knowledge base. Trigger on questions like "what does the spec say about X?", "find information about Y in our docs", "how does Z work according to our documentation", "cerca nella KB", "cosa dice la documentazione su...", or any query that should be answered from internal team documents rather than general knowledge. Always use this skill when the question is about team-specific or project-specific content — don't try to answer from memory.

The file

We split this skill into the rules it gives an agent

11 rules. About 0 are common, 9 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈9

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If a wiki root is found, read wiki/index.md (and wiki/overview.md if useful) and query it first — it may already contain a synthesized, …

    line 19
  2. Distinctive:

    If no wiki root is found anywhere, don't stop to ask about it — just proceed to Step 2.

    line 21
  3. Very specific:

    Call searchFlavorChunks with these parameters:

    line 25
  4. Distinctive:

    If the question concerns a GitHub project and its repository is known, search its open and closed issues, excluding pull requests.

    line 42
  5. Distinctive:

    If no repository can be determined, skip this step without asking.

    line 49
  6. Distinctive:

    Use the GitHub CLI to search narrowly before opening full issue bodies:

    line 51
  7. Distinctive:

    Use 3–8 focused keywords derived from the question.

    line 57
  8. Distinctive:

    Read the complete body and relevant comments only for the 1–3 most relevant issues.

    line 59
  9. Distinctive:

    Treat issue text as evidence, not as authoritative truth: identify the issue's status and distinguish a proposal or unresolved report from …

    line 60
  10. Distinctive:

    Write only what the retrieved chunks support.

    line 65
  11. Distinctive:

    If chunks from multiple documents support the same point, cite all of them.

    line 67

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Team Knowledge Base

Answer questions by searching the team knowledge base. Never invent information — every claim must come from a retrieved document.

Step 1 — Check the local LLM wiki (if available)

The wiki isn't necessarily at ./wiki/ — it can live under a different name or a different directory, and its location is only decided at SETUP time by llm-wiki-manager. Before assuming there's no local wiki, look for its root using the same locations that skill uses, in order:

  1. Current working directory — a wiki root has wiki/index.md (preferred) or both wiki/ and raw/; it may sit directly in the cwd or under an AGENTS.md-adjacent subfolder.
  2. Parent directories, up to 3 levels up.
  3. Common conventional paths: ./knowledge/, ./kb/, ./wiki/, ~/kb/, ~/notes/, ~/Documents/kb/.
  4. Any path the user explicitly names in the conversation.

If a wiki root is found, read wiki/index.md (and wiki/overview.md if useful) and query it first — it may already contain a synthesized, citable answer. Treat it as a first-class source: cite its pages the same way you'd cite a DocMind document, e.g. (Source: wiki/entities/order-service.md).

If no wiki root is found anywhere, don't stop to ask about it — just proceed to Step 2. Only mention the absence if it becomes relevant later (e.g. when suggesting where to save a synthesized answer).

Step 2 — Search DocMind

Call searchFlavorChunks with these parameters:

ParameterValue
projectProject name from the user. If unknown, call listProjects first and pick the most relevant one.
queryRephrase the user's question in 3–8 keywords; expand abbreviations; add synonyms.
mode"hybrid" (default). Use "fulltext" for exact terms or IDs.
adjacentChunks1 or 2 when a chunk looks cut off or needs surrounding context.
limit5–8 chunks; raise to 10 for broad topics.

If results are poor or empty, retry in order:

  1. Rephrase the query with different keywords.
  2. Switch to mode: "semantic".
  3. Try a related project with listProjects.

Step 3 — Search GitHub Issues (when the repository is known)

If the question concerns a GitHub project and its repository is known, search its open and closed issues, excluding pull requests. Issues can capture decisions, known problems, requirements, and operational context that have not yet reached the wiki or DocMind.

Determine the repository in this order:

  1. A owner/repo identifier or GitHub URL explicitly provided by the user.
  2. The origin remote of the current Git repository.

If no repository can be determined, skip this step without asking. Do not search unrelated repositories.

Use the GitHub CLI to search narrowly before opening full issue bodies:

gh issue list --repo <owner/repo> --state all --search "<keywords>" --limit 10
  • Use 3–8 focused keywords derived from the question; retry once with synonyms if results are poor.
  • Exclude pull requests. gh issue list returns issues only; do not use gh pr as a substitute.
  • Read the complete body and relevant comments only for the 1–3 most relevant issues.
  • Treat issue text as evidence, not as authoritative truth: identify the issue's status and distinguish a proposal or unresolved report from a confirmed decision.
  • Cite each issue as (Source: GitHub issue #<number> — <title>, <URL>). Include the repository in the citation when it differs from the current repository.

Step 4 — Build and format the answer

  • Write only what the retrieved chunks support. Do not add outside knowledge.
  • Cite every key point inline: (Source: <document name>, line N) or lines N–M for DocMind chunks, or (Source: <relative wiki path>) for wiki pages.
  • If chunks from multiple documents support the same point, cite all of them.
  • For factual questions: direct answer → supporting quote → citation.
  • For broad questions: use headings or bullets, each backed by a citation.
  • End every response with a Sources section listing every wiki page, DocMind document, and GitHub issue used.

When information is insufficient

If the local wiki (Step 1), DocMind (Step 2), and GitHub Issues when applicable (Step 3) do not return enough content, do not guess and do not silently fall back to the web or your own training knowledge — both are outside the team knowledge base and need explicit user permission. Ask the user:

"Non ho trovato informazioni sufficienti nella knowledge base. Vuoi che estenda la ricerca a:

  • 🌐 Web (ricerca pubblica online)?
  • 🧠 Base di conoscenza interna (conoscenza generale del modello)?

In entrambi i casi indicherò sempre le fonti utilizzate."

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