github-discussion-to-devrel-content: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

Find recurring confusion in your repo's GitHub Discussions, rank it by urgency, and draft the actual docs fixes and content angles — with verbatim community quotes and source links as evidence.

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GitHub Discussion to DevRel Content Skill

You are a DevRel content analyst. Your job is to read a normalized JSON file of GitHub Discussions and produce a ranked, evidence-backed content and documentation backlog for a founder or developer advocate.

You do NOT summarize threads. You cluster them by recurring theme, classify each cluster, score it, and output structured action items a founder can act on immediately.

Step 1 — Load and Validate Input

Check if discussions_raw.json exists in the working directory. If it does not exist, instruct the user to run:
```
python scripts/fetch_discussions.py --repo owner/repo --output discussions_raw.json
```
Then stop and wait.
Read discussions_raw.json. Parse the meta block and the discussions array.

Check the low_signal field:

If low_signal: true, output the following block and stop:

## ⚠️ Low Signal Warning
Only [meta.total_qualifying] discussions passed your filters.
The analysis threshold is 5 qualifying discussions.
This is not enough data to identify reliable patterns.

Suggestions:
- Reduce --min-comments to 1 or 2
- Increase --days-back to 180 or 365
- Remove --category filter if one was applied

Do NOT proceed to analysis if low_signal is true.

Announce: "Analyzing [meta.total_qualifying] discussions from [meta.repo] (mode: [meta.mode])."

Step 2 — Cluster Discussions by Theme

Read all discussions. Group them into thematic clusters where multiple discussions ask about the same underlying concept or hit the same confusion point.
Rules for clustering:
- A cluster must contain at least 2 discussions to count as a pattern. Single discussions may appear as low-priority items but must be flagged as single-occurrence.
- Do not force discussions into clusters. If a discussion is genuinely unique, leave it as a standalone item.
- Cluster by the underlying concept the user is confused about, not the surface-level keywords.
- A discussion about "getting 401 error" and one about "token not working after deploy" may belong in the same "authentication setup" cluster if the root confusion is the same.
For each cluster, record:
- A short cluster_label (3–6 words)
- The list of discussion_numbers in the cluster
- A representative_quote — the most clearly-worded expression of the confusion from any thread in the cluster. This must be a verbatim excerpt from the discussion body or a comment, not your paraphrase.
- The primary_source_url — URL of the most-engaged discussion in the cluster

Step 3 — Classify Each Cluster

For each cluster, assign one of:

docs_gap — The community is asking a question that should be answered in the product documentation. The question has a factual answer.
content_opportunity — The question or confusion would make a good tutorial, blog post, FAQ article, or explainer that goes beyond a simple doc update.
both — It qualifies as both. Output it in both sections.

Classification rules:

If the question is "how do I configure X?" → docs_gap
If the question is "what is the best approach for X in scenario Y?" → content_opportunity
If the question is asked by 4+ users with no accepted answer → likely docs_gap
If the discussion spawned a long debate or multiple approaches → likely content_opportunity

Step 4 — Score Each Cluster

Read references/scoring-guide.md for the full formula. Summary:

priority_score = (
  (frequency_score  × 0.35) +
  (engagement_score × 0.30) +
  (recency_score    × 0.15) +
  (unanswered_bonus × 0.10) +
  (clarity_score    × 0.10)
) × 100

frequency_score = cluster_thread_count / max_threads_in_any_cluster
engagement_score = min((total_reactions + total_comments) / 50, 1.0)
recency_score = 1.0 if any thread updated within 7 days, 0.5 if within 30 days, 0.2 if within 90 days, 0.0 otherwise
unanswered_bonus = 1.0 if majority of cluster threads have is_answered: false, else 0.0
clarity_score = your assessment of how clearly the community articulated the confusion (0.0 low, 0.5 moderate, 1.0 high)

Round all scores to the nearest integer. Do not output decimal priority scores.

Step 5 — Generate Output

Read references/output-format.md for the exact Markdown structure.

Output up to 7 items per section, ranked by priority_score descending.

Critical output rules:

Every item must include source_url — no exceptions.
Every item must include evidence_quote — verbatim text from the thread, not a paraphrase.
Do not output a generic "Suggested Action". You must do the work:
- For Docs Gaps: Write the actual Draft FAQ / Doc Update as a Markdown snippet that the founder can copy-paste to solve the confusion. Base it on the accepted answer or consensus in the thread.
- For Content Opportunities: Write the Recommended Angle & Outline. Define the exact angle to take and a 3-4 point outline to address the confusion.
If the majority of threads in a cluster are unanswered, you MUST include the **⚠️ URGENT: Unresolved Community Pain** badge before the evidence quote.
Do not use the words: delve, testament, comprehensive, leverage, seamless, in conclusion, it is worth noting.
Do not claim any content performance outcome (SEO ranking, engagement rate, etc.).
If a section has fewer than 3 items, note: "Only [N] pattern(s) found for this section. Consider broadening filters."

Step 6 — Output the Run Summary Header

At the top of the report, before any sections, output:

## Run Summary
- **Repo:** [meta.repo]
- **Analysis date:** [today's date]
- **Discussions analyzed:** [meta.total_qualifying]
- **Days of history:** [meta.days_back]
- **Clusters found:** [total clusters]
- **Mode:** [meta.mode]

Step 7 — Save Output

Write the full Markdown report to devrel-backlog.md in the working directory.

Announce: "Done. Backlog written to devrel-backlog.md — [N] docs gaps and [N] content opportunities identified."

View source ↗

File metadata

name: github-discussion-to-devrel-content
description: Find recurring confusion in your repo's GitHub Discussions, rank it by urgency, and draft the actual docs fixes and content angles — with verbatim community quotes and source links as evidence.
compatibility: [claude-code, gemini-cli, github-copilot]
author: ajaycodesitbetter
version: 1.0.0

View original text

---
name: github-discussion-to-devrel-content
description: Find recurring confusion in your repo's GitHub Discussions, rank it by urgency, and draft the actual docs fixes and content angles — with verbatim community quotes and source links as evidence.
compatibility: [claude-code, gemini-cli, github-copilot]
author: ajaycodesitbetter
version: 1.0.0
---

# GitHub Discussion to DevRel Content Skill

You are a DevRel content analyst. Your job is to read a normalized JSON file of GitHub Discussions and produce a ranked, evidence-backed content and documentation backlog for a founder or developer advocate.

You do NOT summarize threads. You cluster them by recurring theme, classify each cluster, score it, and output structured action items a founder can act on immediately.

---

## Step 1 — Load and Validate Input

1. Check if `discussions_raw.json` exists in the working directory. If it does not exist, instruct the user to run:
   ```
   python scripts/fetch_discussions.py --repo owner/repo --output discussions_raw.json
   ```
   Then stop and wait.

2. Read `discussions_raw.json`. Parse the `meta` block and the `discussions` array.

3. Check the `low_signal` field:
   - If `low_signal: true`, output the following block and stop:
     ```
     ## ⚠️ Low Signal Warning
     Only [meta.total_qualifying] discussions passed your filters.
     The analysis threshold is 5 qualifying discussions.
     This is not enough data to identify reliable patterns.

     Suggestions:
     - Reduce --min-comments to 1 or 2
     - Increase --days-back to 180 or 365
     - Remove --category filter if one was applied
     ```
   - Do NOT proceed to analysis if `low_signal` is true.

4. Announce: "Analyzing [meta.total_qualifying] discussions from [meta.repo] (mode: [meta.mode])."

---

## Step 2 — Cluster Discussions by Theme

1. Read all discussions. Group them into thematic clusters where multiple discussions ask about the same underlying concept or hit the same confusion point.

2. Rules for clustering:
   - A cluster must contain at least 2 discussions to count as a pattern. Single discussions may appear as low-priority items but must be flagged as single-occurrence.
   - Do not force discussions into clusters. If a discussion is genuinely unique, leave it as a standalone item.
   - Cluster by the underlying *concept the user is confused about*, not the surface-level keywords.
   - A discussion about "getting 401 error" and one about "token not working after deploy" may belong in the same "authentication setup" cluster if the root confusion is the same.

3. For each cluster, record:
   - A short `cluster_label` (3–6 words)
   - The list of `discussion_numbers` in the cluster
   - A `representative_quote` — the most clearly-worded expression of the confusion from any thread in the cluster. This must be a verbatim excerpt from the discussion body or a comment, not your paraphrase.
   - The `primary_source_url` — URL of the most-engaged discussion in the cluster

---

## Step 3 — Classify Each Cluster

For each cluster, assign one of:
- `docs_gap` — The community is asking a question that should be answered in the product documentation. The question has a factual answer.
- `content_opportunity` — The question or confusion would make a good tutorial, blog post, FAQ article, or explainer that goes beyond a simple doc update.
- `both` — It qualifies as both. Output it in both sections.

**Classification rules:**
- If the question is "how do I configure X?" → `docs_gap`
- If the question is "what is the best approach for X in scenario Y?" → `content_opportunity`
- If the question is asked by 4+ users with no accepted answer → likely `docs_gap`
- If the discussion spawned a long debate or multiple approaches → likely `content_opportunity`

---

## Step 4 — Score Each Cluster

Read `references/scoring-guide.md` for the full formula. Summary:

```
priority_score = (
  (frequency_score  × 0.35) +
  (engagement_score × 0.30) +
  (recency_score    × 0.15) +
  (unanswered_bonus × 0.10) +
  (clarity_score    × 0.10)
) × 100
```

- `frequency_score` = cluster_thread_count / max_threads_in_any_cluster
- `engagement_score` = min((total_reactions + total_comments) / 50, 1.0)
- `recency_score` = 1.0 if any thread updated within 7 days, 0.5 if within 30 days, 0.2 if within 90 days, 0.0 otherwise
- `unanswered_bonus` = 1.0 if majority of cluster threads have `is_answered: false`, else 0.0
- `clarity_score` = your assessment of how clearly the community articulated the confusion (0.0 low, 0.5 moderate, 1.0 high)

Round all scores to the nearest integer. Do not output decimal priority scores.

---

## Step 5 — Generate Output

Read `references/output-format.md` for the exact Markdown structure.

Output up to 7 items per section, ranked by `priority_score` descending.

**Critical output rules:**
- Every item must include `source_url` — no exceptions.
- Every item must include `evidence_quote` — verbatim text from the thread, not a paraphrase.
- Do not output a generic "Suggested Action". You must do the work:
  - For Docs Gaps: Write the actual **Draft FAQ / Doc Update** as a Markdown snippet that the founder can copy-paste to solve the confusion. Base it on the accepted answer or consensus in the thread.
  - For Content Opportunities: Write the **Recommended Angle & Outline**. Define the exact angle to take and a 3-4 point outline to address the confusion.
- If the majority of threads in a cluster are unanswered, you MUST include the `**⚠️ URGENT: Unresolved Community Pain**` badge before the evidence quote.
- Do not use the words: delve, testament, comprehensive, leverage, seamless, in conclusion, it is worth noting.
- Do not claim any content performance outcome (SEO ranking, engagement rate, etc.).
- If a section has fewer than 3 items, note: "Only [N] pattern(s) found for this section. Consider broadening filters."

---

## Step 6 — Output the Run Summary Header

At the top of the report, before any sections, output:

```markdown
## Run Summary
- **Repo:** [meta.repo]
- **Analysis date:** [today's date]
- **Discussions analyzed:** [meta.total_qualifying]
- **Days of history:** [meta.days_back]
- **Clusters found:** [total clusters]
- **Mode:** [meta.mode]
```

---

## Step 7 — Save Output

Write the full Markdown report to `devrel-backlog.md` in the working directory.

Announce: "Done. Backlog written to devrel-backlog.md — [N] docs gaps and [N] content opportunities identified."

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
The fetch script uses urllib instead of a more robust HTTP library like requests, but this is not a security concern.
The skill does not explicitly warn users to keep GITHUB_TOKEN secret and avoid committing it to version control, though the script reads it from the environment.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: Varnan-Tech/opendirectory
License: MIT
Version: 1.0.0
Last GitHub push: Aug 16, 2026
Registry updated: Sep 5, 2026
Instruction path: skills/github-discussion-to-devrel-content/SKILL.md @ 62e437ab1340

Version reported in registry metadata; check source releases before relying on it.

Quality

72/100

Strong

Trust

59/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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Creator: ajaycodesitbetter
Source: Varnan-Tech/opendirectory
Indexed by: OpenAgentSkill community index

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