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github-discussion-to-devrel-content

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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Precio sin confirmar★ 635 Estrellas de GitHubRegistro actualizado · 5 sept 2026agent-skill

Resumen

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.

Leer documentación completa

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

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."

Ver fuente ↗

Metadatos del archivo

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

Ver texto original

---
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."

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Precio y costes de ejecución

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Ejecutarlo: Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
Licencia: MIT
Precio sin confirmar: No hemos confirmado el precio. Los enlaces existentes al código y a la instalación siguen disponibles.

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Fuente del skill registrada

La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.

Revisar antes de instalar: Evitar instalación automática

Licencia: 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

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Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.

Empieza con una tarea pequeña

1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.

Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

Indexado

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente: Varnan-Tech/opendirectory
Licencia: MIT
Versión: 1.0.0
Último push de GitHub: 16 ago 2026
Registro actualizado: 5 sept 2026
Ruta de instrucciones: skills/github-discussion-to-devrel-content/SKILL.md @ 62e437ab1340

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

72/100

Sólido

Confianza

59/100

Do not auto-install

Auditoría

75/100

Requiere revisión

Abrir auditoría completa Ver informe de evaluación

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

Verified installs: —
Resultados: —

Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.

Acceso para agentes

La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.

Abrir API de instalación Formato de texto LLM Abrir Manifest Plan de resolución automática

Más detalles

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  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 72,
    "label": "Strong"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "The fetch script uses urllib instead of a more robust HTTP library like requests, but this is not a security concern.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "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"
  ],
  "agent_contract": {
    "task_input": "Use github-discussion-to-devrel-content in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 67/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 35/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "varnan-tech-github-discussion-to-devrel-content (github-discussion-to-devrel-content)",
      "install_command": "npx skills add Varnan-Tech/opendirectory --skill github-discussion-to-devrel-content",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "varnan-tech-github-discussion-to-devrel-content",
      "task": "Use github-discussion-to-devrel-content in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/varnan-tech-github-discussion-to-devrel-content",
    "api": "https://www.openagentskill.com/api/agent/skills/varnan-tech-github-discussion-to-devrel-content",
    "audit": "https://www.openagentskill.com/skills/varnan-tech-github-discussion-to-devrel-content/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=varnan-tech-github-discussion-to-devrel-content&task=Use%20github-discussion-to-devrel-content%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20github-discussion-to-devrel-content%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20github-discussion-to-devrel-content%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/varnan-tech-github-discussion-to-devrel-content/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/varnan-tech-github-discussion-to-devrel-content"
  }
}

Para el creador

Fuente de la ficha

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Creador: ajaycodesitbetter
Fuente: Varnan-Tech/opendirectory
Indexado por: Índice comunitario de OpenAgentSkill

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