Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add phazonoverload/devadvokit --skill find-series-opportunitiesgit clone --depth 1 https://github.com/phazonoverload/devadvokitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/phazonoverload/devadvokit/find-series-opportunities)<a href="https://agentmods.dev/skills/phazonoverload/devadvokit/find-series-opportunities"><img src="https://agentmods.dev/badge/skills/phazonoverload/devadvokit/find-series-opportunities/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/phazonoverload/devadvokit/find-series-opportunities"><img src="https://agentmods.dev/badge/skills/phazonoverload/devadvokit/find-series-opportunities.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.00919 |
| Opus 5.5 | $0.00018 | $0.00368 |
| Sonnet 5.5 | $0.00009 | $0.00184 |
| Haiku 4.5 | $0.00005 | $0.00092 |
Grade A, and why
find-series-opportunities scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Before doing anything else:
- Check if
~/.devadvokit.mdexists. - If it does, read it silently and use it throughout this skill.
- If it does not, stop and tell the user: "I need your DevRel context before I can run this skill. Please run /setup-devadvokit first."
What NOT to Do
This skill produces content strategy — not content calendars or editorial schedules. Specifically:
- No publication dates — that's an editorial planning skill
- No content calendar — this is about theme discovery and arc completion
- No SEO analysis — this is about conceptual relationships, not search optimization
The goal is finding the series hiding in your existing work.
How This Skill Works
This skill reads your content library from ~/.devadvokit.md and finds patterns across it. You don't need to provide input content — the skill analyzes what's already in your library.
Output
Produce all of the following after reading the content library.
1. Thematic Clusters
Content grouped by underlying theme (not just surface topic):
For each cluster (2+ pieces identified):
- Cluster name: [The underlying theme that connects them]
- Pieces in this cluster:
- "[Title]" ([Year]) - what angle it takes
- "[Title]" ([Year]) - what angle it takes
- The connection: [Why these belong together beyond being the same topic]
- Series potential: [Low/Medium/High — whether this could become an intentional series]
Identify 2–4 clusters minimum.
2. Repeated Points from Different Angles
Where you've made the same core point without realizing it:
For each repeated point found:
- The recurring insight: [The point you keep making]
- Where it appears:
- "[Title]" ([Year]) - how you framed it
- "[Title]" ([Year]) - how you framed it
- What's different each time: [How the framing shifts]
- Series candidate? [Yes/No — whether these could be consolidated or sequenced]
3. Incomplete Arcs
Where you wrote part 1 and part 3, but part 2 is missing:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 115 lines · 46 tokens per session scan A 0ef8cebf4b5e
find-series-opportunities is a skill published in the GitHub repository phazonoverload/devadvokit (16 stars, last pushed 7mo ago), licensed MIT. It adds 46 tokens to every session and 919 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
find-series-opportunities compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| find-series-opportunitiesphazonoverload/devadvokit | Skill | 16 | 7mo ago | MIT | A | 46 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |