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-content-anglesgit 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-content-angles)<a href="https://agentmods.dev/skills/phazonoverload/devadvokit/find-content-angles"><img src="https://agentmods.dev/badge/skills/phazonoverload/devadvokit/find-content-angles/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-content-angles"><img src="https://agentmods.dev/badge/skills/phazonoverload/devadvokit/find-content-angles.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 | $0.00058 | $0.00902 |
| Opus 5.5 | $0.00023 | $0.00361 |
| Sonnet 5.5 | $0.00012 | $0.00180 |
| Haiku 4.5 | $0.00006 | $0.00090 |
Grade A, and why
find-content-angles 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 — 103 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 angles, frameworks, and directions — not finished content. Specifically:
- No full drafts — you're finding the seeds of future pieces, not writing them
- No SEO keyword research — that's a different skill for a different workflow
- No social media posts — use
/repurpose-talkfor that
The goal is content strategy discovery, not content production.
Q&A
Ask these questions one at a time. Wait for each answer before asking the next.
-
What's the content piece? Paste or describe it — blog post, talk abstract, case study, docs PR, etc. This is your source material.
-
What was the core insight or lesson here, in one sentence? (What's the one thing you want people to take away?)
-
Who was the original audience for this piece? (The people you wrote it for the first time.)
-
What response did this get when you published it? (Comments, questions, "but what about X" — these are clues for angles you didn't explore.)
Output
Produce all of the following once the Q&A is complete.
1. Abstraction Ladder
Start with the specific problem you solved, then climb up 3 levels of generalization:
- Level 1 (specific): [What you actually did]
- Level 2 (pattern): [What this is an instance of]
- Level 3 (principle): [What principle this demonstrates]
- Level 4 (universal): [What universal truth this points to]
Include one sentence on when each level is the right framing for a new piece.
2. Audience Pivots
The same insight reframed for different readers. Generate 3 pivots:
- Pivot 1: [Different job role perspective]
- Pivot 2: [Different experience level perspective]
- Pivot 3: [Different context/industry perspective]
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 · 103 lines · 58 tokens per session scan A 443a3a2d738e
find-content-angles is a skill published in the GitHub repository phazonoverload/devadvokit (16 stars, last pushed 7mo ago), licensed MIT. It adds 58 tokens to every session and 902 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-content-angles compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| find-content-anglesphazonoverload/devadvokit | Skill | 16 | 7mo ago | MIT | A | 58 | 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 |