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 Eliyce/paqad-ai --skill content-plannergit clone --depth 1 https://github.com/Eliyce/paqad-aiWrote 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/eliyce/paqad-ai/content-planner)<a href="https://agentmods.dev/skills/eliyce/paqad-ai/content-planner"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/content-planner/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/eliyce/paqad-ai/content-planner"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/content-planner.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 | $0.00042 | $0.00273 |
| Opus 5 | $0.00021 | $0.00137 |
| Sonnet 5 | $0.00008 | $0.00055 |
| Haiku 4.5 | $0.00004 | $0.00027 |
Grade A, and why
content-planner 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 8d 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.
What it actually says
Content Planner
What It Does
Turns a broad content ask into a concrete brief that a writer can act on without re-asking the original requester for context.
Use This When
Use this whenever the next step would be drafting and the request lacks audience, goal, structure, or success metric.
Inputs
- The original request and any linked references.
references/planner-checklist.mdfor which fields a brief must contain.
Procedure
- Walk
references/planner-checklist.md(audience, goal, constraints, outline, dependencies, success metric); never skip a field silently. - Fill
assets/brief.template.mdwith project-specific values; mark unknowns explicitly. - Validate with
scripts/lint-brief.sh— exit 0 means every required field is present.
Output Contract
- Match
assets/brief.template.mdexactly. - Output must pass
scripts/lint-brief.sh.
Resources
references/planner-checklist.mdscripts/lint-brief.shassets/brief.template.md
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 37 lines · 42 tokens per session scan A d095bbe5fe9f
content-planner is a skill published in the GitHub repository Eliyce/paqad-ai (8 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 273 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
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auto-go
A command that implements code from a SPEC, a document describing the required behavior and work.
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Multi-agent pipeline orchestration skill.
adaptive-quality
Per-task execution profile selection based on complexity in Balanced quality mode.