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.
git clone --depth 1 https://github.com/revaya-ai/revaya-aios-workspace-templateWrote 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/commands/revaya-ai/revaya-aios-workspace-template/capture)<a href="https://agentmods.dev/commands/revaya-ai/revaya-aios-workspace-template/capture"><img src="https://agentmods.dev/badge/commands/revaya-ai/revaya-aios-workspace-template/capture/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/commands/revaya-ai/revaya-aios-workspace-template/capture"><img src="https://agentmods.dev/badge/commands/revaya-ai/revaya-aios-workspace-template/capture.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 | $0.00000 | $0.00663 |
| Opus 5 | $0.00000 | $0.00331 |
| Sonnet 5 | $0.00000 | $0.00133 |
| Haiku 4.5 | $0.00000 | $0.00066 |
Grade A, and why
capture 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 11d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/capture — Quick Content Idea Capture
Capture a content idea and classify it as a stub in the pipeline.
Variables
$ARGUMENTS (the raw idea, topic, or observation to capture)
Instructions
You are capturing a content idea into the pipeline. Quick classification, duplicate check, store as stub. For full concept development, use /develop.
Step 1: Understand the Idea
Extract the core in 1-2 sentences from the user's input.
Step 2: Check for Duplicates
.venv/bin/python3 -c "
import sys, sqlite3; sys.path.insert(0, '.')
from scripts.content.db import get_connection
conn = get_connection()
rows = conn.execute(\"\"\"
SELECT id, title, production_status FROM content_ideas
WHERE title LIKE '%KEYWORD%' ORDER BY created_at DESC LIMIT 5
\"\"\").fetchall()
for r in rows: print(f' #{r[\"id\"]} [{r[\"production_status\"]}] {r[\"title\"]}')
if not rows: print(' No duplicates found.')
conn.close()
"
Replace KEYWORD with the most distinctive word from the idea.
If duplicates exist, tell the user and ask if they want to proceed or develop the existing one instead.
Step 3: Classify
Read content/strategy.md to understand their platform, pillars, and audience segments.
Determine:
- Channel: linkedin / youtube (primary or secondary platform for this idea)
- Format: Appropriate format for that channel (from strategy.md format types)
- Content pillar: Which of the 4 pillars this falls under (Operational Problems / Lived Experience / Practical Value / Personal Journey)
- Audience segment: Drowning Operator / AI-Curious Builder / Claude Power User
- Funnel position: awareness / consideration / conversion
Present the classification to the user for quick confirmation (one line: "LinkedIn post, Operational Problems pillar, ICP, awareness — sound right?").
Step 4: Store as Stub
After confirmation:
.venv/bin/python3 -c "
import sys; sys.path.insert(0, '.')
from scripts.content.db import get_connection
from scripts.content.writer import write_content_idea
idea = {
'title': 'TITLE_HERE',
'description': 'DESCRIPTION_HERE',
'channel': 'CHANNEL',
'format_type': 'FORMAT',
'source_type': 'manual',
'content_pillar': 'PILLAR',
'audience_segment': 'SEGMENT',
'funnel_position': 'POSITION',
'notes': 'NOTES',
}
conn = get_connection()
idea_id = write_content_idea(conn, idea)
conn.close()
print(f'Stored as stub #{idea_id}')
"
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.
- 11d ago First seen · 94 lines · 0 tokens per session scan A b29cea8b50a3
capture is a command published in the GitHub repository revaya-ai/revaya-aios-workspace-template (2 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 663 tokens. 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 2026-08-31.
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