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 agentmods add skills/maxtechera/ship/measurenpx skills add maxtechera/ship --skill measuregit clone --depth 1 https://github.com/maxtechera/shipWhat 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 | $0.00020 | $0.00707 |
| Opus 5 | $0.00010 | $0.00353 |
| Sonnet 5 | $0.00004 | $0.00141 |
| Haiku 4.5 | $0.00002 | $0.00071 |
Grade A, and why
content-measure 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 2d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Measure
Measurement produces evidence and next actions — not just numbers.
When to Use
- 24-48 hours after publishing (day-1 snapshot)
- 7 days after publishing (week-1 snapshot)
- When comparing performance across a batch of assets
- To close the feedback loop before the next production cycle
Inputs Required
- Published asset URLs/permalinks
- Analytics access (GA4, platform native insights, or manual metrics)
- Measurement window (24h / 72h / 7d)
Required Outputs
- Metrics snapshots per live asset — date range + source table (not estimated)
- Asset score — relative performance vs baseline or prior batch
- Winner/loser list — top 3 performers, bottom 2 underperformers
- Feedback events — what to change next, mapped to a specific action
Metrics to Capture (by Platform)
| Platform | Key metrics |
|---|---|
| Instagram Reel | Views, reach, saves, shares, profile visits, retention % at 3s/midpoint |
| Instagram Carousel | Impressions, reach, saves, link clicks |
| YouTube Short | Views, watch time %, subscribers gained |
| Blog | Sessions, avg time on page, scroll depth, CTA clicks |
| Open rate, click rate, unsubscribes | |
| Landing page | Sessions, form submissions, conversion rate |
Scoring
Score each asset against two baselines:
- Your average — how does this compare to your own past 30 days?
- Batch average — how does this compare to other assets in the same batch?
Score: above / on-par / below (qualitative) + the specific metric gap
Feedback Events Format
Asset: [URL or ID]
Result: [metric] [value] ([above/below] [baseline by X%])
Pattern: [what this tells us — specific, not generic]
Action: [what to do differently next time — specific, actionable]
Routed to: [copy approach / hook formula / posting time / format choice]
Example:
Asset: reel/2026-04-10-claude-roi
Result: 3s retention 45% (below batch avg of 62%)
Pattern: Talking-head opener drops retention faster than visual/text opener
Action: Next 3 reels — open with text overlay or screen capture, not face
Routed to: storyboard → hook section → visual-first rule
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.
- 2d ago First seen · 84 lines · 20 tokens per session scan A e12117660b53
content-measure is a skill published in the GitHub repository maxtechera/ship (2 stars, last pushed 4mo ago), licensed MIT. It adds 20 tokens to every session and 707 once invoked, about $0.0001 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 2026-08-31.
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