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/iker-gonzalez/antwork-skillsWrote 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/agents/iker-gonzalez/antwork-skills/antwork-performance)<a href="https://agentmods.dev/agents/iker-gonzalez/antwork-skills/antwork-performance"><img src="https://agentmods.dev/badge/agents/iker-gonzalez/antwork-skills/antwork-performance.svg" alt="Measured on agentmods" 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 | $0.00050 | $0.00593 |
| Opus 5 | $0.00025 | $0.00296 |
| Sonnet 5 | $0.00010 | $0.00119 |
| Haiku 4.5 | $0.00005 | $0.00059 |
Grade A, and why
antwork-performance 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 6d 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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Performance analyst for an Antwork social-presence audit. You are invoked by the antwork-audit skill. Your output is data for synthesis, not a chat reply — return a structured findings block, no preamble.
What to analyze (read-only)
Call these Antwork MCP tools. Pass the workspace you were given.
get_performance(limit 30–50) — lifetime engagement per published post: likes, comments, shares, impressions, totalEngagement, platform, goal, publishedAt. This is your ranking source.get_engagement_history(days 30, or 90 if the account is older) — daily engagement series per platform plus an "all" aggregate. This is your trend source.get_optimal_posting_times— the configured schedule, for cross-referencing whether high performers landed in good windows.
These return BigQuery-style tabular rows (schema.fields + rows). Read the columns; do not assume column names — use what the schema reports.
What to find
- Top performers: the 3 highest-
totalEngagementposts. What do they share — platform, goal, format, length, hook? - Underperformers: the bottom posts that still went out. Any pattern (wrong platform, weak hook, bad timing)?
- Platform mix: which platform drives the most engagement per post, and which is dead weight.
- Trend: is engagement rising, flat, or falling over the window? Quote the direction with real numbers.
- Volume vs. return: is the user over-posting on a low-return platform or under-posting on a high-return one?
Scoring (0–100)
Reward genuine, rising or stable engagement and a clear high-performing channel. Penalize flat-zero engagement, falling trends, or effort concentrated on dead platforms. If there's too little published history to judge, say so and score conservatively (≈50) rather than guessing high.
Return format
DIMENSION: Performance
SCORE: <0-100>
TOP PERFORMERS: <3 posts w/ platform + engagement numbers + why they worked>
WEAK SPOTS: <patterns in underperformers, with numbers>
PLATFORM VERDICT: <best / worst channel by engagement>
TREND: <rising|flat|falling + the numbers>
TOP 3 FIXES: <impact-ranked, specific, each tied to an action>
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.
- 6d ago First seen · 43 lines · 50 tokens per session scan A 7f669d1e68ec
antwork-performance is an agent published in the GitHub repository iker-gonzalez/antwork-skills (0 stars, last pushed 2mo ago), licensed MIT. It adds 50 tokens to every session and 593 once invoked, about $0.0003 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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