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 commands/saigonxiii/evc/performance-reviewgit clone --depth 1 https://github.com/SaigonXIII/evcWrote 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/saigonxiii/evc/performance-review)<a href="https://agentmods.dev/commands/saigonxiii/evc/performance-review"><img src="https://agentmods.dev/badge/commands/saigonxiii/evc/performance-review.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 | $0.00000 | $0.00695 |
| Opus 5 | $0.00000 | $0.00347 |
| Sonnet 5 | $0.00000 | $0.00139 |
| Haiku 4.5 | $0.00000 | $0.00069 |
Grade A, and why
performance-review 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 3d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Review
Weekly performance analysis of published content. Run on Friday PM or Monday AM before planning.
Trigger
User invokes /content-review or asks to "review this week's content", "how did our posts perform", "content analysis"
Arguments
- Optional: week number (e.g.,
/content-review W13). Defaults to current week.
Steps
1. Gather Published Content
Read the output folder for the specified week:
content-engine/output/[week]/- If no output folder exists, check
brand/templates/social/exports/for recent exports - Read any X copy, LinkedIn copy, and banner files produced
2. Analyze Against Plan
Read the week's brief from content-engine/calendar/ and check:
- Did we execute all 5 planned posts?
- Did we hit the right audience segments per day?
- Did the narrative arc hold (each post building on the previous)?
- Any posts that deviated from the plan? Why?
3. Pull Live X Metrics
Fetch engagement data for the last 7 days of {{SOCIAL_HANDLE_X}} posts using whichever metrics surface the user has wired:
- If an X/Twitter MCP is installed: call its metrics/analytics tools to pull per-tweet impressions, likes, retweets, replies, and engagement rate.
- If the X API is wired via another integration: use that.
- Otherwise: prompt the user to paste the week's metrics (from X Analytics) or to enable a metrics integration before continuing.
You need per-tweet impressions, likes, retweets, replies, engagement rate, and the top-performing post.
Then:
- Match tweets to content calendar days by
created_atdate - Rank posts by engagement rate
- Identify which pillar/audience/template performed best
- Compare against previous weeks if past review files exist in
content-engine/calendar/ - Calculate week-over-week trends (impressions, engagement rate)
- Flag any day that had no post (missed schedule)
4. Narrative Arc Review
- Read the past 2-3 weeks of content briefs
- Assess whether the overall narrative is building and compounding
- Identify gaps: are we over-indexing on one pillar or audience?
- Suggest the narrative direction for next week
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.
- 3d ago First seen · 74 lines · 0 tokens per session scan A eebbcd710ed3
performance-review is a command published in the GitHub repository SaigonXIII/evc (56 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 695 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 2026-08-30.
Other commands, from other repositories
review-tool
Research a tool or product for skill-gap, architecture, and verb-fit per the Anatomy framework.
setup
First-run setup — replace the fictional company and authors with yours.
review-content
Review content for brand alignment, lead placement, bio consistency, and proof points.
voice-check
Check a draft against the author's voice profile and flag deviations.
content-status
Scan all content directories and report status of drafts, ideas, and published content.
draft-blog
Scaffold a blog post with proper structure, voice, and frontmatter.