Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ShipWithAI/shipwithai-pluginsnpx agentmods add commands/shipwithai/shipwithai-plugins/ui-metricsWrote 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/shipwithai/shipwithai-plugins/ui-metrics)<a href="https://agentmods.dev/commands/shipwithai/shipwithai-plugins/ui-metrics"><img src="https://agentmods.dev/badge/commands/shipwithai/shipwithai-plugins/ui-metrics/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/shipwithai/shipwithai-plugins/ui-metrics"><img src="https://agentmods.dev/badge/commands/shipwithai/shipwithai-plugins/ui-metrics.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 | $0.00036 | $0.00479 |
| Opus 5.5 | $0.00014 | $0.00192 |
| Sonnet 5.5 | $0.00007 | $0.00096 |
| Haiku 4.5 | $0.00004 | $0.00048 |
Grade A, and why
ui-metrics 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 1mo 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
Show whether the harness is getting better over time. This is read-only analysis — the work is in the deterministic script; you just run it and interpret.
1. Compute
python3 harness/bin/metrics_ledger.py # writes harness/ledger/metrics.md (gitignored)
Read harness/ledger/metrics.md.
2. Interpret the three numbers
- Iterations-to-pass per screen (↓): how many loop iterations a screen needed before it stopped surfacing findings. Falling over time means prevention (rails, gates, reused components) is catching things earlier. A screen that needs 4 every time is a candidate for a structural fix.
- First-pass yield (↑): share of screens that came in with zero Tier-1 structural blockers on the first render. Rising means the upstream rails (Konsist boundaries, tokens, designsystem reuse) are working — fewer broken-before-tasteless first drafts.
- Recurrence after deposit (→ 0): for each promoted category, how many findings appeared after it was deposited. This is the key health signal. Zero = the promotion stuck. Non-zero (flagged ⚠️) = the deposit isn't holding — the finding slipped past the gate you added, so harden it further down the ladder (rubric line → deterministic assertion → structural impossibility). Recurrence rising after a promotion is the cue to re-open it in
/ui-distill.
3. Act on it
If any category is flagged ⚠️, that's the most valuable thing the metrics surfaced: re-run /ui-distill for it and push the deposit one rung deeper. Everything else is trend-watching — re-run after each batch of /ui-iterate work; the ledger is the durable history, this report is the current snapshot.
These need data to be meaningful — they read what
/ui-iterate(capture) and/ui-distill(deposit) write. On an empty ledger the report just says so.
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.
- 1mo ago First seen · 27 lines · 36 tokens per session scan A 180cec0f2b5d
ui-metrics is a command published in the GitHub repository ShipWithAI/shipwithai-plugins (9 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 479 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other commands, from other repositories
specmanager-build
Build one phase of a SpecManager feature's plan via the builder subagent. Stops at the phase boundary; never advances.
debug
Systematic debugging with hypotheses and evidence gathering.
review-skills
Review changed skills: automated bash checks + 9 structural dimensions (D1-D9) + 5 intent checks (M1-M5). PASS/FAIL verdict. Fix in-place.
execute
Execute the next available tracked task with TDD, pre-commit validation, PR creation, AI code review, and issue tracker bridge sync.
ccc-orchestrate
Run a sequential multi-agent workflow (feature, bugfix, refactor, security or custom chain) with structured handoffs, plus optional tmux and worktree workers for long-running sessions. Use when a task needs planner, implementer and reviewer stages in order.
ccc-theme
List, preview or switch the Commander terminal color theme (Claude, OLED Black, Matrix, or a random palette) used by hooks and terminal art. Use when the user asks to change the theme, colors or skin.