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/chrisdbaldwin/masques/performancegit clone --depth 1 https://github.com/ChrisDBaldwin/MasquesWhat 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 | $0.00019 | $0.01508 |
| Opus 5 | $0.00010 | $0.00754 |
| Sonnet 5 | $0.00004 | $0.00302 |
| Haiku 4.5 | $0.00002 | $0.00151 |
Grade A, and why
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 yesterday.
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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Command
Show how the always-on audience scored a session. Output has two layers (PRD D4):
- Layer A — the house reaction (always, from session one): a single
7-point verdict —
perfect · great · good · neutral · bad · awful · detracting. An honest read of how the session went, never a claim that the masque caused it. - Layer B — lift (only once earned): how the masque compares to your own baseline corpus on the same task-class — a delta, never a bare level. Hidden until the baseline is thick enough. The audience never shows a misleading number.
This replaces the old five-proxy "masque verdict." Those proxies were
masque-agnostic — they scored the same with or without a masque — so they are
demoted to supporting_signals (context), never the verdict (PRD D7).
Instructions
Step 1: Check Prerequisites
- Judge script present:
test -f ${CLAUDE_PLUGIN_ROOT}/services/judge/judge.sh && echo ok || echo missing
- DuckDB present:
command -v duckdb >/dev/null && echo ok || echo missing
If missing: "Scoring requires DuckDB. Install: brew install duckdb." 3. Telemetry data present:
test -f ${CLAUDE_PLUGIN_ROOT}/services/collector/data/logs.jsonl && echo ok || echo "no data"
If no data: the audience hasn't captured anything yet — "Run /audience seat
to open the house (it stays open), then start a session." Note capture needs
the Claude Code OTEL env (see /audience seat).
Step 2: Identify the session to score
By default the judge scores the most recent session. To score this
session explicitly, pass its id (which is also what /don records for
attribution):
echo "$CLAUDE_CODE_SESSION_ID"
Optionally read .claude/masque.session.yaml to mention the active masque by
name in your framing.
Step 3: Run the judge
TARGET_SESSION="$CLAUDE_CODE_SESSION_ID" ${CLAUDE_PLUGIN_ROOT}/services/judge/judge.sh
(Omit TARGET_SESSION to score the most recent session.) Optional knobs:
BASELINE_MIN=N— baseline sessions per task-class before Layer-B lift shows (default 5).RUBRIC_BAND=<band>— if a rubric judge (an LLM pass, or a Witness-masque agent — D7) has read the session against the masque'srubricand produced a band, pass it here; the judge will use it for Layer A (judge: rubric) instead of the activity fallback.
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.
- yesterday First seen · 159 lines · 19 tokens per session scan A b3a5879faff4
performance is a command published in the GitHub repository ChrisDBaldwin/Masques (14 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 19 tokens to every session and 1,508 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 2026-08-30.
Other commands, from other repositories
brand-setup
Configure a new brand profile with colors, fonts, logo, visual style, platforms, and compliance rules.
save
Save this conversation as a new or existing reusable context.
screens
Structures product UX screen design — inventory, flow, states, and reusable briefs for external design tools — without generating pixels or UI code.
graphify
Turn your vault into a clustered knowledge graph with HTML and JSON outputs.
review
Cold re-quiz on code that already shipped — your own session commits, not the change in front of you.
think-expand-options
Break past the obvious option set by reframing the problem, transforming the seed with SCAMPER, and reversing its foundational assumptions, then shortlisting candidates.