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/frankxai/Starlight-Intelligence-SystemWrote 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/frankxai/starlight-intelligence-system/perf-feedback-rehearsal)<a href="https://agentmods.dev/commands/frankxai/starlight-intelligence-system/perf-feedback-rehearsal"><img src="https://agentmods.dev/badge/commands/frankxai/starlight-intelligence-system/perf-feedback-rehearsal/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/frankxai/starlight-intelligence-system/perf-feedback-rehearsal"><img src="https://agentmods.dev/badge/commands/frankxai/starlight-intelligence-system/perf-feedback-rehearsal.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 | $0.00000 | $0.04063 |
| Opus 5 | $0.00000 | $0.02031 |
| Sonnet 5 | $0.00000 | $0.00813 |
| Haiku 4.5 | $0.00000 | $0.00406 |
Grade A, and why
perf-feedback-rehearsal 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 11d 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 — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/perf-feedback-rehearsal
Load SIP.md, VOICES.md, agents/starlight-performance.md, skills/people-intelligence/feedback-conversations.md, and the manager's Genius Profile if available. Produce a Feedback Rehearsal — multiple openings, SCARF-aware adjustments, three reaction trees with the manager's response to each, and aftercare. Save to people-intelligence/performance/rehearsal-<topic-slug>-<YYYY-MM-DD>.md.
Disclaimer (non-waivable)
This is rehearsal architecture for a high-stakes feedback conversation. It is NOT legal counsel — if this feedback is part of a documentation chain leading to termination, demotion, or formal discipline, route to /perf-difficult-conversation and have the documentation reviewed by employment counsel before delivery. It is NOT psychotherapy — if the recipient is in clinical-level distress, route to clinical support, not feedback. The rehearsal goes stale after 7 days; deliver soon or rehearse again.
Input
$ARGUMENTS
When this command fires
- A manager has feedback to deliver and the conversation is non-trivial
- The feedback addresses a recurring pattern, not a one-off
- The recipient is likely to react strongly (history of defensiveness, status threat, tenure imbalance)
- The manager has not delivered feedback like this before and wants a rehearsed structure
When this command does NOT fire
- The feedback is trivial ("hey, the deck title was misspelled") — no rehearsal needed; just SBI in the moment
- The conversation is termination, demotion, or formal discipline → route to
/perf-difficult-conversation - The conversation is conflict between two parties → route to
/perf-conflict-mediation - The recipient is in clinical-level distress → refuse rehearsal and route to clinical
- The "feedback" is really a PIP scaffolding for termination → refuse and route to honest termination conversation
Process
- Recall prior rehearsals (memory handshake).
- Before generating, check the SIS memory layer for prior rehearsals on the same topic or with the same manager/recipient pair. The substrate-aware orchestrator routes via the People Intelligence namespace.
- Run from the SIS repo root:
cd private/voice-operator && MSYS_NO_PATHCONV=1 python -m service.memory.cli recall \ --query "<topic-slug> <manager-slug> <recipient-slug>" \ --k 3 \ --namespace people-intelligence/perf \ --source /perf-feedback-rehearsal - Parse the JSON output. If hits surface, treat them as continuity context: reference the prior pattern, note what changed, avoid repeating dead-ends. If no hits, proceed without prior context — first rehearsal in this lineage.
- This is the People Intelligence dog-food gate for v0.1 of the SIS memory orchestrator (see
skills/memory/sis-memory-orchestrator/SKILL.md). The recall is logged tomemory/_audit/<date>.jsonl.
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.
- 11d ago First seen · 272 lines · 0 tokens per session scan A 9d3948a24ab1
perf-feedback-rehearsal is a command published in the GitHub repository frankxai/Starlight-Intelligence-System (8 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,063 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 2026-08-31.
Other commands, from other repositories
agent
Create and manage custom AI agents.
memories
View and manage learned memories.
mpm-session-resume
Load context from paused session.
learn
End-of-session learning loop — analyze signals, approve instincts, capture directives, refresh MEMORY.md. Invokes dreamteam learn; one source of behavioral truth shared with the team.md SESSION LEARNING step.
forget
Delete specific memories.
learn
Add new learning to memory.