perf-feedback-rehearsal

perf-feedback-rehearsal is a command for Claude Code from frankxai/Starlight-Intelligence-System. It costs 0 tokens per session (4,063 once invoked), scanned A, original, MIT.

A preparation plan for a difficult feedback conversation, using a description of the situation and information about the person receiving the feedback. It provides possible openings, likely reactions, replies, alternative conversation paths, and aftercare.

In plain words
What is it for?
Use it to rehearse feedback, prepare responses to different reactions, adjust the conversation for the recipient, and decide when a formal performance or legal process is more appropriate.
Why use it?
It lets a manager think through a high-stakes conversation before delivering feedback about a recurring problem. It can make the response less improvised when the other person reacts strongly.

Command for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the starlight-intelligence-system plugin — 6 skills, 121 commands, 7 agents shipped together

Good fit Use it to rehearse feedback, prepare responses to different reactions, adjust the conversation for the recipient, and decide when a formal performance or legal process is more appropriate.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/frankxai/starlight-intelligence-system/perf-feedback-rehearsal
Install

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.

Clone the repo
git clone --depth 1 https://github.com/frankxai/Starlight-Intelligence-System

Made for: Claude Code.

Or install starlight-intelligence-system, the plugin that ships this one along with the rest of its 6 skills, 121 commands, 7 agents.

Wrote 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.

agentmods badge for perf-feedback-rehearsal

README.md
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Your own site
<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.

agentmods 80×15 button for perf-feedback-rehearsal

Your own site · 80×15
<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>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,063 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash 9d3948a24ab1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

.claude/commands/perf-feedback-rehearsal.md · 272 lines

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

  1. 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 to memory/_audit/<date>.jsonl.

Read the full file on GitHub · 272 lines

Changes

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.

  1. 11d ago First seen · 272 lines · 0 tokens per session scan A 9d3948a24ab1

Subscribe to this mod's changes

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.