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- Added a new "Prompt Builder" tab in the Popup for generating AI prompts with customizable parameters. - Introduced new state variables for managing prompt styles, personas, formats, and models. - Enhanced the Options page to fetch and display models based on the provided API key. - Updated the actions and types to include the new 'prompt' action and its associated parameters. - Implemented migration logic for legacy plaintext API keys to encrypted storage. - Updated the getSystemPrompt function to incorporate prompt parameters for better instruction generation. - Added tests for the new functionality, including context menu entries and prompt generation logic.
1.9 KiB
1.9 KiB
name, description, allowed-tools
| name | description | allowed-tools |
|---|---|---|
| self-model-audit | Compare what the harness believes about the operator and project (docs/SELF_MODEL.md, CLAUDE.md, role memory) against what recent work and corrections actually reveal, and propose edits that close the gap. Use periodically or after repeated "that's not what I meant" signals. | Read Grep Glob |
Find where the harness is modeling a stale, aspirational, or simply wrong version of the operator or the project — then propose the smallest edits that make the model match reality. Read-only: propose changes, don't apply them without approval.
- Read the belief set.
docs/SELF_MODEL.md,docs/PROJECT_BRIEF.md, the operator/project instructions inCLAUDE.md, active## Lessons, and relevant role memory. Note every claim the system holds about who the operator is, what they want, and how they work. - Read the evidence. Recent handoffs (
docs/HANDOFF.md), recorded decisions (docs/DECISIONS.md), corrections captured inLESSONS_LEARNED.md, and the shape of recent tasks. Infer what the operator's actual behavior and choices reveal. - Find the gaps. Flag each place the stated model conflicts with revealed behavior: preferences that changed, aspirational goals the system optimizes for but recent work contradicts, assumptions never re-confirmed, and voice/style drift. Distinguish "genuinely stale" from "reasonable disagreement" — do not pathologize a deliberate choice.
- Propose edits. For each gap, give the exact
SELF_MODEL.md(orCLAUDE.mdinstruction) change that closes it, tied to the evidence that justifies it. Prefer removing an over-specific belief over adding more. - Return the gap list (belief → contradicting evidence → proposed edit), and route any accepted change through the operator or
system-steward. Never infer a sensitive attribute, and never store credentials, financial/health data, or anything the operator hasn't agreed to persist.