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Claude Feedback Quote Ledger
by monna
Capture scattered client comments as a verbatim, source-linked quote ledger before revision decisions distort the evidence.
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See it in action
You say
Create a quote ledger from two sources. Email — round 2: “Please move the pricing note above the table. Could we also use the earlier headline?” Chat note from Maya: “The pricing section is approved.” Preserve exact wording and flag missing references. Redact Maya's name.
Your agent does
ARTIFACT: CLAUDE-FEEDBACK-QUOTE-LEDGER MODE: SOURCE-BOUND
SOURCE REGISTER | SRC-01 | Email — round 2 | NOT SUPPLIED | Email | NOT SUPPLIED | — | | SRC-02 | Chat note | [REDACTED] | Chat | NOT SUPPLIED | Name redacted |
QUOTE LEDGER | SRC-01-Q01 | SRC-01 | Please move the pricing note above the table. | EXPLICIT REQUEST | pricing note / table | ANCHORED | NONE | | SRC-01-Q02 | SRC-01 | Could we also use the earlier headline? | QUESTION | headline | MISSING ANCHOR | NONE | | SRC-02-Q01 | SRC-02 | The pricing section is approved. | APPROVAL-LIKE | pricing section | ANCHORED | NONE |
OPEN CONTEXT GAPS
- SRC-01-Q02: “earlier headline” does not identify a version or source.
BOUNDARY No contradiction resolution, scope decision, prioritization, implementation plan, or client reply was produced.
What you get
About this skill
Capture the evidence before planning revisions
Client feedback usually arrives across email, chat, document comments, and meeting notes. Copying it into a task list too early can erase the exact wording, source, and missing context.
Claude Feedback Quote Ledger creates a source-bound intake artifact before revision planning begins. It registers every supplied source, splits comments into atomic items, preserves verbatim wording, distinguishes visible requests from questions, and flags unresolved references without inventing the missing context.
What you receive
- Stable source and item IDs
- Exact quotes with no silent polishing
- Conservative request, question, observation, and approval-like labels
- Explicit artifact targets when visible
- Missing-anchor flags for phrases such as “the earlier one”
- User-controlled redaction handling
- Coverage counts and seven quality gates
- A clear boundary against scope decisions and premature prioritization
Best for
- Freelancers collecting revision notes from several channels
- Agencies preparing feedback before assigning work
- Claude project users who need traceable client evidence
- Teams worried summaries lose exact wording
Boundaries
This focused entry product handles evidence capture only. It does not reconcile contradictory reviewers, decide scope, prioritize changes, rewrite the deliverable, or draft a client reply.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Creator
6 skills on Agensi
I design high-value AI prompts, reasoning systems, and practical workflows for people who need more than generic outputs. My work focuses on turning complex tasks into structured AI systems for research, education, RAG, validation, decision-making, systems engineering, and business workflows. I build prompts that are designed to: • reduce ambiguity and weak reasoning • ground outputs in evidence and clear constraints • detect gaps, contradictions, and misconceptions • guide AI through complex multi-step decisions • produce structured, usable deliverables instead of generic answers I’m especially interested in RAG, ontology-driven reasoning, evidence validation, adaptive learning, and AI workflow design. If you’re looking for prompts that do real analytical work—not just better wording—you’re in the right place.
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