skills/feedback-synthesizer/SKILL.md
Turn inbound advisor, collaborator, or reviewer feedback into structured project updates. Use when meeting notes, emails, or review comments need to become claim updates, risk entries, action items, and experiment decisions — distinct from rebuttal writing for formal reviews.
npx skillsauth add a-green-hand-jack/ml-research-skills feedback-synthesizerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Convert unstructured inbound feedback into durable project memory. This skill answers: given what an advisor, collaborator, or reviewer said, what should change in claims, risks, experiments, writing, or decisions?
Use this skill when:
Do not use this skill for formal review responses after a submission decision — use rebuttal-strategist. Do not use this skill to write the feedback update reply — use advisor-update-writer.
Pair this skill with:
rebuttal-strategist for formal reviewer responses after an acceptance/rejection decisionadvisor-update-writer to write the follow-up update or reply after synthesis is doneresearch-project-memory to propagate synthesized decisions to memory boardsexperiment-design-planner or result-diagnosis when feedback calls for new experiments or reinterpretation<installed-skill-dir>/
├── SKILL.md
└── templates/
└── feedback-record.md
memory/claim-board.md, memory/risk-board.md, and memory/action-board.md before synthesizing — feedback must be checked against existing project state.paper/.agent/paper-evidence-board.md when feedback touches paper claims or results.templates/feedback-record.md before writing a feedback record.Collect:
Identify what artifact or claim the feedback is responding to.
For each distinct feedback point, classify:
| Field | Value | |---|---| | Feedback item | (quoted or paraphrased) | | Source | advisor / collaborator / informal-reviewer / committee | | Target | claim / experiment / writing / method / data / framing / scope | | Tone | concern / suggestion / direction / question / praise | | Priority | must-address / should-address / noted / disagree | | Affects submission | yes / no / unclear |
Priority rules:
must-address: core claim is challenged, fatal weakness identified, or advisor/supervisor explicitly directed a changeshould-address: valid concern that weakens the paper or wastes compute if ignorednoted: stylistic preference, minor suggestion, or nice-to-havedisagree: feedback that conflicts with existing evidence, is based on a misunderstanding, or the team has deliberated and decided not to followFor each must-address or should-address item:
memory/claim-board.md? Update claim strength, wording, or evidence requirements.memory/risk-board.md.memory/action-board.md with priority.memory/decision-log.md.memory/evidence-board.md with status missing or planned.For disagree items: record the disagreement and the rationale for not acting. This protects the project if the same concern reappears.
Use templates/feedback-record.md.
Save to:
memory/feedback/<YYYY-MM-DD>-<source>-<topic>.md
The record should include:
advisor-update-writer: draft the reply or follow-up update after synthesisexperiment-design-planner: plan experiments triggered by must-address feedbackresult-diagnosis: reinterpret existing results in light of new concernspaper-evidence-board: update claim/evidence alignment when feedback changes what the paper needs to provepaper-writing-assistant: revise specific sections flagged by feedbackrebuttal-strategist: if formal reviews arrive with an acceptance/rejection decisionBefore finishing:
memory/feedback/ with date and sourcetesting
Bootstrap project-local ml-research-skills. Use from global installs when creating a new ML research project, enabling this collection in an existing ML research repo, or deciding whether to install the full bundle locally. Route to project-init for new projects; do not handle paper or experiment work directly.
development
Route project operations tasks — git, memory, bootstrap, remote, workspace, code review, timeline, ops — to the correct skill. Use when the task involves commits, pushes, worktrees, project memory, enabling project-local skills, SSH/server coordination, sidecar runners, or audits. Do not solve the ops task directly.
testing
Route ML/AI paper writing tasks to the correct skill — contract planning, prose drafting, section writing, consistency editing, review simulation, rebuttal, submission, or citation work. Use when the task involves writing, revising, reviewing, or submitting a paper instead of guessing between paper-writing-assistant, paper-writing-contract-planner, paper-reviewer-simulator, auto-paper-improvement-loop, or citation skills. Do not draft prose directly.
data-ai
Project-local router for ML research skill selection. Use inside an initialized ML research project, or while maintaining this skill repo, when the user describes an ML research/paper/experiment/discovery/ops/release workflow and may not know the skill; route to a domain router or high-signal leaf. Do not use for generic non-ML projects.