Chalk Labs
Need help deploying peer-review-critique?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/shiien-peer-review-critique
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: data
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/peer-review-critique
What's inside
- Skill name
peer-review-critique- Description
- Stage 6 of the peer-review pipeline. Compiles findings from stages 1-5 into an AAAI-format draft review, then performs a self-critique pass (check for unsupported claims, factual errors, incomplete citations), then revises the draft into the final review. When run_config.yaml has recommendation=yes, also emits a venue-level-aware accept/weak accept/weak reject/reject recommendation. Invoked by the `peer-review` orchestrator; standalone triggers include "compile these review findings into a review", "self-critique this draft review".
- License
- MIT
- Instructions
- 87 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# peer-review-critique
Combined compile + self-critique + revise stage. This is one skill because the three sub-steps are cheap to keep together and share context — matching the paper's Initial Review → Self-Critique → Final Review Revision loop.
## Inputs
- `paper_pdf`, `paper_md`, `paper_id`
- All prior stage outputs: `01_story.md`, `02_presentation.md`, `03_evaluations.md`, `04_correctness.md`, `05_significance.md`
- `shared/base_instruction.md`, `shared/review_schema.md`, `shared/output_format.md` (required final format)
- `shared/review_level.yaml` (for the level's `critical_threshold`, `major_threshold`, `required_qualities`)
- `run_config.yaml` (to look up `level` and `recommendation` flag; and `reviewer_backgrounds[i]` when running under a reviewer sub-agent)
- `level_bar.md` (appended to `base_instruction.md` under `## Level bar`; affects how you weigh findings)
## Process (three sub-steps, one skill)
### Sub-step A: Compile draft
1. Read all 5 stage outputs.
2. Deduplicate overlapping findings.
3. Assemble a draft in AAAI format per `shared/output_format.md`:
- Title (derived from paper's main claim)
- Synopsis (1-2 sentences)
- Summary (contribution, method, results)
- Strengths (pull the paper's genuine merits from what each stage chose NOT to flag)
- Weaknesses (every finding from stages 1-5, severity-tagged)
- References (external works cited by `significance` stage, in APA)
4. Write draft to a scratch buffer (not disk yet).
### Sub-step B: Self-critique
5. Re-read the draft alongside the original paper_md.
6. Check for:
- Unsupported claims in the review (every weakness must cite stage evidence)
- Factual errors (numbers, section/equation refs that don't exist)
- Incomplete citations (every APA entry must have author/year/venue)
- Severity inflation (is that really [critical] or just [major]?)
- **`vote_moving_weakness`** (from `shared/taste-priors.md`) — every `[major]` or `[critical]` weakness must be phrased so the authors can tell what concrete evidence would resolve or downgrade it. A weakness the authors cannot act on is a complaint, not a review; rewrite it to end with a trailing `Resolution:` clause that names the specific evidence or clarification that would reduce the severity, or drop it.
7. Produce a critique list (mental or scratch).
### Sub-step C: Revise (and emit recommendation)
8. Apply all critique fixes to the draft.
9. Verify all six AAAI section headings are present verbatim.
10. **Recommendation** (only if `run_config.yaml` has `recommendation: yes`):
- Aggregate severity counts across stages 01–05 (sum the `severity_counts` YAML frontmatter from each `NN_<stage>.md`).
- Load `shared/review_level.yaml[<level>]` for `critical_threshold`, `major_threshold`, `required_qualities`.
- Apply the decision logic documented in `shared/output_format.md` (§ "Decision mapping"):
```
if critical_count >= level.critical_threshold:
rec = reject
elif major_count > level.major_threshold:
rec = weak_reject
elif any required_quality is missing per stage findings:
rec = weak_reject
elif level == best_paper and minor_count > 3:
rec = weak_accept
elif (critical + major + minor) == 0:
rec = accept
else:
rec = weak_accept
```
For `oral` / `best_paper`: if neither the `story` nor `significance` stage surfaces a
finding supporting a "novel insight" AND the paper's contribution reads as
incremental, degrade the result by one step (accept→weak accept, weak accept→weak
reject).
- Append a `## Recommendation` section to the review. The first non-blank line is the
literal token (`accept` / `weak accept` / `weak reject` / `reject`). The next line
begins with `Justification:` and cites (a) the aggregate severity counts, and (b)
the top 1–2 weakness bullets (by severity) that drove the decision — especially
when the recommendation is `weak reject` or `reject`.
11. Write final to `outputs/peer-review/<paper_id>/06_final.md` (or `reviewer_<i>/06_final.md` when invoked from a reviewer sub-agent).
## Tools
None (just Read/Write).
## Output
`outputs/peer-review/<paper_id>/06_final.md` (or `reviewer_<i>/06_final.md`) — must conform to `shared/output_format.md`. Unlike stage outputs 01-05, this file is the **final review itself**, not a findings file. When `recommendation: yes`, it also contains a `## Recommendation` section after `## References`.
## Failure modes to avoid
- Do NOT include findings that contradict each other without adjudication.
- Do NOT drop `[critical]` findings when revising — only clarify or re-sever.
- Do NOT add new content in Sub-step C that wasn't in the draft or the critique list, except for the `## Recommendation` section.
- Do NOT emit a recommendation when `run_config.yaml` has `recommendation: no` (or the file is absent) — in that case, omit the section entirely.
- Do NOT invent severity counts; always aggregate from the stage findings' YAML frontmatter.
## Tests
`tests/test_critique.sh` — runs on mock stage outputs with a seeded structural gap (a required final-review section missing). The stage's output must contain all six AAAI headings verbatim, and, when recommendation is enabled in the run config, must include a `## Recommendation` section whose first non-blank line is exactly one of the four allowed labels.From Shiien/Self-Evolved-Research-Framework, MIT.
More from Shiien/Self-Evolved-Research-Framework
- 0.534score
paper-read
Shiien
Read a research paper (arXiv link, PDF, or pasted text) and produce structured reading notes saved to resources/papers/.
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
experiment-dse
Shiien
Design-space exploration — systematic hyperparameter sweep over a search space defined by an `experiment-plan`.
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
checklist
Shiien
All checklist-tree write operations in one mode-based skill — CREATE (add a leaf or branch task with L2 sub-checklist / paper audit template), UPDATE (mark…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
decision-analyze
Shiien
Structured analysis of a decision — for each option list pros/cons, effort, risk, milestone alignment, and reversibility, then produce a recommendation (user…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
code
Shiien
Code-workflow scaffolding in one mode-based skill — BRANCH (git branch/worktree isolation with exp|feat|fix prefixes), ROADMAP (step-by-step implementation…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
experiment-plan
Shiien
Design the full experimental spec before any runs — map paper claims to experiments, define independent / dependent variables, pin baselines, schedule…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
experiment-analyze
Shiien
The EVALUATE stage of the research loop.
SkillCFreshness: C★ 7Claude Code +9 - 0.530score
experiment-monitor
Shiien
Thin wrapper over `python -m harness ext-status` for monitoring external GPU runs.
SkillCFreshness: C★ 7Claude Code +9 - 0.530score
experiment-run
Shiien
Launch a training / experiment run.
SkillCFreshness: C★ 7Claude Code +9 - 0.469score
paper-lit-search
Shiien
Multi-source literature search — query arXiv + Semantic Scholar + the local `resources/papers/` library for a topic, dedupe across sources, rank by relevance…
SkillDFreshness: D★ 7Claude Code +9 - 0.469score
peer-review-correctness
Shiien
Stage 4 of the peer-review pipeline.
SkillDFreshness: D★ 7Claude Code +9 - 0.469score
peer-review-evaluations
Shiien
Stage 3 of the peer-review pipeline.
SkillDFreshness: D★ 7Claude Code +9
Signals
- Stars
- 7
- Forks
- 1
- Open issues
- 0
- Last commit (this folder)
- Apr 24, 2026 (6 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from Shiien
- 0.534score
checklist
Shiien
All checklist-tree write operations in one mode-based skill — CREATE (add a leaf or branch task with L2 sub-checklist / paper audit template), UPDATE (mark…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
experiment-dse
Shiien
Design-space exploration — systematic hyperparameter sweep over a search space defined by an `experiment-plan`.
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
code
Shiien
Code-workflow scaffolding in one mode-based skill — BRANCH (git branch/worktree isolation with exp|feat|fix prefixes), ROADMAP (step-by-step implementation…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
experiment-plan
Shiien
Design the full experimental spec before any runs — map paper claims to experiments, define independent / dependent variables, pin baselines, schedule…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
paper-read
Shiien
Read a research paper (arXiv link, PDF, or pasted text) and produce structured reading notes saved to resources/papers/.
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
decision-analyze
Shiien
Structured analysis of a decision — for each option list pros/cons, effort, risk, milestone alignment, and reversibility, then produce a recommendation (user…
SkillCFreshness: C★ 7Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get peer-review-critique running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
peer-review-critique skill. Agent Mart. https://agentmart.dev/skills/shiien-peer-review-critique (last updated 2026-10-24)