Borrowing it
Nothing to install: this file belongs to htlin222/robust-lit-review. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/htlin222/robust-lit-review/main/.claude/skills/lit-review/SKILL.mdgit clone --depth 1 https://github.com/htlin222/robust-lit-reviewWrote 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.
[](https://agentmods.dev/skills/htlin222/robust-lit-review/lit-review)<a href="https://agentmods.dev/skills/htlin222/robust-lit-review/lit-review"><img src="https://agentmods.dev/badge/skills/htlin222/robust-lit-review/lit-review/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.
<a href="https://agentmods.dev/skills/htlin222/robust-lit-review/lit-review"><img src="https://agentmods.dev/badge/skills/htlin222/robust-lit-review/lit-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.03265 |
| Opus 5 | $0.00015 | $0.01632 |
| Sonnet 5 | $0.00006 | $0.00653 |
| Haiku 4.5 | $0.00003 | $0.00327 |
Grade A, and why
robust-lit-review 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.
How it starts
The opening of the file, as written. The whole thing — 335 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Robust Literature Review Pipeline
You are orchestrating a world-class automated systematic literature review. This is the single entry point for all operations.
Available Commands
The user can request any of these modes:
| Command | What it does |
|---|---|
/lit-review |
Full pipeline: topic -> search -> filter -> validate -> write -> render |
/lit-review --hitl |
Full pipeline with Human-in-the-Loop mode (9 checkpoints) |
/brainstorm-topic |
Brainstorm and refine search terms before running |
Human-in-the-Loop Mode (--hitl)
When enabled, the pipeline pauses at 9 checkpoints where human judgment matters most.
Each checkpoint presents multiple-choice options via AskUserQuestion.
from litreview.pipeline.checkpoints import (
format_checkpoint_for_user, CheckpointLog,
cp1_search_strategy, cp2_borderline_articles, cp3_final_article_set,
cp4_thematic_grouping, cp5_key_claims, cp6_prisma_audit,
cp7_cover_letter, cp8_final_preview, cp9_publish_decision,
)
| CP | When | Why human needed | Default (auto-mode) |
|---|---|---|---|
| CP1 | After query generation | Wrong query = wrong review | Strategy A |
| CP2 | After filtering | Borderline articles need domain judgment | Review individually |
| CP3 | After selection | Missing landmark papers? Topic imbalance? | Approve |
| CP4 | Before writing | Thematic structure shapes the narrative | Approve structure |
| CP5 | After writing | LLM may hallucinate stats/dosing/p-values | All correct |
| CP6 | After PRISMA audit | Some items may be legitimately N/A | Auto-fix all |
| CP7 | Cover letter | Target journal affects framing | Approve |
| CP8 | Before render | Last quality gate | Render |
| CP9 | Before publish | Public action requires consent | Publish |
How to implement each checkpoint:
# 1. Generate checkpoint
cp = cp1_search_strategy(topic, suggested_queries)
# 2. Present to user (if --hitl enabled)
prompt_text = format_checkpoint_for_user(cp)
# Use AskUserQuestion tool with prompt_text
# 3. Record decision
cp.selected = user_response # "A", "B", "C", etc.
log.record(cp)
# 4. Branch pipeline based on selection
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.
- 11d ago First seen · 335 lines · 31 tokens per session scan A 370ec9ded026
robust-lit-review is a skill published in the GitHub repository htlin222/robust-lit-review (55 stars, last pushed 2d ago), licensed MIT. It adds 31 tokens to every session and 3,265 once invoked, about $0.0002 per session on Opus 5. 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 2026-08-30.
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