sayed/research-pipeline

Produce a rigorous, verified, fully-cited research report on ANY topic by running a disciplined five-stage pipeline: CLARIFY the question, PLAN (an editable research plan), FAN-OUT parallel search subagents, VERIFY every key claim (cross-source + adversarial + cross-model), then SYNTHESIZE a cited report. Use this WHENEVER the user wants real research rather than a quick answer — "research X", "do a deep dive on Y", "write me a report / brief / literature review on Z", "compare A vs B and back it with sources", "what's the state of the art in …", "investigate …", "find evidence for/against …", "give me a market/landscape/competitor analysis", or any question where being wrong is costly and the answer needs citations. Trigger even when the user does not say the word "research" but clearly needs multi-source, fact-checked, cited output. This is the general-topic web-research orchestrator — not the codebase-to-wiki `deep-research` skill. For a fast single-fact lookup, answer directly instead.

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research-pipeline

Name: sayed/research-pipeline
Rating: 75.2 (1 reviews)
Author: sayed

A Claude Code agent skill that produces rigorous, verified, fully-cited research reports on any topic by running a disciplined five-stage pipeline:

CLARIFY → PLAN (editable) → FAN-OUT (parallel subagents)
        → VERIFY (source-grounded · adversarial · cross-model)
        → SYNTHESIZE (cited report)      [checkpoint/resume throughout]

Why it exists

Every serious research system (Anthropic's multi-agent Research, Google Deep Research, OpenAI Deep Research, GPT Researcher) is the same loop. This skill makes that loop explicit and adds a layered verification discipline so the product is a report you can defend, not a fast guess.

What's inside

File	Purpose
`SKILL.md`	The five-stage loop, the claim-ledger backbone, effort tiers, stop conditions, anti-patterns
`references/playbook.md`	Per-stage procedure, subagent brief, shared research brief, ledger format
`references/verification.md`	Source-grounded → adversarial → cross-model protocol (honest about what cross-model buys)
`references/evidence-standards.md`	Per-domain evidence ladders, high-stakes handling, known limitations
`references/report-template.md`	The cited-report structure
`scripts/checkpoint.py`	Self-describing resume state (`state.json`)
`scripts/verify_citations.py`	Mechanical report ↔ ledger consistency gate (anti "citation theater")

Key ideas

Claim ledger — every load-bearing claim is a row (claim → quoted passage → URL → date → status). The report is generated from the ledger, and verify_citations.py checks they stay consistent.
Cross-model verification is an adjunct, not ground truth — a different vendor re-reads your digest, not the live web, so it's a cheap hallucination-smell check layered on top of source-grounded verification.
Effort tiers (quick / standard / deep) scale subagent count and verification depth to the stakes.
Graceful degradation — cross-model CLIs are optional; a missing/blocked vendor never stalls a run.

Install

Copy the research-pipeline/ directory into ~/.claude/skills/, or install the packaged .skill file.

Provenance

Designed and hardened with cross-vendor review (codex + agy) on both the design and actual output reports.

.tessl-plugin

references

scripts

README.md

SKILL.md

tile.json