Research Assist

Research Assist Skill

An OpenClaw skill that turns Zotero evidence into a profile-driven literature digest, using arXiv by default and optionally expanding recall with OpenAlex and Semantic Scholar before deduplication.

CLI Usage

# Full digest: profile check → literature retrieval → rank → markdown output
uv run --project ~/.openclaw/skills/research-assist \
  research-assist --action digest --config ~/.openclaw/skills/research-assist/config.json

# Ad-hoc literature search
uv run --project ~/.openclaw/skills/research-assist \
  research-assist --action search --query "gaussian process" --top 5

# Check profile refresh status
uv run --project ~/.openclaw/skills/research-assist \
  research-assist --action profile-refresh --config ~/.openclaw/skills/research-assist/config.json

# Zotero MCP server (for profile evidence + feedback writeback)
uv run --project ~/.openclaw/skills/research-assist research-assist-zotero-mcp

Or via Python module:

uv run --project ~/.openclaw/skills/research-assist \
  python -m codex_research_assist --action digest --config ~/.openclaw/skills/research-assist/config.json

Default config path: ~/.openclaw/skills/research-assist/config.json

During install or reconfiguration, do not embed the full setup questionnaire in this file. Use references/setup-routing.md as the install-time interaction guide, ask only the questions relevant to the user's goal, then edit config.json directly.

Install-Time Behavior

Installation and reconfiguration are one-time operations.

Hard rules for the host agent:

• use references/setup-routing.md only when the user is installing, reconfiguring, or when required config is missing
• once config.json is valid, normal digest/search/render/feedback runs must not reopen setup questions
• do not restate dormant install options during regular literature work
• if the user asks for normal runtime work, prefer using the existing config over discussing installation
• when setup selects optional backends or delivery routes, execute the required install/setup commands instead of only listing them
• before leaving setup, run a minimal verification for the selected backend or route and report the result
• only fall back to manual instructions when a step is blocked by missing secrets, missing local services, missing permissions, or a platform limitation

Config Format

{
  "profile_path": "~/.openclaw/skills/research-assist/profiles/research-interest.json",
  "output_root": "~/.openclaw/skills/research-assist/reports",
  "retrieval_defaults": {
    "max_results_per_interest": 20,
    "since_days": 7,
    "max_age_days": 7
  }
}

Architecture

config.json (OpenClaw skill config)
    ↓
openclaw_runner.py (CLI entry, markdown to stdout)
    ├── profile_refresh_policy  → check if profile needs update
    ├── pipeline.py             → multi-source literature retrieval (arXiv default)
    ├── ranker.py               → two-signal scoring (map_match + zotero_semantic)
    └── format_*_markdown()     → structured markdown output

No LLM calls inside the packaged Python pipeline. Retrieval, ranking, and formatting are pure data operations. Intelligence comes from the calling agent (OpenClaw / Claude Code / Codex CLI).

Profile refresh should be handled by the OpenClaw controller or agent layer, using live Zotero evidence via the bundled Zotero MCP.

Workflow Stages

1. profile_update

• read the current Zotero evidence base when refresh is required
• maintain profiles/research-interest.json
• preserve the compact contract: method_keywords, query_aliases, exclude_keywords
• keep method labels short and retrieval-friendly
• prefer zotero_semantic_search for discovery, then zotero_search_items for exact resolution
• use research-assist-zotero-mcp for live Zotero reads (no direct API calls)

OpenClaw generation rule:

• treat Zotero like a studio palette, not a flat folder dump
• use collection structure as the sketch of the research map
• use representative papers as the main evidence for what each region actually contains
• use semantic search as the blending layer that connects nearby themes across collections
• write interests that feel like stable method axes, not loose keyword bags
• aim for about 6 interests by default; usually stay in the 4-8 range unless the evidence strongly says otherwise
• if the draft has too few interests, split mixed regions by real method differences; if it has too many, merge nearby regions that share one stable method axis
• if collection names and paper content disagree, trust the papers more than the folder label
• if summary terms are frequent but too generic, use them only to refine wording, not to define the map
• the final profile should read like a compact map of the user's research territory: a few clear regions, each with short labels and retrieval-friendly aliases

2. retrieval

• query arXiv by default, optionally add OpenAlex and Semantic Scholar per interest
• expand the paper pool before ranking when multiple sources are enabled
• generate structured candidate JSON with full provenance
• deduplicate across interests and across enabled sources

3. review

• rank candidates with two-signal scoring:
  • map_match (0.30): how well the paper fits the current research-map slices
  • zotero_semantic (0.70): how close the paper is to nearby Zotero literature
• apply the low-map guard:
  • if map_match < 0.30, apply the configured penalty to avoid semantic-only false positives
• output ranked markdown to stdout for agent review
• prefer a smaller sharper set over a noisy dump
• stay abstract-first

Digest Enrichment

• OpenClaw should treat agent-filled review as the default digest-enrichment path
• after retrieval, let the host agent enrich the top-ranked candidate JSON files with review patches
• use review_generation.agent_top_n to cap how many ranked candidates the host agent needs to inspect
• let the host agent decide the final visible subset by setting review.selected_for_digest
• use review_generation.final_top_n as the hard upper bound for the final rendered digest
• keep fallback_to_system enabled unless the user explicitly wants hard failure instead of fallback text
• after patches are applied, re-render the digest so HTML / Telegram outputs use the enriched review text
• why_it_matters should sound like a recommendation, not a provenance report
• caveats should capture real uncertainty or scope boundaries, not generic hedging
• the host agent should also fill review.zotero_comparison, including nearest-neighbor fallback when candidate-level evidence is missing
• keep nearest-neighbor output compact: usually 1-2 items
• the host agent is not responsible for email / telegram wrapper copy, subjects, or routing

Delivery Routing

• use one shared delivery path and branch at the end with delivery.primary_channel
• default primary channel is email; telegram is backup or alternate primary
• channel wrappers are system-owned:
  • email subject/body/profile card/stat cards
  • telegram compact message shell
• do not ask the host agent to generate channel-specific wrappers

Stage 6: feedback_sync

After the digest is reviewed and delivered, the host agent may push non-destructive feedback back into Zotero.

Workflow:

1. collect the user's explicit feedback on each digest candidate (keep, drop, archive, watch, etc.)
2. encode feedback as reports/schema/zotero-feedback.schema.json
3. call zotero_apply_feedback through the bundled Zotero MCP with dry_run=true first
4. show the dry-run plan to the user and ask for confirmation before applying
5. only after confirmation, re-run with dry_run=false

Allowed feedback decisions:

• read_first — high-priority paper, tag and promote in library
• skim — worth scanning, tag for later review
• watch — track this topic area, add to watchlist collection
• skip_for_now — not relevant now, mark but do not remove
• archive — reviewed and filed, move to archive collection
• watchlist — add to a standing watchlist for periodic check-in
• ignore — not relevant, tag to suppress in future runs
• unset — no decision yet, skip writeback for this item

What feedback can do (non-destructive only):

• add tags (including ra-status:* decision tags)
• add or change collection membership
• append notes to items
• create new collections if needed for organization

What feedback must never do:

• delete Zotero items or collections
• modify item metadata (title, authors, abstract, DOI)
• move or delete attachment files
• rewrite top-level taxonomy without explicit user instruction
• apply changes without showing the dry-run plan first

Matching behavior:

• match items by item_key (preferred), doi, or title_contains
• at least one match field must be provided per decision
• DOI matching is case-insensitive
• title_contains uses substring match (not exact)
• if no match is found, the decision is recorded as not_found in the plan

Edge cases:

• duplicate tags are deduplicated (case-insensitive)
• previous ra-status:* tags are replaced when a new decision is applied
• the research-assist system tag is always preserved
• unset decisions produce no status tag and no writeback
• empty add_tags, remove_tags, add_collections, remove_collections are allowed (no-op for that field)

Hard Rules

• do not expand concise method labels into long topic sentences
• do not make full text the default review mode
• do not delete Zotero items or collections automatically
• prefer dry_run=true for any Zotero writeback
• do not treat scheduler wiring as part of the skill

Key Runtime Files

• OpenClaw runner: src/codex_research_assist/openclaw_runner.py
• Ranker: src/codex_research_assist/ranker.py
• Pipeline: src/codex_research_assist/arxiv_profile_pipeline/pipeline.py
• Example config: config.example.json
• Example profile: profiles/research-interest.example.json

Reference Documents

• references/workflow.md — stage order and controller boundary
• references/contracts.md — profile contract and review policy
• references/distribution.md — packaging include/exclude rules
• references/setup-routing.md — install-time route selection and option questions
• references/review-generation.md — system vs agent_fill review contract
• references/profile-map-generation.md — how to turn Zotero evidence into a research-map-style profile

Packaging Boundary

Include in distributable skill:

• SKILL.md, config.example.json, pyproject.toml, uv.lock
• src/
• references/
• profiles/research-interest.example.json
• automation/arxiv-profile-digest.example.toml
• automation/prompts/
• reports/schema/
• generated package-root install.sh

Exclude:

• generated reports, temporary state
• local secret config
• scheduler wrappers
• repository planning documents (NEXT_PLAN.md, CODEMAP.md)

Source: zhanglg12/research-assist — distributed by TomeVault.