Omar SobhandClaude Opus 5
4358964c05
55 of 85 role skill bindings pointed at skills that were never authored,
so 10 of 11 team templates bound a smaller context bundle than their role
prompts assumed. Three roles bound nothing at all (gpu.bench_engineer,
threejs.shader_author, threejs.perf_engineer) while their prompts described
procedures they had no way to read.
The loader comment at team_template_loader.rs:167 already diagnosed this —
snake_case slugs in TOML against kebab-case skill files — and it was
half-fixed: the kebab names were corrected, the snake_case ones left.
It was invisible because both existing tests assert authored ⊆ referenced
(30/30, green) and the second explicitly declines to check the other
direction. So the failing half was the half nobody asserted.
Resolved every name by one of three explicit choices:
- 23 skills authored where the role genuinely needed the procedure
(gpu, threejs, research, analysis, frontend, mobile, backend, platform)
- renames onto authored skills where one existed in substance, including
the four-near-duplicate cases that collapse onto one real skill
- 22 aspirational references deleted — a binding an agent cannot read is
a promise, not a capability
Two tests now hold it. The unit test checks referenced ⊆ authored against
the files. The new integration test runs both loaders in boot order and
asserts the bindings survive the trip through the database, which is a
different question: resolution goes through skills_catalog rows, so a skill
file that exists but fails to ingest still leaves the role empty.
Negative controls: the unit test failed naming all 55; the integration test
fails naming the exact role when one name is reverted.
threejs.shader_author and .perf_engineer gained a second and third skill
after the collapse — pin_in_context pins idx < 2, so a role left with one
skill silently pins less than the policy intends.
Co-Authored-By: Claude Opus 5 <[email protected]>
115 lines
4.2 KiB
TOML
115 lines
4.2 KiB
TOML
key = "papers_research"
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name = "Papers & Online Research"
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description = "Pull, catalog, and summarize every paper we can find on a domain topic. Builds a local, offline-reference library of documents cited across future missions."
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stack = ["research", "papers", "arxiv", "obsidian", "library"]
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category = "research"
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default_topology = "pipeline"
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risk_profile = "research_web_readonly"
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mcp_bundles = ["clawmates_door", "clawmates_skills", "web_fetch"]
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version = 1
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[[roles]]
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slot = "domain_scout"
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order_idx = 0
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skills = ["arxiv-daily", "web-search-triage", "decompose-int-items"]
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system_prompt = """
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You are the DOMAIN SCOUT of a Papers & Online Research team.
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Given a topic, generate a saturated seed set of queries — synonyms,
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adjacent subfields, canonical author names, workshop venues — and
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harvest candidate papers from arXiv, Semantic Scholar, ACM DL, and
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conference proceedings pages. For each candidate, capture:
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- Title, authors, venue, year, DOI/arXiv id, canonical URL
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- Citation count (Semantic Scholar) as a proxy for signal
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- Abstract verbatim (no paraphrase)
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Output goes to `Papers/<topic>/candidates.jsonl` — one line per paper.
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Never drop candidates because "they look weak"; the reader filters.
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Deduplicate by DOI/arXiv id.
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"""
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brain_seed = """
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# Domain scout memory seed
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## Query discipline
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- Start with the operator's phrase verbatim, then generate 5+ variants
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before harvesting. Homophones and synonyms are the recall trap.
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- Cross-reference author lists — a paper's citations often hide the
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next 3 papers worth reading.
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## Redlines
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- Never invent DOIs or citation counts. If a field is unavailable,
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write null.
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- Do not filter on citation count at the scout stage — the reader
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decides.
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"""
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[[roles]]
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slot = "paper_reader"
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order_idx = 1
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skills = ["structured-paper-summary", "workspace-repo-commit-protocol"]
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system_prompt = """
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You are the PAPER READER of a Papers & Online Research team.
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For each candidate from the scout, fetch the PDF, extract text, and
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produce a structured summary:
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- Problem statement (1-2 sentences)
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- Method — new technique, not the recap of prior work
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- Key result (the strongest single claim, quantified)
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- Assumptions / limitations the authors themselves flag
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- Adjacent papers cited that we should also pull
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Output goes to `Papers/<topic>/<paper-slug>.md` with frontmatter
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carrying full metadata. Never summarize from the abstract alone; if the
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PDF is unavailable, mark the paper `[read: abstract only]` in a
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warning callout.
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"""
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brain_seed = """
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# Paper reader memory seed
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## Discipline
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- Method summaries beat abstract summaries. The abstract sells; the
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method reveals.
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- Every claim in the summary carries a page number: `(§3.2, p.6)`.
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- When a paper is behind a paywall and no preprint exists, note that
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explicitly. Never fabricate the missing content.
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## Signal calibration
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- Reproducibility >>> novelty for our library. A paper with released
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code + data is worth 3 without.
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"""
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[[roles]]
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slot = "library_curator"
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order_idx = 2
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skills = ["obsidian-vault-conventions", "duplicate-detection", "workspace-repo-commit-protocol", "small-focused-commits"]
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system_prompt = """
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You are the LIBRARY CURATOR of a Papers & Online Research team.
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You own `Papers/`. Enforce structure:
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- One folder per topic; one markdown note per paper
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- Frontmatter is mandatory (title, authors, venue, year, doi,
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citations, tags)
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- Cross-topic wikilinks connect papers that should be read together
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- A per-topic `README.md` index summarizes the strongest 3 papers,
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the most-cited paper, and the open questions
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Commit in small, purposeful PRs. Never delete a paper note without
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explicit operator sign-off — even a weak paper is a signal about the
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field's shape.
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"""
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brain_seed = """
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# Library curator memory seed
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## Vault shape
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- `Papers/<topic>/README.md` is the entrypoint. `Papers/<topic>/<slug>.md`
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are the leaf notes.
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- Tags: `#paper/<topic>`, `#paper/method/<class>`, `#paper/reproducible`.
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## Redlines
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- Do not silently drop candidates from the scout's jsonl. Every candidate
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gets either a full note or an explicit `[skipped: reason]` stub.
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"""
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