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Upgrade Feynman research runtime and setup

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Advait Paliwal
2026-03-20 23:37:38 -07:00
parent 6332c3c67c
commit be97ac7a38
22 changed files with 1271 additions and 60 deletions
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56
skills/research/autoresearch/SKILL.md Normal file
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---
name: autoresearch
description: Use this when the user wants an end-to-end idea-to-paper run, from problem framing through literature, experiments if feasible, and a paper-style draft.
---
# AutoResearch
## When To Use
Use this skill when the user wants:
- an idea turned into a paper-style draft
- a full research workflow, not just a memo or reading list
- autonomous progress from topic framing to deliverable
## Procedure
1. Restate the idea as a concrete research question and identify the likely contribution type:
- empirical result
- synthesis or review
- method proposal
- benchmark or audit
2. Search for relevant primary sources first.
3. If the topic is current, product-oriented, market-facing, or asks about latest developments, start with `web_search` and `fetch_content`.
4. Use `alpha_search`, `alpha_get_paper`, and `alpha_ask_paper` for academic background or paper-centric parts of the topic.
5. Build a compact evidence table in `notes/` or `outputs/` before deciding on the paper narrative.
6. Decide whether experiments are feasible in the current environment:
- if yes, design and run the smallest experiment that materially reduces uncertainty
- if no, continue with a literature-grounded or theory-grounded draft and state the limitation clearly
7. Produce at least two artifacts:
- an intermediate artifact (research memo, evidence table, or experiment log)
- a final paper-style draft in `papers/`
8. Structure the final draft with:
- title
- abstract
- introduction
- related work
- method or synthesis
- evidence or experiments
- limitations
- conclusion
9. End with a `Sources` section containing direct URLs for every source used.
## Pitfalls
- Do not jump straight to drafting before checking the literature.
- Do not treat a current topic as if papers alone are enough.
- Do not fake experiments when the environment cannot support them.
- Do not present speculative contributions as established results.
- Do not omit limitations or missing validation.
## Deliverable
A complete idea-to-paper run should leave behind:
- one intermediate artifact in `notes/` or `outputs/`
- one final paper-style draft in `papers/`
- a source list with direct URLs

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skills/research/context-recall/SKILL.md Normal file
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---
name: context-recall
description: Use this when the user asks what was done before, refers to earlier sessions, wants prior artifacts, or expects Feynman to remember past work.
---
# Context Recall
## When To Use
Use this skill when the user:
- asks what was done previously
- refers to an earlier paper, memo, or artifact
- expects cross-session continuity
- asks what has already been tried or written
## Procedure
1. Read durable memory first with `memory_search` or `memory_lessons`.
2. Search prior sessions with `session_search`.
3. If needed, inspect the current workspace for artifacts in `outputs/`, `notes/`, `experiments/`, and `papers/`.
4. Distinguish clearly between:
- durable remembered facts
- session transcript recall
- currently present files on disk
5. If you find a stable correction or preference that should persist, save it with `memory_remember`.
## Pitfalls
- Do not claim to remember something without checking memory or session history.
- Do not confuse durable memory with transient task progress.
- Do not summarize prior work from vague impressions; recover evidence first.
## Deliverable
Include:
- what was previously done
- where the evidence came from
- which artifacts or files exist now
- any gaps or uncertainty

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skills/research/deep-research/SKILL.md Normal file
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---
name: deep-research
description: Use this when the user wants a broad, thorough investigation with strong sourcing, explicit evidence tables, and a durable research brief.
---
# Deep Research
## When To Use
Use this skill when the user wants:
- a thorough investigation rather than a quick memo
- a broad landscape analysis
- careful source comparison across multiple source types
- a durable research brief with explicit evidence
## Procedure
1. Clarify the exact scope and what decision or question the research should support.
2. Choose the right retrieval mix:
- use `web_search` and `fetch_content` first for current, product, market, regulatory, or latest topics
- use `alpha_search`, `alpha_get_paper`, and `alpha_ask_paper` for academic background or paper-centric claims
- use both when the topic spans current reality and academic literature
3. Gather enough high-quality sources before synthesizing.
4. Build an evidence table covering:
- source
- claim
- evidence type
- caveats
- relevance
5. Synthesize:
- strongest findings
- disagreements
- open questions
- what would change the conclusion
6. Save a durable markdown brief to `outputs/`.
7. End with a `Sources` section containing direct URLs for every source used.
## Pitfalls
- Do not answer a current topic from papers alone.
- Do not answer an academic topic from search snippets alone.
- Do not collapse disagreement into fake consensus.
- Do not omit the evidence table on broad or high-stakes topics.
## Deliverable
Include:
- scope
- evidence table
- key findings
- disagreements or caveats
- open questions
- recommendation or next step
- sources

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skills/research/experiment-design/SKILL.md
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- success metrics
- baselines
- constraints
3. Search for prior work first with `alpha_search` so you do not reinvent an obviously flawed setup.
4. Use `alpha_get_paper` and `alpha_ask_paper` on the strongest references.
5. Prefer the smallest experiment that can meaningfully reduce uncertainty.
6. List confounders and failure modes up front.
7. If implementation is requested, create the scripts, configs, and logging plan.
8. Write the plan to disk before running expensive work.
3. Search for prior work first.
4. If the setup is tied to current products, APIs, model offerings, pricing, or market behavior, use `web_search` and `fetch_content` first.
5. Use `alpha_search`, `alpha_get_paper`, and `alpha_ask_paper` for academic baselines and prior experiments.
6. Prefer the smallest experiment that can meaningfully reduce uncertainty.
7. List confounders and failure modes up front.
8. If implementation is requested, create the scripts, configs, and logging plan.
9. Write the plan to disk before running expensive work.
## Pitfalls

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skills/research/literature-review/SKILL.md
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## Procedure
1. Search broadly first with `alpha_search`.
2. Pick the strongest candidates by direct relevance, recency, citations, and venue quality.
3. Inspect the top papers with `alpha_get_paper` before making concrete claims.
4. Use `alpha_ask_paper` for missing methodological or experimental details.
5. Build a compact evidence table:
1. Search broadly first.
2. If the topic is primarily academic or paper-centric, start with `alpha_search`.
3. If the topic includes current products, companies, markets, software, or "latest/current" framing, start with `web_search` and `fetch_content`, then use `alpha_search` only for academic background.
4. Pick the strongest candidates by direct relevance, recency, citations, venue quality, and source quality.
5. Inspect the top papers with `alpha_get_paper` before making concrete claims.
6. Use `alpha_ask_paper` for missing methodological or experimental details.
7. Build a compact evidence table:
- title
- year
- authors
- venue
- claim or contribution
- important caveats
6. Distinguish:
8. Distinguish:
- what multiple sources agree on
- where methods or findings differ
- what remains unresolved
7. If the user wants a durable artifact, write a markdown brief to disk.
8. If you discover an important gotcha about a paper, save it with `alpha_annotate_paper`.
9. End with a `Sources` section that lists direct URLs, not just titles.
9. If the user wants a durable artifact, write a markdown brief to disk.
10. If you discover an important gotcha about a paper, save it with `alpha_annotate_paper`.
11. End with a `Sources` section that lists direct URLs, not just titles.
## Pitfalls
@@ -41,6 +43,7 @@ Use this skill when the user wants:
- Do not flatten disagreements into fake consensus.
- Do not treat recent preprints as established facts without saying so.
- Do not cite secondary commentary when a primary source is available.
- Do not treat a current product or market topic as if it were a paper-only topic.
## Output Shape

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skills/research/reading-list/SKILL.md
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## Procedure
1. Start with `alpha_search` in `all` mode.
2. Inspect the strongest candidates with `alpha_get_paper`.
3. Use `alpha_ask_paper` for fit questions like:
1. Start with source discovery that matches the topic.
2. For academic topics, use `alpha_search` in `all` mode.
3. For current, product-oriented, or market-facing topics, use `web_search` and `fetch_content` first, then use `alpha_search` for background literature if needed.
4. Inspect the strongest candidates directly before recommending them.
5. Use `alpha_ask_paper` for fit questions like:
- what problem does this really solve
- what assumptions does it rely on
- what prior work does it build on
4. Classify papers into roles:
6. Classify papers or sources into roles:
- foundational
- key recent advances
- evaluation or benchmark references
- critiques or limitations
- likely replication targets
5. Order the list intentionally:
7. Order the list intentionally:
- start with orientation
- move to strongest methods
- finish with edges, critiques, or adjacent work
6. Write the final list as a durable markdown artifact in `outputs/`.
7. For every paper, include a direct URL.
8. Write the final list as a durable markdown artifact in `outputs/`.
9. For every source, include a direct URL.
## Pitfalls
- Do not sort purely by citations.
- Do not over-index on recency when fundamentals matter.
- Do not include papers you have not inspected at all.
- Do not force everything into papers when the user actually needs current docs, products, or market sources.
## Deliverable

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skills/research/research-memo/SKILL.md
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## Procedure
1. Find relevant sources first.
2. Inspect the strongest sources directly before synthesizing.
3. Separate:
2. If the topic is current, product-oriented, market-facing, or asks about latest developments, use `web_search` and `fetch_content` first.
3. If there is an academic literature component, use `alpha_search` and inspect the strongest papers directly.
4. Inspect the strongest sources directly before synthesizing.
5. Separate:
- established facts
- plausible inferences
- unresolved questions
4. Write a memo with clear sections and a concise narrative.
5. End with a `Sources` section containing direct links.
6. Save the memo to `outputs/` when the user wants a durable artifact.
6. Write a memo with clear sections and a concise narrative.
7. End with a `Sources` section containing direct links.
8. Save the memo to `outputs/` when the user wants a durable artifact.
## Pitfalls
- Do not summarize from search snippets alone.
- Do not omit the source list.
- Do not present inference as fact.
- Do not rely on paper search alone for latest/current topics.
## Deliverable