Agent skill

Architect Research

by DanMcInerney in DanMcInerney/architect-loop

A skill your agent uses when the user asks for discovery-scale research that informs a decision: brainstorming a project or feature, choosing a technology, or requests like "research X", "what's the…

MITAuto-check passedResearch & Science

Install Architect Research

skills CLI
$ npx skills add DanMcInerney/architect-loop --skill architect-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install DanMcInerney/architect-loop architect-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/DanMcInerney/architect-loop.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/architect-research .claude/skills/architect-research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
architect-research
GitHub stars
626
Token cost
~2.3k tokens
SKILL.md length
1,158 words
Files
2
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the user asks for discovery-scale research that informs a decision: brainstorming a project or feature, choosing a technology, or requests like "research X", "what's the…

  • Works in 7 steps: Scope → brief → Scout, then design the researchers → Fan out → …
  • The user asks for discovery-scale research that informs a decision: brainstorming a project
  • SKILL.md covers Scale before anything and Procedure
  • Calls codex

What it does

Architect Research is an agent skill from DanMcInerney/architect-loop. Use when the user asks for discovery-scale research that informs a decision: brainstorming a project or feature, choosing a technology, or requests like "research X", "what's the state of the art", or "deep research". Use this skill to turn broad, uncertain questions into sourced decision evidence and a reusable research handoff. For narrow slice-level fact checks inside the build loop, /architect handles those inline.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `tactics.md`).

It sits in Research & Science, covering Brainstorming, Deep research and Literature review. The repository describes itself as: Super optimized /goal loop. Massive token savings and higher quality. Smart model designs and reviews, cheaper model builds.. The licence is MIT.

When your agent uses it

  • The user asks for discovery-scale research that informs a decision: brainstorming a project
  • Choosing a technology
  • Requests like research X
  • Whats the state of the art

Example prompts

  • “research X”
  • “s the state of the art”
  • “deep research”
  • “/architect-research”

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Scope → brief
  2. Scout, then design the researchers
  3. Fan out
  4. Gap round (max 2 extra rounds, usually 1)
  5. Verify (your work, against raw sources)
  6. Synthesize (one pass, one author — you)
  7. Hand off

What it can do on your machine

Read from SKILL.md and the folder at commit 28dca7d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • codex

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Architect Research loads about 2.3k tokens when it runs. Until then it costs about 110 tokens; SKILL.md has 1,158 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~110
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from DanMcInerney/architect-loop at commit 28dca7d, republished under its MIT licence (© DanMcInerney). 1,158 words, ~2,297 tokens.

Download SKILL.mdSave it as .claude/skills/architect-research/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
architect-research
description
Use when the user asks for discovery-scale research that informs a decision: brainstorming a project or feature, choosing a technology, or requests like "research X", "what's the state of the art", or "deep research". Use this skill to turn broad, uncertain questions into sourced decision evidence and a reusable research handoff. For narrow slice-level fact checks inside the build loop, /architect handles those inline.
effort
high

Architect Research

You are the research orchestrator. Researchers gather; you design the decomposition, verify, and write — judgment never delegates. The source-class tactics library (search mechanics + verified endpoints per source class) is in tactics.md next to this file; read it when you design researcher assignments.

Scale before anything

A tool call is one search OR one page fetch.

  • Simple fact-find → answer directly or 1 researcher, 3-10 tool calls. Don't run a harness on a question one search answers.
  • Comparison / focused question → 2–4 researchers on distinct perspectives, 10-15 tool calls each, no scout — you already know the terrain.
  • Brainstorm / SOTA survey / technology choice → scout first, then a designed fan-out of 4–10 researchers, 15-25 tool calls each. Google's published research envelope brackets this tier: ~80 searches ≈ $1–3/task standard, ~160 ≈ $3–7 max.

Procedure

1. Scope → brief

If the question is ambiguous, ask at most 2–3 clarifying questions, then compress everything into a research brief: the question, the decision it informs, constraints, and what "answered" looks like. The brief is the north star — every later step is checked against it, and it's restated at the top of the final report so the reader can audit scope drift.

2. Scout, then design the researchers

Design researcher assignments per topic, not from a fixed taxonomy.

Scout (brainstorm scale only): dispatch ONE cheap researcher (~10 searches, same codex command as step 3) to map the terrain: canonical terminology, the 5–10 load-bearing systems/papers/repos, the named people, which source classes look rich vs empty, and the topic's natural fault lines. The scout returns a map, not findings. Skip the scout when you already know the terrain (comparisons, fact-finds).

Design (you, from the scout report): decompose into 3–10 sub-questions along the topic's own fault lines — distinct perspectives, never keyword variants of one query. For each researcher assignment pick the source-class tactics it needs from tactics.md (academic snowballing, dependents-not-stars repo evidence, production-grade pattern mining, general web, expert tracking) — one researcher may mix tactics; most topics don't need every source class. Scope each researcher to ≤5 subjects and give every researcher assignment an explicit search budget. Reserve expert opinion as a second-wave researcher: its roster (survey authors, maintainers, recurring names) comes from the first wave's findings.

Review the researcher set for overlap AND for gaps against the brief before dispatch. State the plan in a few lines; proceed unless the user redirects.

3. Fan out

Resolve the researcher model as builders, same order as /architect: repo .architect/config, then user ~/.architect/config, then the config-resolved default in skills/architect/dispatch.md — codex/best (gpt-5.5 at xhigh) via the codex CLI; the Claude-native rows (builders = claude/...) are the config-selected alternative. One fresh researcher per assignment, up to 10 in parallel for CLI launches — this is the default codex/best form:

bash
codex exec --sandbox read-only -c web_search="live" \
  -m gpt-5.5 -c model_reasoning_effort="xhigh" \
  -o .architect/research/<NN>-<researcher>.md \
  - < .architect/research/<NN>-<researcher>.prompt.md

Write each researcher block to a .prompt.md file and pass it via stdin (-) — never as a shell argument; quote-mangling shells make codex hang on stdin.

(Web search is on by default in current Codex; "live" forces fresh results. Older CLIs: --enable web_search (0.13x) or -c tools.web_search=true (< 0.133); --search is TUI-only — exec rejects it. Launch ONE canary researcher and confirm it starts cleanly before fanning out. If resolved builders is a claude row, or Codex is unavailable, run researchers as read-only Claude subagents with web search, respecting the built-in harness cap (currently 5) — the researcher blocks work verbatim.)

Every researcher block carries the full contract — objective, output format, source guidance, boundaries — plus:

  • Search budget by tier: simple 5, standard 15, deep 25 searches.
  • Saturation rule: two consecutive searches yielding no new load-bearing facts → return what you have.
  • Findings discipline: every finding has a source tag + date + exact figure or short quote + confidence tag (high = primary source / med = reputable secondary / low = single blog or forum). NOT FOUND beats inference. Disagreements between sources are reported, never resolved. No recommendations — judgment is the orchestrator's. The findings file is capped at ≤ ~2,500 tokens (~10 KB): every source URL appears EXACTLY ONCE, in a numbered source list at the end of the file, and findings cite sources by tag (e.g. [S3]).
Show full SKILL.md (494 more words)Show less
4. Gap round (max 2 extra rounds, usually 1)

After reading wave-1 findings, write (or update, on round 2) a skeleton draft of the final report at .architect/research/<topic>.draft.md (gitignored working state) — an answer-first outline where every section carries a SUPPORTED / THIN / EMPTY status against the brief. Gap researchers are designed from the THIN/EMPTY sections — the holes in the draft generate the queries, not a coverage score kept in your head. Every NOT FOUND from prior researchers carries forward into a do-not-rechase list that every gap-researcher block must include, so gap researchers don't re-spend budget chasing a dead end. This is also where the expert-opinion researcher dispatches: extract the expert roster from the first wave (survey authors, maintainers, recurring names) and send a targeted researcher after them. Hard stop after two refinement rounds — past that you're chasing nonexistent information.

5. Verify (your work, against raw sources)
  • Extract the load-bearing claims — the facts the decision depends on.
  • Require ≥2 independent sources per load-bearing claim. Independent means independent origin — two articles rewriting the same press release are one source.
  • Tag each: VERIFIED (≥2 independent agree) / UNVERIFIED (<2, no contradiction) / DISPUTED (sources disagree — report both positions and why they differ: date, method, definition) / SUSPICIOUS (contradicts available evidence).
  • Adversarial pass on the top claims: search "<claim> criticism", "<X> problems", "<X> vs <alternative>" — actively try to falsify.
  • Citations are only URLs fetched this session. Never cite from memory — even search-grounded agents fabricate 3–13% of URLs. Spot-check the load-bearing ones by fetching them yourself.
  • Recency discipline: every quantitative or current-state claim carries a source date; prefer the most recent authoritative treatment; date-restrict searches on fast-moving topics. Anything that smells like training-data leakage gets re-verified or cut.
  • Source hierarchy: primary (papers, official docs, changelogs, first-party engineering blogs) > reputable secondary > SEO listicles (pointers only, never citations).
  • Opinion ≠ fact. Expert opinions are reported as positions — quoted, dated, conflict-of-interest flagged — and never count toward the ≥2-source rule for factual claims. Expert disagreements are first-class findings: they mark the genuinely open questions.
6. Synthesize (one pass, one author — you)

Parallelize gathering, never synthesis. Write docs/research/<topic>.md:

  • Answer first (BLUF), then evidence, then method.
  • The brief, restated.
  • Per major finding: the claim + confidence tag + what it implies for the decision + what evidence would change this conclusion.
  • Disputes surfaced with both positions — never silently averaged.
  • Expert positions map: who believes what (quoted, dated, conflict-of-interest flagged), and where credible experts disagree.
  • Open questions: each UNVERIFIED/DISPUTED item with the specific search or experiment that would resolve it (this doubles as the next round's input).
  • Citations dated and tier-labeled: [primary, 2026-04].

Commit the report — this is the research handoff: its Open-questions section is the next round's input, and the repo is the memory. Raw findings stay in .architect/research/ (gitignored).

7. Hand off

A later session resumes work by reading the committed research handoff and dispatching gap researchers against its Open-questions section instead of restarting the harness. If this feeds the build loop: distill the report into docs/spec/<slice>.md per /architect and continue there.

© DanMcInerney, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/architect-research of DanMcInerney/architect-loop.

  • SKILL.md
  • tactics.md

Open the folder on GitHubat commit 28dca7d

Compare with similar skills

Architect Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Architect Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Architect Research this skillDanMcInerney/architect-loop626—~2.3kAutomated safety check: PassMIT
Deep Research Agent TeamImbad0202/academic-research-skills51k—~13kAutomated safety check: PassCustom licence
Deep Researchbrycewang-stanford/Auto-Empirical-Research-Skills4.5k—~8kAutomated safety check: PassCustom licence
Lead Researchtamdogood/builder-essential-skills220—~2.1kAutomated safety check: PassMIT
Deep Research WorkflowTokenRhythm/opensquilla7.1k—~1.3kAutomated safety check: PassApache-2.0
Deep Researchsanjay3290/ai-skills4319 repos~683Automated safety check: NotesApache-2.0

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Questions about Architect Research

What does Architect Research do?

A skill your agent uses when the user asks for discovery-scale research that informs a decision: brainstorming a project or feature, choosing a technology, or requests like "research X", "what's the…. Architect Research is an agent skill from DanMcInerney/architect-loop. Use when the user asks for discovery-scale research that informs a decision: brainstorming a project or feature, choosing a technology, or requests like "research X", "what's the state of the art", or "deep research".

When should I use Architect Research?

Architect Research fits situations like: the user asks for discovery-scale research that informs a decision: brainstorming a project; choosing a technology; requests like research X; whats the state of the art.

How do I install Architect Research in Claude Code?

Run `npx skills add DanMcInerney/architect-loop --skill architect-research -a claude-code`. Or copy the skill folder (skills/architect-research in DanMcInerney/architect-loop) into .claude/skills/architect-research in your project. Claude Code loads it when a task matches its description.

How do I install Architect Research in Codex?

Run `npx skills add DanMcInerney/architect-loop --skill architect-research -a codex`. Or copy the skill folder (skills/architect-research in DanMcInerney/architect-loop) into .agents/skills/architect-research in your project. Codex loads it when a task matches its description.

Can I use Architect Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add DanMcInerney/architect-loop --skill architect-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/architect-research, .gemini/skills/architect-research, .github/skills/architect-research and .opencode/skills/architect-research in your project.

What does Architect Research need to run?

Going by SKILL.md and its folder, Architect Research needs the command-line tools its instructions call (codex).

Does Architect Research access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Architect Research safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Architect Research use?

Architect Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Architect Research use?

About 2.3k tokens (SKILL.md is roughly 9.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Architect Research?

Skills that share tags, products or a category with Architect Research: Deep Research Agent Team (Imbad0202/academic-research-skills, 51k stars), Deep Research (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars), Lead Research (tamdogood/builder-essential-skills, 220 stars) and Deep Research Workflow (TokenRhythm/opensquilla, 7.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Architect Research?

DanMcInerney (a GitHub user) maintains it in DanMcInerney/architect-loop, which has 626 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on September 13, 2026.

Source: DanMcInerney/architect-loop on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.