Generates an experiment-overview snapshot of all research questions under research/reports/. Invoke when a new point-in-time report across all RQs should be produced.
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Build the results site and publish it to GitHub Pages. Checks every precondition deploy-site.py enforces before spending a build on them, presents the research/ diff the build produces for a commit, and deploys only on explicit confirmation. Trigger when the user says "publish", "deploy", "site deployen", "gh-pages", "auf die Seite bringen", or asks how to get results online.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
The lab has two publishing paths and they are not alternatives:
| Path | Produces | Entry point |
|---|---|---|
| this skill | the live site on the gh-pages branch, one page per RQ | pnpm deploy:site |
/build-overview | a frozen Markdown snapshot under research/reports/ | generate-snapshot-skeleton.py |
Do not reach for one when the user asked for the other. "Snapshot", "report",
"Bericht", "Stand" → /build-overview. "Site", "online", "deploy", "Pages" →
here.
export-site-data.py globs research/questions-*/*/README.md. So the site
carries the per-harness and cross-harness RQs only — research/workflow-dev/
and research/kata-dev/ are deliberately not published yet. If the user expects
a workflow-development RQ on the site, say that it is out of scope rather than
hunting for a bug.
Each RQ page shows its findings.md as written, behind a warning banner. There
is no separate review step, so a findings file that contradicts its own data
goes online as it stands. Worth a sentence to the user when you know of one.
deploy-site.py pushes to a public branch. Never run it without the user
saying so in this conversation. Building is free and reversible; deploying is
not. When the user asks "how do I publish", answer with the commands and stop —
do not deploy to demonstrate.
deploy-site.py refuses on any of these, and it refuses after running its own
build, so checking first saves the build. Report them as a block:
cd /path/to/agentic_coding_lab
git rev-parse --abbrev-ref HEAD # must be main
git merge-base --is-ancestor HEAD origin/main && echo pushed || echo NOT-pushed
git status --porcelain -- research site | wc -l # must be 0
command -v uv || echo "uv MISSING"
test -d site/node_modules || echo "site deps MISSING"
Two of these are the traps:
site/node_modules is not in the repo. Without it the Astro build dies
with astro: not found and build-site.py exits 1 after already
re-aggregating every RQ. Fix: cd site && pnpm install --frozen-lockfile.
Do not worry about the host's pnpm version — corepack honours the
packageManager pin in site/package.json and uses that one.uv is the documented interpreter and may be absent. Both pnpm scripts
are uv run --project ../experiments python .... Install it the way the
Dockerfile does, at the same pin, rather than reaching for pip (the system
Python is PEP 668 managed and has no ensurepip):
curl -LsSf https://astral.sh/uv/0.12.17/install.sh | sh
cd experiments && uv sync
cd site && pnpm build:site
It re-aggregates every RQ, runs the site data export, then astro build. Stops
at the first failing step. Refuses while a batch runs, through the aggregator's
batch lock — so check docker ps if it exits on that.
It closes with git diff --stat over research/. no changes is the only
output that lets a deploy proceed, because deploy-site.py publishes nothing
when its own build leaves a diff there.
A diff means committed aggregations were stale. Sort it before offering a commit, because the two kinds read completely differently:
n, mean or sd moves meaningfully, or a metric
appears where it was null. Usually a metric was computed (compute-cost.py,
compute-mutation-score.py) and never aggregated, or a run was re-measured
and other RQs share that run's cell.summary.json — "sd": 3.271085446759225 →
3.2710854467592254. Harmless, and one-time: two consecutive aggregations are
byte-identical afterwards.Separate them before reporting:
git diff -U0 -- research | grep -E '^[+-]' | grep -vE '^[+-][+-]' \
| grep -vE '"(sd|mean)":\s*[0-9.e-]{12,}' | head -40
Report the substantive ones by RQ and metric, then ask whether to commit. Never
git checkout the diff away — the committed values are the stale ones.
If the churn is not one-time, the pins have drifted. experiments/pyproject.toml
pins pandas/numpy to the host's system versions for exactly one reason: so
uv run and a plain python3 compute the same numbers. When the host's Python
moves and the pins do not, every uv-driven build rewrites the last digits of
every mean and a deploy can never go green. Check
python3 -c "import pandas, numpy; print(pandas.__version__, numpy.__version__)"
against the pins before concluding anything about the data.
Only after the user has said to deploy, and only with phase 1 green and phase 2
reporting no changes:
cd site && pnpm deploy:site
It runs the full build again, then commits site/dist/ plus .nojekyll onto
gh-pages in a temporary worktree as Deploy <short sha>: <subject> and pushes.
An orphan branch on the first deploy. The published site therefore always
matches one pushed commit on main.
Afterwards, report the gh-pages commit and the deployed main sha:
git log --oneline -1 origin/gh-pages
findings.md — that is /run-rq and /reanalyze./build-overview.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Generates an experiment-overview snapshot of all research questions under research/reports/. Invoke when a new point-in-time report across all RQs should be produced.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Mint a new YYYY-MM-DD-exact-coding-baseline snapshot under research/workflow-dev/export/. Detects the current best correctness- oriented workflow from research/workflow-dev/workflow-construction.md (or takes an explicit source-workflow argument) and transforms it from a lab workflow into a consumer-ready one on three axes: strips lab-only measurement content, re-enables human-in-the-loop checkpoints, and converts auto-loading config into an explicitly-invoked skill. Supports both promoted EXACT Coding lines: Opus/Hybrid and SOL/Predictive TDD. Exports Claude Code, pi, OpenCode, cursor-agent and, for SOL, Copilot. Trigger when the user says "exact-coding baseline export", "neue exact-coding baseline", "exact-coding-baseline-export", or asks to refresh the baseline snapshot.
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TDD Green Phase - Implement minimal code to make the failing test pass
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Re-run the analysis pipeline on all runs matching an RQ, reaggregate metrics, and propose findings updates against the fresh data. Trigger when the user says "reanalyze RQ-N", "reanalyse RQ-N", "Runs neu analysieren", or wants to refresh metrics/findings after a pipeline change (analyze-run.sh fix, adapter added, ESLint config).
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TDD Red Phase - Activate ONE test from the test list and make it fail with explicit predictions
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TDD Refactor Phase - Improve code while keeping tests green using Simple Design Rules and APP mass
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