Conversational guide for using tools/session-retro/observe.mjs to capture friction, wins, corrections, decisions, gaps, tool-use, and checkpoints as they happen during a session. Use at session start (to kick off the log), at inflection points (to record events), and at session end (to archive and feed the retrospective generator).
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Most session retrospectives are written from memory at the end. Memory is lossy. This skill captures the habit of logging events as they happen, so the retrospective has real data to work with.
start <mission-name> as one of the first
Bash tool calls, so the started_commit is recorded.end, then run generate.mjs to produce the
SESSION-RETRO.md.# Start
node tools/session-retro/observe.mjs start "<mission-name>"
# Log an event
node tools/session-retro/observe.mjs event <kind> "<label>" '<json-payload>'
# Check in on the log
node tools/session-retro/observe.mjs status
# End (archives current.jsonl to a dated file)
node tools/session-retro/observe.mjs end
| Kind | When | Example label |
|---|---|---|
friction | Something took more effort than it should have | "read-before-edit hook fired N times" |
win | Something worked notably well | "idempotent pipeline saved 3 re-runs" |
correction | User or hook corrected direction | "user reminded: no Co-Authored-By" |
tool-use | Notable tool invocation | "installed better-sqlite3 as optional dep" |
decision | Judgment call deserving review | "committed 1,881 generated files" |
gap | Missing skill/agent/chipset | "no batch-rewriter for N-script cascade" |
checkpoint | Progress marker for long-running work | "Pass 2 chapters: 300/602" |
tokens | Token consumption (dual-writes to token-budget) | "pass-2-chapter-gen" |
Free-form kinds are allowed but less useful for aggregation. Prefer the standard list.
tokens is the one kind where positional args are reinterpreted to keep the
call site short and to dual-write to current.tokens.jsonl:
# observe.mjs event tokens <in_tokens> <out_tokens> [label]
node tools/session-retro/observe.mjs event tokens 45000 3200 "pass-2-chapter-gen"
The entry lands in both current.jsonl (as a tokens event) and
current.tokens.jsonl (as a budget entry), so token-budget.mjs status and
the retrospective observations stay in sync without a second call.
Payloads are free-form JSON. Common fields:
count — numeric count of the occurrencefile / files — paths involvedduration_min — wall-clock timecost / tokens — resource spendimpact — "workflow" | "output" | "blocked" | "none"opportunity — what skill/agent would have helpedIf you didn't log live, you can backfill at session close by reviewing what
happened and calling observe event for each remembered moment. Better than
nothing. The timestamp will be session-end rather than event-time, but the
kinds and labels are still useful.
Events from current.jsonl are consumed by
tools/session-retro/generate.mjs and rendered in the SESSION-RETRO.md under
Observations, grouped by kind. The human-authored sections
(What Worked, What Could Be Better) then reference specific events as
evidence.
The release-history feature session logged 33 events retroactively:
Those 33 events became the concrete evidence for the retro's "What Worked" / "What Could Be Better" / "Recommendations" sections.
end never runs
and the JSONL never archives. Generate the retro before context dies.tools/session-retro/observe.mjs — the logger itselftools/session-retro/generate.mjs — the retrospective generatordecision-framework-invoker — often co-fires with decision eventsまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Provides web accessibility best practices for semantic HTML, ARIA, keyboard navigation, color contrast, and screen reader patterns. Use when building UI components, reviewing accessibility, or when user mentions 'a11y', 'accessibility', 'ARIA', 'screen reader', 'keyboard navigation', 'WCAG'.
日本語の概要は準備中です。原文の説明を表示しています。
Active listening techniques for effective communication. Covers attending behaviors, paraphrasing, reflective listening, clarifying questions, empathic response, barriers to listening, listening in conflict, and cross-cultural listening. Use when building listening skills, improving understanding in conversation, mediating disputes, or analyzing communication breakdowns.
日本語の概要は準備中です。原文の説明を表示しています。
Adversarial spec-compliance PR review — cross-references diffs against approved specs, verifies runtime claims against source, detects competing PRs, audits scope/convention compliance. Use before merging.
日本語の概要は準備中です。原文の説明を表示しています。
Provides best practices for AI agent orchestration including MCP servers, A2A protocol, multi-agent coordination, and swarm architectures. Use when designing agent systems, configuring MCP servers, setting up agent teams, or when user mentions 'MCP', 'A2A', 'agent orchestration', 'multi-agent', 'swarm', 'agent team', 'LangGraph', 'CrewAI', 'AutoGen'.
日本語の概要は準備中です。原文の説明を表示しています。
Symbolic manipulation, equation solving, and algebraic structures for mathematical reasoning. Covers distributive law, factoring, completing the square, linear through polynomial equation solving, systems of equations (substitution, elimination, Gaussian elimination, matrix methods), algebraic structures (groups, rings, fields), modular arithmetic, polynomial theory, and inequalities. Use when solving equations, simplifying expressions, working with algebraic structures, or performing symbolic manipulation.
日本語の概要は準備中です。原文の説明を表示しています。
Understanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.
日本語の概要は準備中です。原文の説明を表示しています。