analyze-usage

name	analyze-usage
description	Analyze higher-level patterns in Claude Code usage. Outputs (a) graphic monthly/weekly digest with metaphors — aquarium of biomes (🐋🦈🐬🐟🦐🦠), archetypes (⚙️🔬🌐🛠📝🔍🏗💬), rhythm, stack palette, DORA radar (CFR + lead time + pushes), friction (compacts/pivots/subagents); (b) per-session parquet bundles for further analysis (biome, archetype, rhythm, growth, milestones, idle gaps, subagent pyramids, compact patterns, parallelism, burst classes, topics). Use when the user asks about weekly/monthly reports, session biomes, productivity profiles, "what kind of work was done", when a session became a whale, DORA metrics, or wants drill-down view of a specific session.
allowed-tools	Bash, Read, Write, Edit, Glob, Grep

analyze-usage

Higher-level usage analytics on top of research-pipeline. Extracts patterns that the existing pipeline does not surface directly: biome class, archetype, growth curves, milestones, gaps, subagent pyramids, compact patterns, and burst-level RW classes. Output is a pair of parquet bundles plus an agent-written markdown report.

When to use

Trigger on any of:

• "biome", "whale", "shark", "creature", "плактон", "кит", "акула"
• "archetype", "constructor", "operator", "researcher"
• "growth", "milestone", "когда сессия стала", "rate of growth"
• "subagent pyramid", "subagent stats", "сабагенты"
• "compact pattern", "context window over time", "автокомпакты"
• "idle gaps", "should I split this session", "split point"
• "drill into session", "view session timeline" + a session id
• "weekly digest", "monthly report", "отчёт за неделю/месяц/квартал", "графический отчёт", "DORA", "CFR", "lead time" → use scripts/period_report.py --from --to --period monthly|weekly|both
• "rhythm", "Mono / Phased / Mixed / Chaos", "ритм сессии", "stack palette", "self-feedback", "TDD ratio", "motivated read" — these come from lib/classifiers.py and the corresponding extractors

If the user only wants the base research dataset (sessions, turns, tool_calls, loops, correlations), use research-pipeline instead. This skill extends that dataset.

Three-tier output

outputs/
  raw/        Tier 1 stat parquet, original UUIDs/paths/branches — owner only
  anon/       Tier 1 anonymized — shareable bundle (audit with `--audit-anon`)
  llm/        Tier 2 LLM-augmented features (session names, narratives) — owner only
  reports/    markdown reports written by the agent (Tier 2)

• raw/ keeps original session UUIDs, project paths, file paths, branch names, and prompt token frequencies. Owner-only; never share without re-running the anonymizer.
• anon/ follows the anonymization contract below. Hashed session ids (s0001 / a0001), hashed project slugs (p<6hex>), file ext only, no raw paths or branch names, prompt tokens replaced by 6-char hashes (reverse dictionary stays in raw/_token_dict.json). Auditable via pattern_detector.py --audit-anon.
• llm/ holds Tier 2 outputs that depend on the agent (Claude). Tier 1 extractors stay deterministic; LLM enrichment is layered on top via extract_llm_*.py extractors that build a queue (_queue.jsonl) the agent consumes and writes back as parquet. Owner-only by default.
• reports/ is markdown the agent generates from the parquet tables. Reports may include real session names and quotes if the user owns the data.

Tier model

• Tier 0 — tests: tests/run_all.sh runs 28 unit + smoke tests (classifiers, stats, full extractor pipeline on a synthetic fixture, anonymization leak audit). Run before shipping a new extractor.
• Tier 1 — always-on (no API key): pure Python extractors using regex and statistics. Lives in scripts/extract_*.py. Reuses lib/ helpers.
• Tier 2 — agent-side LLM: when the skill is active, the agent itself reads parquet + raw jsonl and adds the narrative layer (session naming, phase detection, pattern interpretation, drill-down quotes). No external LLM API call required.

If you (the agent) need a metric that doesn't exist yet, write a new extract_<name>.py, wire it into pipeline.py, add a test in tests/test_extractors_smoke.py, run it. The pipeline is designed to extend without rewrite.

Run

# Graphic period digest (monthly + weekly, with метафоры — aquarium, palette, DORA radar)
uv run .claude/skills/analyze-usage/scripts/period_report.py \
  --from 2026-02-01 --to 2026-04-30 --period both

# Just one month at a time
uv run .claude/skills/analyze-usage/scripts/period_report.py \
  --from 2026-04-01 --to 2026-04-30 --period monthly

# Just last week (auto-saves a copy)
uv run .claude/skills/analyze-usage/scripts/period_report.py \
  --from 2026-04-23 --to 2026-04-30 --period weekly --out /tmp/last_week.txt

# Full pipeline (assumes research-pipeline has populated /tmp/claude_analysis/)
uv run .claude/skills/analyze-usage/scripts/pipeline.py \
  --since 2026-04-01 \
  --outputs .claude/skills/analyze-usage/outputs/

# Only specific extractors
uv run .claude/skills/analyze-usage/scripts/extract_biome.py
uv run .claude/skills/analyze-usage/scripts/extract_growth.py

# Drill into one session (requires raw jsonl, owner-only)
uv run .claude/skills/analyze-usage/scripts/view_session.py 2c052d83

The pipeline expects ~/.claude/projects/*.jsonl to be present. Override the location by setting CLAUDE_PROJECTS_ROOT=/some/dir/projects (used by the smoke tests; useful for sandboxed runs on someone else's logs).

The current extractors parse the jsonl logs directly — they do not reuse /tmp/claude_analysis/sessions.parquet from research-pipeline. A future extractor can short-circuit by reading that parquet if it exists, but until that lands, every extractor walks ~/.claude/projects/ on each run.

Available extractors

Script	Output (raw + anon)	What it computes
extract_biome.py	biome.parquet	Whale / Shark / Dolphin / Fish / Shrimp / Plankton per session (main + subagents)
extract_archetype.py	archetype.parquet	8-class archetype (Variant B 45/25): Constructor / Operator / Researcher / Builder / Scholar / Inspector / Polymath / Discusser, plus rhythm class (Mono / Phased / Mixed / Chaos)
extract_growth.py	growth.parquet, milestones.parquet	Per-session: n_bursts, daily R/LOC, CV, Gini, acceleration_R; milestone burst-index for thresholds 10/30/100/500
extract_outputs.py	outputs.parquet	Commits by type (feat/fix/refactor/chore/docs/test/ci) with review-fix vs prod-fix split, pushes, PR/MR/issue creation, comments, files edited/written, lines added/removed, unique branches/issues
extract_subagents.py	subagent_pyramid.parquet	Per-parent distribution of subagent sizes (whale/shark/.../plankton bins)
extract_compacts.py	compact_pattern.parquet	Per-session compact frequency, real_prompts before first compact, max context size, compact_per_asst
extract_gaps.py	idle_gaps.parquet	Gaps >8h with suggested split points (long-form: one row per gap)
extract_topics.py	topic_signatures.parquet	BoW per session (top-20 tokens). raw keeps tokens, anon hashes them; reverse dictionary in raw/_token_dict.json
extract_burst_classes.py	burst_classes.parquet	Per-session counts: pure_qa / read_only / bash_only / write_only / write_heavy / chain_run / mixed
extract_parallelism.py	parallelism.parquet	Hour-bin concurrent session counts (date+hour → cnt)

Other tool	Purpose
period_report.py	Graphic monthly/weekly digest with metaphors (aquarium, palette, DORA radar, friction). Reads jsonl directly, no parquet needed. Args: --from --to --period monthly|weekly|both --format text|markdown|html|all [--out FILE | --out-dir DIR] [--open]. With --open auto-opens HTML in browser.
extract_llm_session_names.py	Tier 2 stub: enqueues Whale/Shark/Dolphin sessions for narrative summarization by the agent. Writes outputs/llm/_queue.jsonl (and an empty session_names.parquet stub). Agent consumes the queue and appends rows.
bin/analyze-usage	Standalone CLI wrapper. Subcommands: period, pipeline, session SID, audit, patterns, report, test. Add to PATH for non-agent usage.
pipeline.py	Orchestrator — runs all 10 extractors sequentially (~4–5 min on a full corpus). Args: --since --outputs --only <names>
view_session.py SID	Drill-down view of one session by sid prefix (raw jsonl required). Prints biome/archetype/rhythm + first 5 prompts + bash subcategory mix
pattern_detector.py	Default: heuristic flags from raw/ parquets: needs_split (≥2 idle gaps), pure_constructor (Constructor+Phased), feature_with_debug (Inspector+compacts≥2), pipeline_rescue (compacts≥3 + LOC<500), ghost_session (Plankton+0 tools). With --audit-anon: walks anon/ parquets and asserts no raw UUIDs / abs paths / bash commands / branches / mcp slugs leak — exits 1 on any leak.
report_generator.py	Markdown report skeleton from parquet tables → outputs/reports/<date>.md

Library helpers

lib/ holds the shared building blocks. Extractors should import from there rather than duplicate logic.

Module	Provides
lib/parsers.py	find_session_files(root=None), find_subagents(jsonl), parse_session(jsonl, since, until) → Iterator[Event], build_bursts(events, sid, project) → list[Burst], load_session(jsonl) → ParsedSession, split_chain(cmd). Honors CLAUDE_PROJECTS_ROOT env var.
lib/classifiers.py	Pure-function classifiers: biome_of(real_prompts), archetype_of(edit, bash, read) (Variant B 45/25), rhythm_of(event_seq) (Mono / Phased / Mixed / Chaos / "—"), stack_of(file_path) (frontend / backend / infra / docs / config / other), bash_sub_of(cmd) (git / test / build / run / device / deploy / format / net / playwright_cli / shell / other), commit_type_of(first_line), is_review_fix(body). Plus emoji dicts: BIOME_EMOJI, ARCHETYPE_EMOJI, RHYTHM_EMOJI.
lib/stats.py	cv(xs), gini(xs), shannon_entropy(counter), milestone_at(cum, threshold), acceleration(daily_xs), pearson(xs, ys), percentile(xs, p), median(xs), power_law_fit(xs, ys) (returns slope/intercept/r/n).
lib/anonymize.py	hash_path(s) → p<6hex>, hash_token(w), hash_branch(name), hash_mcp_slug(name), SidProjector(prefix) (sequential s0001 / a0001 mapping), file_ext(path), k_anon_filter(rows, group_key) (drops groups with N<5), write_token_sidecar(dict, raw_dir).
lib/io.py	outputs_dirs(base=None) → (raw, anon), outputs_dirs_v2(base=None) → (raw, anon, llm) (auto-mkdir), write_parquet(rows, path) (snappy, returns row count, empty marker if no rows).
lib/render.py	Period-report renderers in three formats: render_period_terminal/markdown/html_section(label, agg), render_html_doc(title, sections), render_weekly_table_*, render_trends_*. Pure data → string. Used by period_report.py.

Tests

tests/run_all.sh runs four test suites (~15 seconds total, 28 tests):

Suite	Coverage	Count
tests/test_classifiers.py	biome thresholds, archetype Variant B (pure / hybrid / too-few-tools), rhythm Mono / Chaos / too-short, stack classification (all extensions), bash sub regex coverage, conventional commit type, review-fix detection	11
tests/test_stats.py	cv (empty + known), gini (extremes), Shannon entropy, milestone_at, pearson (perfect / zero), percentile / median, power_law_fit (y=2x² recovers slope=2), acceleration (const / accel / decel)	10
tests/test_extractors_smoke.py	Builds a synthetic ~/.claude/projects/ fixture with 3 controlled sessions (Operator-Fish, Constructor with feat+fix+review-fix+push, ghost). Sets CLAUDE_PROJECTS_ROOT, runs all 10 extractors, asserts schemas + specific values (e.g. session2 must have feat=1, fix=2, review_fix=1, prod_fix=1, pushes=1, archetype=Constructor).	1 (full pipeline)
tests/test_audit_anon.py	Builds anon parquets with synthetic content; asserts pattern_detector.py --audit-anon returns 0 on clean fixture and 1 on each kind of leak (raw UUID, abs path, raw bash, raw branch in nested list, bad sid format).	6

bash .claude/skills/analyze-usage/tests/run_all.sh

When you add an extractor, add a corresponding assertion to test_extractors_smoke.py so future refactors stay honest.

Anonymization contract

When emitting to outputs/anon/:

• session_id → sequential s0001 (main) or a0001 (subagent)
• project_path → p<6-hex> SHA-1 truncation
• file_path → drop, keep file_ext and file_type only
• bash_command → drop, keep category + structural flags only
• prompt_text → drop, keep chars, intent_flags, and token_hash BoW
• branch_name → drop, keep branch_hash only
• tool_use_id → drop
• mcp_server_slug → hash, keep verb intact
• Any aggregate with N<5 sessions in the bucket → suppress (K=5 threshold)

The outputs/anon/ directory is auditable via pattern_detector.py --audit-anon: it walks every parquet, recursively scans every string- typed column (including nested lists/dicts), and exits 1 if it finds raw UUIDs, absolute paths, bash commands, branch names, or un-hashed MCP slugs. Run after every pipeline regeneration before sharing.

Extending the pipeline

To add a new metric foo:

1. Create scripts/extract_foo.py using lib/parsers.py to load sessions and lib/classifiers.py / lib/stats.py / lib/anonymize.py / lib/io.py for the rest. Follow the shape of existing extractors (CLI: --since YYYY-MM-DD, --outputs DIR).
2. Emit two parquet files: outputs/raw/foo.parquet (full data) and outputs/anon/foo.parquet (anonymized via SidProjector + hash_path
   • token hashes).
3. Append extract_foo to the EXTRACTORS list in scripts/pipeline.py.
4. Add a row to the Available extractors table above.
5. Add an assertion in tests/test_extractors_smoke.py checking the new schema + at least one value on the synthetic fixture.
6. Run bash tests/run_all.sh — must stay 28+/28+ green.

Do not assume only humans extend this — the agent itself adds extractors when the user asks "what about " and the existing tables don't cover it.