atlas-argus

name	atlas-argus
description	Measure atlas's own run health, audit context/asset waste, and mine session transcripts. Three lenses: RUN HEALTH reads the observability DB for metrics (wall-clock, dispatches, verifier coverage) and baseline->target goals; ASSET AUDIT scores each context-loaded skill/agent/plugin by token cost and relevance to flag disable/relocate; SESSION FORENSICS mines transcripts for unused tools and unverified claims into rules. No args reports trends.
when_to_use	the task involves argus

atlas-argus

Atlas improves by measuring itself. Each run emits quantitative signals to the global SQLite observability DB at ~/.atlas/atlas.db (env ATLAS_DB). This skill reads those signals, surfaces the run's health scores, and proposes improvements that carry explicit baselines and targets -- no qualitative-only entries accepted.

Elicitation: when invoked without a clear lens, ask ONE AskUserQuestion - run health (recommended after an orchestration run), asset/context audit, session forensics, or cross-run trends - instead of guessing or running all three at full depth. When the asset audit proposes disabling/relocating assets, present the verdicts as a multiSelect AskUserQuestion (keep/apply per asset) rather than applying anything silently; the learn-from-restores rule depends on explicit user choices.

Single source of truth

The global SQLite DB at ~/.atlas/atlas.db (env ATLAS_DB), populated by the hooks and read via scripts/atlas_db.py. Access it through the public functions below -- never parse raw files or rely on memory alone.

Files are NOT the SSOT for run metrics. claude-mem is the narrative layer only: use it to store a lesson that survived three or more runs, not as the primary record.

Public functions in atlas_db.py:

• connect(path=None) -- open the DB (creates if absent)
• init(conn) -- create schema
• register_project(conn, root_path, name=None, stack=None) -> int -- upsert project row, returns project_id
• start_run(conn, project_id, session_id, task_summary=None, model=None) -> int -- open a run, returns run_id
• current_run_id(conn, session_id) -> int | None -- most recent OPEN run for this session (None once the Stop hook has finalized it)
• latest_run_id(conn, session_id) -> int | None -- most recent run for this session, open OR closed; use this when reading/deriving metrics after the run has been finalized
• log_event(conn, run_id, tool, context, is_inline_op, path=None) -> int -- append an event
• log_dispatch(conn, run_id, agent_type, model=None, wave_id=None) -> int -- record a dispatch
• record_recall(conn, run_id, hit) -> None -- increment recall_hits (hit=True) or recall_misses (hit=False) for the run; the engine Orient step calls this once per run via the record-recall <session> hit|miss CLI. Touches only the recall columns, so it survives derive_run_metrics refreshes.
• inline_ops_since_last_dispatch(conn, run_id) -- count inline ops since the last dispatch
• finalize_run(conn, run_id, wall_clock_s=None) -> None -- close the run
• run_metrics(conn, run_id) -> dict -- return the metrics row for a run
• derive_run_metrics(conn, run_id, session_id) -> dict -- compute the metrics no live hook can fill and write them onto the run's metrics row: est_context_tokens, parallel_waves, and in_flight_peak from the transcript mirror; wall_clock_s if not already set; verifier_coverage from the dispatches table (agent_type pairing -- see below -- NOT from tool_calls or the transcript mirror). The ingest hook now calls this automatically after every mirror refresh (Stop/SubagentStop/SessionEnd/PreCompact), so live runs populate on their own. Call it manually only for a session whose mirror you just backfilled. It deliberately does NOT touch recall_hits/recall_misses (those are recorded live by the engine Orient signal via record_recall), so a derive refresh never clobbers them.
• unpaired_implementer_dispatches(conn, run_id) -> int -- implementer-type dispatches beyond the verifier-type dispatches available to check them: max(0, implementers - verifiers), using the same dispatches-table agent_type matching rule as verifier_coverage. The completion gate consumes this to flag shipping work that never got an independent verifier.
• purge_observer_sessions(conn) -> dict -- one-shot cleanup of synthetic observer-session mirror rows (deletes the session_logs row plus its child rows in messages/tool_calls/user_prompts/signals for any transcript matching a known synthetic-session marker); touches only mirror tables, never runs/dispatches/events/metrics/improvements/asset_verdicts. CLI: python3 scripts/atlas_db.py purge-observer-sessions.
• record_improvement(conn, run_id, dimension, baseline, target, note) -> int -- persist a proposed improvement
• trends(conn, limit=20) -> list -- cross-run/cross-project trend rows over the FULL metric set (run_id, root_path, inline_ops, dispatches, parallel_waves, in_flight_peak, est_context_tokens, recall_hits, recall_misses, verifier_coverage, wall_clock_s); most recent limit runs. Mirror-derived columns read NULL for any run whose session has no ingested transcript.

What it measures

run_metrics returns a dict with these columns. Each maps to a specific behavior signal:

Column	Meaning	Red-flag signal
inline_ops	Tool calls made in the orchestrator's own context	High value = drift; the dispatch rule is being violated ("too small to delegate")
dispatches	Total subagent dispatches this run	Low relative to task complexity = under-delegation
parallel_waves	Distinct concurrent dispatch waves	Low on a multi-stage task = sequential dispatch when fan-out was required
in_flight_peak	Max simultaneous agents in a single wave	Below 3 on a 3+-stage task = missed concurrency
est_context_tokens	Estimated orchestrator context consumption	High = the orchestrator is bulk-reading source instead of delegating discovery
recall_hits	Times a memory lookup returned a usable lesson	Low = memory not being queried at Orient
recall_misses	Times memory was queried and returned nothing useful	High miss rate on a mature project = lessons not being captured
verifier_coverage	Fraction of dispatched changes that received an independent verifier	Below 1.0 = unverified changes shipping
wall_clock_s	Elapsed seconds for the run	Baseline for tracking improvement over time

How these are populated: inline_ops comes live from the PostToolUse tripwire (events table) and is written by finalize_run on Stop, which also defaults wall_clock_s to the run's elapsed time. dispatches is ALSO written by finalize_run at Stop, but that value is a one-shot snapshot - dispatches landing in later turns of the same session (via the dispatch_tripwire last-run fallback) never reached it, so the column could read 0 even when the dispatches table had rows for the run. derive_run_metrics now recomputes dispatches as a live COUNT(*) FROM dispatches WHERE run_id=? on every call, so the stored value always reflects every dispatch ingested by the time it runs (plugins/atlas/scripts/atlas_db.py:380-397). est_context_tokens, parallel_waves, and in_flight_peak are computed from the transcript mirror by derive_run_metrics(conn, run_id, session_id), which the ingest hook now runs automatically after each mirror refresh - so they fill on their own for any session whose transcript is ingested. A run whose session was never ingested still reads NULL for those three; backfill the transcript and call derive_run_metrics to fill it. verifier_coverage is computed differently: it is min(1.0, verifiers/implementers), counting implementer-type vs verifier/validator-type dispatches directly from the dispatches table by matching agent_type (recorded live at dispatch time) - NOT from tool_calls or the transcript mirror, whose target values suffer a ~99% key-mismatch against real agent names. This means verifier_coverage does not require transcript ingestion, only the dispatches rows for the run. A value of 0.0 means implementer dispatches shipped with zero verifier dispatches to check them; NULL means there were zero implementer dispatches this run - verification coverage is not applicable, a materially different state from 0.0 and must not be conflated with it. unpaired_implementer_dispatches(conn, run_id) exposes the same pairing as a raw count (max(0, implementers - verifiers)) for the completion gate. recall_hits / recall_misses are recorded live by the engine: at Orient the engine queries memory and then calls record-recall <session> hit|miss - hit when the lookup surfaced a usable lesson, miss when it ran but returned nothing usable. This is a self-report by the agent that did the lookup, not a blind count of memory tool calls. A run that never reached an engine Orient step (e.g. a plain chat session) reads NULL here; treat NULL as "no orchestration recall recorded", not zero. This skill may still refine the values by reading the messages when it audits a run.

Measurable improvements

Every proposed improvement MUST carry an explicit baseline -> target and be persisted via record_improvement(conn, run_id, dimension, baseline, target, note). No qualitative-only entries.

Example workflow:

# requires plugins/atlas/scripts on sys.path (the hooks insert it; do the same here)
import atlas_db, os

conn = atlas_db.connect(os.environ.get("ATLAS_DB", os.path.expanduser("~/.atlas/atlas.db")))
session_id = os.environ.get("CLAUDE_CODE_SESSION_ID", "")
# latest_run_id (not current_run_id) so this still resolves after Stop finalized the run
run_id = atlas_db.latest_run_id(conn, session_id)
metrics = atlas_db.run_metrics(conn, run_id)

# Example: verifier coverage was 0.6 this run
if metrics["verifier_coverage"] is not None and metrics["verifier_coverage"] < 1.0:
    atlas_db.record_improvement(
        conn, run_id,
        dimension="verifier_coverage",
        baseline=metrics["verifier_coverage"],
        target=1.0,
        note="Independent verifier dispatched on every shipping change, not just 'large' ones."
    )

# Example: inline_ops spiked
if metrics["inline_ops"] > 4:
    atlas_db.record_improvement(
        conn, run_id,
        dimension="inline_ops",
        baseline=metrics["inline_ops"],
        target=4,
        note="Dispatch tripwire threshold is 4; any excess means an orchestrator inline-op rule was bypassed."
    )

Dimension keys map directly to the metrics columns above. A note explains the behavioral root cause and the concrete change to make. When in doubt, name the law (1-7 in atlas-metis) that the metric signals was violated.

Asset/context audit (the context-cost lens)

Run health asks "is atlas behaving?". This lens asks "is the session carrying weight it does not need here?". A fresh session pays for every globally-loaded skill, agent, and enabled plugin whether or not it is relevant to the project.

Driver: scripts/asset_audit.py [project_root] [--json] [--apply].

What it does:

1. Inventory every context-loaded asset (skills under ~/.claude/skills, agents under ~/.claude/agents - both may be symlinks; it follows them) and estimates each one's context cost (chars/4, surfaced as an estimate).
2. Detect the project stack from files and package.json deps (mcp, node-ts, python, dotnet, azure, frontend, terraform, ...).
3. Score relevance: an asset with no tech tag is universal (always kept); a tagged asset is kept if its tags meet the project, else flagged.
4. Choose the LEVEL - the part that matters:
   • off-stack here but on-stack for another known project -> disable-here (project settings.local.json), never a global cut.
   • off-stack everywhere -> relocate-global candidate.
5. Tier by risk: AUTO (novelty/off-stack-everywhere, safe to relocate) vs CONFIRM (everything else). --apply relocates only AUTO items, by moving (never deleting) into ~/.claude/{skills,agents}-disabled with a restore manifest under ~/.claude/.context-cleanup-manifests/.

Learning loop. Verdicts persist to asset_verdicts (see atlas_db.py). When you restore a flagged asset, call atlas_db.note_asset_restore(conn, kind, key) - that asset is then suppressed and never re-flagged. Track quality with atlas_db.asset_audit_summary(conn) (false_positive_rate): a rising rate means the taxonomy is over-flagging and needs tightening, not more applying.

Public functions in atlas_db.py for this lens: record_asset_verdicts, mark_asset_applied, note_asset_restore, suppressed_assets, asset_audit_summary.

Apply discipline: AUTO auto-applies under --apply; everything in CONFIRM is presented to the user with its reason and the level (disable-here vs relocate-global) before any move. Disabling at the project level is preferred over a global relocate whenever the asset serves another project.

Session forensics (the transcript-mirror lens)

Run health asks "is atlas behaving?"; the asset audit asks "is the session carrying dead weight?". This lens asks "what actually happened across every session, and what should change because of it?". It reads the rich mirror of the on-disk session transcripts that hooks/ingest_session.py and scripts/session_ingest.py --backfill land in the DB. The sparse events table never held this; these tables do.

Tables

• session_logs - one row per transcript: project, cwd, model, token totals (input/output/cache_read/cache_creation), counts, error_count, and the ingest cursor. Token totals are recomputed from child rows, so they are correct regardless of re-ingest. Also carries an agent column ('claude' is the schema default; 'codex' is written explicitly by the codex adapter) - slice any of the read helpers below by joining on session_logs.agent to compare behavior across coding agents. Transcripts under a known synthetic-session marker (currently .claude-mem/observer-sessions, the per-session mirror claude-mem writes for its own observer) are excluded at ingest and never land a row here.
• messages - every user/assistant/system message: role, is_sidechain (subagent), thinking, text, per-message usage tokens, model.
• tool_calls - every tool invocation: tool_name, kind (builtin|skill|mcp|agent), target (tool name / skill / server.tool / subagent type), server, secret-scrubbed input_summary, input_bytes (true size = context cost proxy), is_error, result_bytes.
• user_prompts - the user's actual typed prompts (tool-result messages excluded), with a norm clustering key.
• signals - behavioral flags tagged at ingest: assumption_admission, unverified_claim, user_correction, each with a snippet and a message link.

Read helpers in atlas_db.py

Use these; do not re-parse transcripts.

• tool_usage(conn, kind=None, project_id=None) - per-target rollup: calls, errors, sessions, input_bytes.
• idle_assets(conn, kind, known_keys) - of the assets present in this environment, which were NEVER invoked in any session (the unused set).
• context_tool_health(conn) - cache-hit ratio plus call/error counts for context-mode, claude-mem, and ponytail.
• signal_counts(conn) / signal_rollup(conn, signal_type=None) - the behavioral-issue rollup and the per-incident snippets.
• repeated_prompts(conn, min_count=3) - normalized prompts that recur across sessions (the repetitive-task signal).

The four questions to answer, and the action each produces

1. Which tools/skills/MCP/agents are used vs idle? Compare tool_usage(conn, kind=...) against the installed inventory (discover_capabilities.py / asset_audit.py); feed the unused set to idle_assets. A skill/agent/MCP with zero calls across many sessions is a relocate-global or removal candidate (route through the asset-audit apply discipline). A high-error target is a description/schema bug - file an improvement against that tool, not the model.
2. Is the context layer actually helping? Read context_tool_health. A low cache_hit_ratio, or context-mode/claude-mem/ponytail with near-zero calls on large-output sessions, means the protection is configured but unused - propose a CLAUDE.md nudge or a hook, with the ratio as the baseline.
3. What is being asked repeatedly? repeated_prompts clusters re-typed requests. A cluster of 3+ is a workflow that should become a skill/command or a standing CLAUDE.md rule so the user stops re-asking.
4. Where did atlas mislead the user? signal_counts / signal_rollup('assumption_admission') surface the exact class of issue that motivated this lens: a claim made without testing, a callback "wired" only in a plan, a "should work" with no evidence. Each confirmed incident becomes a record_improvement AND, when it recurs across 3+ sessions, a concrete CLAUDE.md / rules edit (e.g. strengthen the verification-protocol clause the incident violated). Quote the snippet and cite the session as evidence.

Every output of this lens still obeys the measurable-improvement rule: persist a record_improvement(conn, run_id, dimension, baseline, target, note) with a real baseline (the count, the ratio, the error rate) and a target. Signals are candidate findings, not proof - confirm against the linked message text before proposing a rule change, the same way a human would re-read the transcript.

Refresh + backfill

The ingest hook keeps the mirror current for claude sessions. To (re)index history on demand: python3 scripts/session_ingest.py --backfill [~/.claude/projects] (idempotent), or one session: python3 scripts/session_ingest.py <transcript.jsonl>.

Codex sessions are never picked up by the claude Stop/SessionEnd hook path - backfill them explicitly: python3 scripts/session_ingest.py --backfill-agent codex [root] (root defaults to ~/.codex/sessions). Caveat when comparing agents: codex's token_count events are per-event deltas that sum to total_token_usage, but codex emits far more of these events than assistant messages, and the adapter persists only the one nearest each stored message - so codex session totals systematically undercount what codex itself reports. Treat cross-agent token comparisons as directional, not exact, and note that context_tool_health() aggregates across all agents with no agent filter, so its totals blend regimes once codex rows exist.

A backlog of pre-exclusion observer-session rows was purged from the live DB on 2026-07-09 after an audit found 96.6% of session_logs rows were this pollution; see purge_observer_sessions(conn) above and evidence at .atlas/docs/evidence/2026-07-09-observer-purge.md.

Trends (no-arg)

Pass the open DB connection: trends(conn, limit=20). It is called the no-arg path because the user supplies no arguments and no run_id is needed, not because conn is optional.

When called with no run context, run trends(conn, limit=20) and summarize cross-run and cross-project direction:

• Which dimensions are improving (falling inline_ops, rising verifier_coverage)?
• Which are regressing or stuck?
• Are any projects persistently underperforming a specific dimension?

Return a compact table: dimension, recent-mean, prior-mean, direction (+/-/flat), and one sentence of interpretation. Flag any dimension where the target set in a prior record_improvement call has not been reached after three or more subsequent runs -- that improvement is stalled and needs a different approach.

The nudge

The nudge.py hook (hooks/nudge.py) fires on Stop and SubagentStop, at most once per 15-minute window. It asks: did this turn produce a reusable lesson? If yes, use record_improvement to persist a metric-backed entry AND capture a claude-mem lesson only when the pattern has repeated at least 3 times and is not already in memory.

When to capture a claude-mem lesson:

• The user corrects or rejects the work ("no, it should be...", "I prefer X", "stop doing Y") -- and the correction applies across projects.
• A command, API, or build step fails in a non-obvious way and the failure is likely to recur.
• A pattern has occurred 3+ times in different sessions.

Do not capture:

• One-time instructions or file-specific context.
• Hypotheticals.
• Lessons already present in memory (check first with memory_search).
• Inferences from silence -- wait for explicit correction or repeated evidence.

Capture format for claude-mem:

TYPE:    Decision | Pattern | Error | Constraint | Preference
CONTEXT: project / feature / library@version
LESSON:  one or two sentences -- what to do differently next time
SOURCE:  file:line or command + observed result

Cite the source of any memory-derived action. Keep new lessons tentative until they repeat; promoting too fast pollutes the hot set.