Observatory

/observatory — System Health Analysis and Self-Improvement Flywheel

Analyze the hook/agent/dispatch system's operational health, surface patterns, and manage the improvement suggestion lifecycle. The observatory is the system's honest self-exam — it tells you what's actually happening, not what should be happening.

Why this exists: Hooks emit metrics into obs_metrics. Agents propose improvements into obs_suggestions. Without synthesis, those signals accumulate silently. The observatory skill runs the analysis layer, presents findings in human-readable form, and drives the accept/reject/converge lifecycle that closes the improvement loop.

Subcommands

Parse $ARGUMENTS to determine the mode:

Phase 1: Gather Data

CLI invocation: Prefer cc-policy if it is on PATH. If not, fall back to python3 ~/.claude/runtime/cli.py. The wrapper at ~/.claude/bin/cc-policy is committed to the repo; on first machine setup, run bash ~/.claude/bin/install.sh to register it on PATH.

Detection one-liner you can use:

CCP=$(command -v cc-policy || echo "python3 $HOME/.claude/runtime/cli.py")

For all modes except status, run the full summary:

$CCP obs summary

For status mode only:

$CCP obs status

Note: obs summary takes no flags — window is fixed at 24h. Other commands use positional arguments; check $CCP obs --help if uncertain about syntax.

Parse the JSON output. If the command fails (non-zero exit), report the error and stop — do not proceed with stale or empty data.

Phase 2: Present Findings

Structure the output in these sections. Skip empty sections silently.

Health Summary

Present key metrics in a table:

| Metric            | Value      | Status  |
|-------------------|------------|---------|
| Total metrics (24h)| N          | —       |
| Test pass rate    | X%         | OK / WARN |
| Guard denials     | N          | OK / WARN |
| Active suggestions| N          | —       |
| Last analysis     | N min ago  | —       |
| Review infra failures | N%     | OK / WARN |

Status thresholds:

• Test pass rate: OK if >= 80%, WARN if < 80%
• Guard denials: OK if 0 in window, WARN if any
• Review infra failures: OK if <= 20%, WARN if > 20%

Active Patterns

For each pattern in patterns, present as a bullet with severity:

[HIGH] repeated_denial: Policy 'branch-guard' denied 4 times in 24h window
       → Review policy configuration or adjust workflow
[MED]  slow_agent: Agent 'implementer' duration trending upward (slope=9.0s/run)
       → Check for context growth or prompt bloat
[LOW]  review_quality: 60% infra failure rate (3/5 reviews failed)
       → Investigate provider health

Severity mapping from severity_score:

• HIGH: score >= 5.0
• MED: score >= 1.0 and < 5.0
• LOW: score < 1.0

Trend Analysis

For each key metric with count > 0 in trends, show:

agent_duration_s  avg=32.5s  slope=+0.12 (trending up)
test_result       avg=0.82   slope=-0.01 (stable)
guard_denial      avg=1.0    count=3

Flag metrics with slope > 0.1 as "trending up" and slope < -0.1 as "trending down".

Suggestions

List active suggestions (proposed + accepted) in a table:

| ID | Category       | Title                          | Status   |
|----|----------------|--------------------------------|----------|
|  3 | perf           | Reduce impl duration           | proposed |
|  7 | review_quality | Investigate codex infra        | accepted |

Convergence Results

If convergence is non-empty, show results:

| ID | Metric          | Baseline | Measured | Result    |
|----|-----------------|----------|----------|-----------|
|  7 | agent_duration_s| 45.0s    | 38.0s    | IMPROVED  |
|  4 | test_result     | 0.75     | 0.72     | UNCHANGED |

Effective values: 1 = IMPROVED, 0 = UNCHANGED, -1 = REGRESSED

Review Gate Health

Total reviews: N  |  Infra failures: N (X%)
Provider breakdown: codex=N, gemini=N
Predictive accuracy: X% (review gate vs evaluator agreement)

Phase 3: Interactive Suggestion Management (run and suggest modes only)

Skip this phase for report, status, and converge modes.

For each proposed suggestion, present it and prompt:

Suggestion #3: [perf] Reduce impl duration anomaly
Body: Reduce context size
Target metric: agent_duration_s (baseline: 45.0s)

Accept (a), Reject (r), Defer (d), Skip (s), Quit (q)?

Handle responses:

• a → cc-policy obs accept <id>
• r → cc-policy obs reject <id> --reason "<reason>" (prompt for reason)
• d → cc-policy obs defer <id>
• s → skip to next suggestion
• q → stop suggestion loop

After processing, show a summary: "Accepted N, Rejected N, Deferred N."

Phase 4: Propose Suggestions from Patterns (run mode only)

For each detected pattern that does NOT already have a corresponding proposed or accepted suggestion (match by pattern_type in category field), offer to create one:

Pattern detected: slow_agent for 'implementer' (severity=9.0)
Create improvement suggestion? (y/n)

If yes:

cc-policy obs suggest --category slow_agent \
  --title "Reduce implementer agent duration" \
  --body "Slope=9.0s/run — investigate context growth or prompt bloat" \
  --target-metric agent_duration_s \
  --baseline <current_avg>

Phase 5: Convergence Check (converge mode)

Run:

cc-policy obs converge

Present results using the Convergence Results table format from Phase 2.

Interpret each result:

• IMPROVED: "Suggestion effective — metric improved >= 10% from baseline."
• UNCHANGED: "No significant change (< 10% delta). Consider escalating or closing."
• REGRESSED: "Metric worsened — the change may have had unintended effects."

Enforcement Rules

1. Never fabricate data. If cc-policy obs summary returns no metrics, say so explicitly — do not invent health indicators.

2. Pattern severity drives priority. HIGH patterns must be mentioned first. Don't bury critical findings below routine stats.

3. Convergence interpretations are factual. Report what the numbers show; don't speculate about causes beyond what the evidence supports.

4. Suggestions require user confirmation. Never auto-accept or auto-create suggestions. The flywheel closes only when the user approves the cycle.

5. Empty is informative. Zero patterns, zero suggestions, zero denials are valid and meaningful states — report them, don't skip the section.

Edge Cases