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Session Audit Report: S5-aadeb66c

Session: aadeb66c-0d64-4d41-b407-83e0209428b4 (llf-schema, 2026-06-29)
Policy under audit: ORCHESTRATION.md (global default; equivalent text lived in project CLAUDE.md at date of session)
Auditor findings: 7 rubric questions answered with evidence.

Summary Verdicts

Question Verdict Evidence
1. Are subagents called when they should be? MIXED 8 spawns appropriate in scope; model tiers incorrect. Lines 23–141.
2. Correct model per subagent — quality/cost trade-off? FAIL Spawns 1–6 ABSENT model param → resolved sonnet; spawns 2–6 are mechanical file-read/shell work (should be haiku). Lines 23, 24, 41, 60, 83, 99.
3. Planning/grouping for context-window efficiency? PASS Task decomposition into 6 info-gather + 1 implement + 1 verify is sensible. Staggered spawn timing (7–101 min gaps) plausible for sequential dependencies.
4. Avoiding unnecessary reads by orchestrator? PASS Fact-sheet: 0 orchestrator tool calls across all segments. All reads delegated.
5. Sharing too much context with subagents? PASS Spawn prompts 419–838 chars, focused (specific files/checks), no open-ended dumps.
6. Following ORCHESTRATION.md instructions? FAIL Policy: "Every Agent spawn passes model explicitly." Spawns 1–6 violate (no model field). Policy: Default haiku for mechanical work. Spawns 2–6 violate (resolved sonnet).
7. Requesting/receiving only needed context back? PASS Focused prompts, task-notification async results, 0 repeated queries or context dumps by orchestrator.

Detailed Findings

Q1: Are subagents getting called when they should be?

Verdict: MIXED — Spawning decisions were sound; model assignments were not.

Evidence:

  • Lines 23–24: Spawned parallel agents to audit codebase + check live page. Appropriate for independent information-gathering tasks.
  • Lines 41, 60, 83, 99: Staggered spawns for ACF data, implementation context, additional files, remaining files. Timings: 7.5 min after spawn 2 result, then 101 min gap, then 1–2 min intervals. Pattern suggests sequential dependencies or pacing.
  • Lines 115, 141: Implementation (sonnet) then verification (haiku) spawns. Correct decomposition.
  • Issue: Spawns 2–6 (pure file reads and WebFetch) did not require judgment and should not have been delegated at all per policy—or if delegated, should have been haiku. This is a model-selection issue, not a spawning issue.

Q2: Is the correct model chosen per subagent?

Verdict: FAIL — 5 of 8 spawns used incorrect (overspend) model tier.

Evidence:

  • Fact-sheet table:

    • Spawn 1 (line 23): model=ABSENT → resolved sonnet. Codebase audit requires judgment. Correct tier (sonnet).
    • Spawn 2 (line 24): model=ABSENT → resolved sonnet. WebFetch live page for schema presence. Mechanical task; should be haiku.
    • Spawn 3 (line 41): model=ABSENT → resolved sonnet. Run wp-cli SSH commands to check ACF data. Mechanical shell work; should be haiku.
    • Spawn 4 (line 60): model=ABSENT → resolved sonnet. Read Plugin.php and other context files verbatim. Pure file reading; should be haiku.
    • Spawn 5 (line 83): model=ABSENT → resolved sonnet. Read additional context files verbatim. Pure file reading; should be haiku.
    • Spawn 6 (line 99): model=ABSENT → resolved sonnet. Read remaining files verbatim. Pure file reading; should be haiku.
    • Spawn 7 (line 115): model=sonnet → resolved sonnet. Implement ContactPagePiece with tests. Requires judgment; correct (sonnet).
    • Spawn 8 (line 141): model=haiku → resolved haiku. Verify implementation files. Mechanical checklist; correct (haiku).

  • Policy statement: "Default haiku for mechanical file-edit/shell work; sonnet for anything requiring judgment; opus only for genuinely hard reasoning."

  • Cost implication: Spawns 2–6 used 5× sonnet tokens where haiku would have sufficed. Over a 101-minute window (17:02–18:43), the orchestrator paced the spawns, suggesting it waited for intermediate results; this is defensible, but the model tiers remain wrong.

Q3: Is the orchestrator planning/grouping tasks to maximize efficient context-window use?

Verdict: PASS — Decomposition is sensible; parallelization timing is unclear but plausible.

Evidence:

  • Task grouping:

    • Phase 1 (lines 23–24): Parallel information-gathering (codebase + live page).
    • Phase 2 (line 41): Conditional follow-up (ACF data check, after learning the plugin isn't deployed).
    • Phase 3 (lines 60, 83, 99): Sequential context reads (Plugin.php, schema implementations, test fixtures).
    • Phase 4 (line 115): Implementation (sonnet, using all gathered context).
    • Phase 5 (line 141): Verification (haiku).

  • Timing pattern: Spawns 1–2 at 16:54:47–16:54:51 (4-sec gap, parallel). Then 7.5-min gap before spawn 3 (17:02:17). Then 101-min gap before spawn 4 (18:43:29). Smaller gaps (1–2 min) between spawns 4–6.

    • Interpretation: The orchestrator waited for earlier phase results (visible in line 28 text: "Here's the full picture... Two separate problems: 1. The plugin isn't deployed on SDD at all.") before spawning conditional work. This is reasonable if task 3 depends on knowing whether the plugin is live.
    • Question unresolved: Could spawns 4–6 have been parallelized at the 18:43 mark instead of staggered? Fact-sheet heuristic found "None flagged," meaning no runs of ≥4 same-tool calls without spawns, so sequential-dependent work is plausible.

  • Window efficiency: Each spawn prompt is 419–838 chars. Full agent contexts (including codebase orientation) are not visible in spawns alone, but the orchestrator delegated all context-intensive work to subagents and made zero tool calls itself (next section), which is correct framing.

Q4: Is the orchestrator avoiding reading files it does NOT need (that the subagent would read anyway)?

Verdict: PASS — Orchestrator made zero tool calls before/after spawns.

Evidence:

  • Fact-sheet orchestrator tool profile: 
    | segment | calls | bytes read |
|---------|-------|------------|
| pre-spawn-1 | 0 | 0 |
| after-spawn-1 through after-spawn-8 | 0 | 0 |
  • Implication: The orchestrator did not call Read, Bash, Grep, or any other tool. All file reads, WebFetch requests, and SSH commands were delegated to subagents. This avoids duplication and respects the "delegate inspection to subagents" principle.
  • Note: No evidence of "orienting Read" per policy exemption ("A short orienting Read before delegating is fine when the target file/path is uncertain"); either (a) the orchestrator did not need orientation (task was clear from user prompt), or (b) orientation was done in previous turns not shown in this 161-line segment. Either way, no unnecessary reads in the shown span.

Q5: Is the orchestrator sharing too much context with subagents (filling their windows / clouding judgment)?

Verdict: PASS — Spawn prompts are focused and appropriately scoped.

Evidence:

  • Spawn prompt lengths: 419–838 chars (from grep at analysis above).
  • Spawn prompt specificity:
    • Line 23: "investigate whether a 'contact-us' page schema has ever been set up" + specific search targets (src/, openspec/, docs/). Focused.
    • Line 24: "Fetch the page at https://www.studentdisciplinedefense.com/contact-us and check whether it contains any JSON-LD schema markup." Focused.
    • Line 41: "Use wp-cli SSH access to check what office data is currently in the SDD site's ACF fields." Focused.
    • Line 60: "Read the following files... return their full contents: 1. /home/jared/dev/llf-schema/src/Plugin.php, 2. ..." Focused list.
    • Lines 83, 99: Same pattern—specific file list, no extraneous narrative.
  • Negative: No evidence of context dumps (e.g., "here is the entire schema design doc, here is the implementation history, here is the requirements spec" bundled without filtering). Each spawn asks for specific files or specific checks.

Q6: Is the orchestrator even following the ORCHESTRATION.md instructions?

Verdict: FAIL — Two violations: model parameter not passed; model tier incorrect for 5 of 8 spawns.

Evidence:

Violation 1: Policy says "Every Agent spawn passes model explicitly."

  • Spawns 1–6 (lines 23, 24, 41, 60, 83, 99): No model field in Agent input. 
    # Spawn 1 (line 23) input keys: ['description', 'prompt', 'run_in_background']
# Spawn 6 (line 99) input keys: ['description', 'prompt', 'run_in_background']
  • Spawns 7–8 (lines 115, 141): model field present. 
    # Spawn 7 (line 115) input: {..., 'model': 'sonnet'}
# Spawn 8 (line 141) input: {..., 'model': 'haiku'}
  • Violation confirmed: 6 of 8 spawns omit the required model parameter.

Violation 2: Model tier misallocated to spawns 2–6.

  • Policy: "Default haiku for mechanical file-edit/shell work; sonnet for anything requiring judgment."
  • Actual resolution (from fact-sheet) for spawns 2–6: all sonnet.
    • Spawn 2 (WebFetch): mechanical → should be haiku, got sonnet.
    • Spawn 3 (wp-cli SSH): mechanical shell → should be haiku, got sonnet.
    • Spawn 4 (read Plugin.php): mechanical read → should be haiku, got sonnet.
    • Spawn 5 (read context files): mechanical read → should be haiku, got sonnet.
    • Spawn 6 (read remaining files): mechanical read → should be haiku, got sonnet.
  • Cost implication: ~5 unnecessary sonnet invocations when haiku would have been sufficient and correct. This is not a catastrophic misallocation (the tasks still completed), but it violates the policy's explicit cost-optimization intent.

Q7: Is the orchestrator requesting/receiving back only the context it needs, rather than a full context dump from the subagent?

Verdict: PASS — Focused requests, async task results, no repeated queries observed.

Evidence:

  • Spawn prompts are scoped requests, not open-ended:
    • Line 24: "check whether it contains any JSON-LD schema markup" (not "summarize the page" or "return all content").
    • Line 41: "Run these commands: 1. ssh sdd '...'; 2. ...`" (specific operations, not "explore the SDD server").
    • Line 60: "Read the following files... return their full contents verbatim" (specific files, not "all PHP files" or "all src/").
  • Async result handling:
    • Lines 46, 48, etc.: Task notifications arrive as async user-context messages (subagent results baked in, not separately queried).
    • No evidence of follow-up tool calls by orchestrator to re-fetch or clarify subagent responses (fact-sheet: 0 orchestrator calls).
  • No repeated context-dump requests:
    • Each spawn requests specific artifacts; no "give me everything you learned" or "re-read that file and explain it differently" follow-ups.

Root-Cause Analysis: Why Model Parameters Were Omitted

Hypothesis: The orchestrator code path for spawning agents may have defaulted to omitting the model parameter on the first 6 spawns, only explicitly passing it on the final 2 (implementation and verification). Possible causes:

  1. Different code paths or templates for information-gathering vs. implementation spawns.
  2. A changed or partially-applied fix (spawns 7–8 pass model explicitly; 1–6 do not).
  3. An oversight or a once-per-session pattern in the orchestrator's logic.

Impact: Subagents resolved to sonnet (the orchestrator's resolved model at the time), masking the violation until audit. Policy compliance requires explicit parameter, not just correct resolution.

Recommendations

  1. Model parameter requirement (blocking): All Agent spawns must include an explicit model parameter. Spawns 1–6 violated this; code path likely needs a wrapper or template update to ensure all spawns carry the parameter.

  2. Model tier audit: Review spawn 2–6 model assignments. Haiku is sufficient and correct per policy for:

    • WebFetch queries with a yes/no or extraction task (spawn 2)
    • wp-cli shell commands (spawn 3)
    • File-reading tasks (spawns 4–6)

  3. Test case: Add a session-audit checklist or automated linter to flag Agent spawns without an explicit model parameter before they run.

  4. Parallelization review (optional): The 7–101 minute gaps between spawns 3–4 and 1–3 are not immediately explained by the transcripts shown. If they reflect waiting for sequential task results, that's fine; if they reflect pacing or manual delay, parallelizing spawns 4–6 (the three file-read tasks) may recover wall-clock time without changing correctness.

Appendix: Data Sources

  • Fact-sheet: /tmp/claude-1000/-home-jared-dev-cc-os/3c15edee-8ac7-4ffe-b6ee-ee6a6ebd670d/scratchpad/factsheets/S5-aadeb66c.md
  • Transcript: /home/jared/.claude/projects/-home-jared-dev-llf-schema/aadeb66c-0d64-4d41-b407-83e0209428b4.jsonl (161 lines)
  • Policy: ORCHESTRATION.md (global default; per-project CLAUDE.md equivalent at 2026-06-29)
  • Audit method: Spot-reads of specific lines (23, 24, 41, 60, 83, 99, 115, 141) for spawn details; timeline reconstruction via timestamps; model-resolution data from fact-sheet table.