Published by Sean Carr
Decoupled Context Pipeline Invariants: Verifying 205 Prompts Across 11 Switch States
Extracting an autonomous prompt injection pipeline into an isolated library while keeping hook execution under 100 milliseconds in our test and zero behavioral drift.
At a Glance
Worker Execution Time
16 ms (median)
Median worker execution time was exactly 16 milliseconds on both pre-refactor and post-refactor trees.
Hook Process Wall Time
94 - 95 ms
Full hook process ran at median 94-95ms (vs 97-107ms pre-refactor), keeping hook execution under 100 milliseconds in our test.
Golden Parity Checks
2,255 / 2,255 States
Verification passed across all 205 prompts and 11 switch states with zero differences and 40 live hook captures matching identically.
Heavy Dependencies
0 Packages
Runtime assertions confirmed zero loading of heavy analytical libraries (no numpy or tree-sitter imports).
| Telemetry Metric | Measured Value | Context & Observability |
|---|---|---|
| Worker Execution Time | 16 ms (median) | Median worker execution time was exactly 16 milliseconds on both pre-refactor and post-refactor trees. |
| Hook Process Wall Time | 94 - 95 ms | Full hook process ran at median 94-95ms (vs 97-107ms pre-refactor), keeping hook execution under 100 milliseconds in our test. |
| Golden Parity Checks | 2,255 / 2,255 States | Verification passed across all 205 prompts and 11 switch states with zero differences and 40 live hook captures matching identically. |
| Heavy Dependencies | 0 Packages | Runtime assertions confirmed zero loading of heavy analytical libraries (no numpy or tree-sitter imports). |
Published by Sean Carr
View & print executive brief version →Executive Summary & Core Finding
Refactoring high-throughput prompt injection hooks without breaking interactive agent behavior requires strict mechanical invariants. We extracted the context injection pipeline into an isolated, reusable library across six staged refactoring steps. By asserting output parity against a frozen golden suite of 205 prompts across 11 switch states and 40 live hook captures, we maintained sub-100 millisecond execution (median 16 ms worker time) with zero behavioral drift.
Method
Step 0: Record comprehensive golden outputs for prompt classification, state switching, and rendered payloads before modifying code.
Step 1: Extract gate check and process spawning primitives into isolated modules.
Step 2: Extract prompt classification logic and configuration switch handling.
Step 3: Extract safety boundary enforcement and context rendering routines.
Step 4: Extract symbol retrieval and injection coordination.
Step 5: Implement execution watchdogs and process lifecycle management.
We introduced deliberate one-character mutations to the golden outputs to verify that the golden test oracles were sensitive to subtle regressions and drift.
We enforced architectural isolation rules: lint rules verified that downstream memory package references never leaked into the core context library, and runtime checks confirmed heavy numeric or parsing packages were never loaded into the hook process.
What We Measured
Extracting an isolated context injection library maintained sub-100 millisecond hook execution (median 16 ms worker time) with zero behavioral drift across 205 prompts and 11 switch states verified against pre-recorded golden suites.
Worker Execution Time
16 ms (median)
Median worker execution time was exactly 16 milliseconds on both pre-refactor and post-refactor trees.
Hook Process Wall Time
94 - 95 ms
Full hook process ran at median 94-95ms (vs 97-107ms pre-refactor), keeping hook execution under 100 milliseconds in our test.
Golden Parity Checks
2,255 / 2,255 States
Verification passed across all 205 prompts and 11 switch states with zero differences and 40 live hook captures matching identically.
Heavy Dependencies
0 Packages
Runtime assertions confirmed zero loading of heavy analytical libraries (no numpy or tree-sitter imports).
Experimental Notes & Observability Details
- Slowest smoke run with responsive service took 0.17 seconds.
- When the background index service is unreachable, both old and new implementations cleanly terminate at the designated 1.5-second watchdog timeout.
- Synthetic prompt smoke tests matched byte-for-byte across all 12 tested configurations.
What We Think It Suggests
- Large-scale refactoring of mission-critical agent pipeline components can be executed without regressions if frozen golden outputs covering combinatorial configuration spaces are recorded prior to code movement.
- Fast interactive hooks must avoid importing heavy analytical libraries at startup; deferring or eliminating non-essential imports preserves sub-100 millisecond response times.
- Defining clear mechanical boundaries and fail-closed watchdogs ensures that if a background indexing daemon becomes unresponsive, the developer terminal remains responsive without hanging.
Limits
- Timing Test Flakiness: High background CPU utilization can cause occasional timing test fluctuations in local unit suites (for example, process execution briefly reaching 156 milliseconds against a 100 millisecond threshold).
- Schema Rigidity: Golden test assertions lock existing payload formatting tightly; intentional changes to prompt formatting or injected metadata require formal re-recording and review of the golden dataset.
- Dependency Requirement: The decoupled hook requires the core tooling repository to be importable; missing environment paths cause the hook to fail closed rather than silently falling back.
Reproduce It (Data & Benchmarks)
- Golden test suite: selfmem/tests/goldens/ctx_goldens.json
- Golden verification tests: tests/test_ctx_goldens.py
- Hook implementation: codekit/ctx/
- Extraction step commits: selfmem b554cec through eb85931; codekit cf3d06c through 5492a8b
Data and prompt templates only; no proprietary code is published.
Detailed Analysis & Field Observations
Terminal prompt submission hooks run on the critical path of every human and autonomous interaction. When extracting prompt classification, symbol retrieval, and context rendering into an independent shared library, any subtle behavioral divergence, dependency creep, or latency regression will degrade developer responsiveness or silently alter agent context.
We refactored the context injection pipeline across six incremental, mechanically verified steps. Before modifying any production logic, we recorded a frozen golden test suite covering 205 representative prompts evaluated across 11 discrete configuration switch states, alongside 40 live terminal hook captures.
Throughout the extraction, every refactoring step was required to maintain exact byte-for-byte output against the golden dataset and pass strict mechanical invariants, including zero heavy dependencies (no numpy or tree-sitter imports), a 1.5-second fail-closed watchdog, and sub-100 millisecond hook process execution (median 16 ms worker time).
End-to-end smoke tests passed synthetic prompt payloads into real hook processes under both interactive and headless execution modes, asserting byte-for-byte stdout parity and zero return codes.