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feat(estimation): build historical plan statistics query service for estimation context assembly #1295

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brent.edwards wants to merge 1 commit from feature/m6-estimation-historical-stats into master
pull from: feature/m6-estimation-historical-stats
merge into: cleveragents:master
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brent.edwards commented 2026-04-02 09:11:07 +00:00
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Summary

Implement the historical plan statistics infrastructure for the estimation actor's context assembly, as specified in docs/specification.md lines 19077-19081 (estimation actor analyzes "Historical data from similar plans (if available)").

Closes #652

Changes

Domain Model

  • HistoricalPlanStats (src/cleveragents/domain/models/core/historical_plan_stats.py): Frozen Pydantic v2 value object packaging mean/median/p90 cost, mean/median duration, average step count, average child plan count, and success rate. Includes empty() factory for first-run scenarios and as_context_dict() for ACMS context fragment serialization.

Repository

  • get_completed_plans_by_action() on LifecyclePlanRepository: Filters on terminal states (complete, applied, errored, cancelled) ordered by created_at DESC with retry logic.

Application Service

  • HistoricalPlanStatsService (src/cleveragents/application/services/historical_plan_stats_service.py): Stateless service that queries the repository and computes aggregate statistics using stdlib statistics module. Gracefully handles missing cost/timestamp/step data.

ACMS Integration

  • EstimationHistoricalStatsProvider (src/cleveragents/application/services/estimation_context_provider.py): ACMS ContextStrategy implementation that injects historical stats as a high-relevance context fragment into the estimation actor's hot context tier. Handles service failures, empty history, and budget constraints.

Testing

  • 27 Behave scenarios covering model validation, service logic (empty/normal/edge cases), ACMS provider integration, and repository methods
  • 7 Robot integration tests with helper script
  • All quality gates pass:
Stage Result
nox -s lint ✅ Pass
nox -s typecheck ✅ Pass (0 errors)
nox -s unit_tests ✅ Pass (556 features, 13798 scenarios)
nox -s integration_tests ✅ Pass (1875 tests)
nox -s e2e_tests ✅ Pass
nox -s coverage_report ✅ Pass (98.6%, threshold 97%)
## Summary Implement the historical plan statistics infrastructure for the estimation actor's context assembly, as specified in `docs/specification.md` lines 19077-19081 (estimation actor analyzes "Historical data from similar plans (if available)"). Closes #652 ## Changes ### Domain Model - **`HistoricalPlanStats`** (`src/cleveragents/domain/models/core/historical_plan_stats.py`): Frozen Pydantic v2 value object packaging mean/median/p90 cost, mean/median duration, average step count, average child plan count, and success rate. Includes `empty()` factory for first-run scenarios and `as_context_dict()` for ACMS context fragment serialization. ### Repository - **`get_completed_plans_by_action()`** on `LifecyclePlanRepository`: Filters on terminal states (`complete`, `applied`, `errored`, `cancelled`) ordered by `created_at DESC` with retry logic. ### Application Service - **`HistoricalPlanStatsService`** (`src/cleveragents/application/services/historical_plan_stats_service.py`): Stateless service that queries the repository and computes aggregate statistics using stdlib `statistics` module. Gracefully handles missing cost/timestamp/step data. ### ACMS Integration - **`EstimationHistoricalStatsProvider`** (`src/cleveragents/application/services/estimation_context_provider.py`): ACMS `ContextStrategy` implementation that injects historical stats as a high-relevance context fragment into the estimation actor's hot context tier. Handles service failures, empty history, and budget constraints. ## Testing - **27 Behave scenarios** covering model validation, service logic (empty/normal/edge cases), ACMS provider integration, and repository methods - **7 Robot integration tests** with helper script - All quality gates pass: | Stage | Result | |-------|--------| | `nox -s lint` | ✅ Pass | | `nox -s typecheck` | ✅ Pass (0 errors) | | `nox -s unit_tests` | ✅ Pass (556 features, 13798 scenarios) | | `nox -s integration_tests` | ✅ Pass (1875 tests) | | `nox -s e2e_tests` | ✅ Pass | | `nox -s coverage_report` | ✅ Pass (98.6%, threshold 97%) |
feat(estimation): build historical plan statistics query service for estimation context assembly
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2aa0ff64bb 
Implement the historical plan statistics infrastructure for the
estimation actor's context assembly, as specified in docs/specification.md
lines 19077-19081 (estimation actor analyzes "Historical data from
similar plans (if available)").

Components added:

1. HistoricalPlanStats — Frozen Pydantic v2 value object in
   domain/models/core/ packaging mean/median/p90 cost, mean/median
   duration, average step count, average child plan count, and success
   rate. Includes empty() factory for first-run scenarios and
   as_context_dict() for ACMS context fragment serialization.

2. LifecyclePlanRepository.get_completed_plans_by_action() — Repository
   method filtering on terminal states (complete, applied, errored,
   cancelled) ordered by created_at DESC with retry logic.

3. HistoricalPlanStatsService — Stateless application service that
   queries the repository and computes aggregate statistics using
   stdlib statistics module. Handles missing cost/timestamp/step data
   gracefully.

4. EstimationHistoricalStatsProvider — ACMS ContextStrategy
   implementation that injects historical stats as a high-relevance
   context fragment into the estimation actor's hot context tier.
   Handles service failures, empty history, and budget constraints.

Testing: 27 Behave scenarios covering model validation, service logic
(empty/normal/edge cases), ACMS provider integration, and repository
methods. 7 Robot integration tests with helper script. All quality
gates pass: lint, typecheck (0 errors), unit_tests (556 features,
13798 scenarios), integration_tests (1875 tests), e2e_tests, and
coverage_report (98.6%, threshold 97%).

ISSUES CLOSED: #652
brent.edwards added this to the v3.5.0 milestone 2026-04-02 09:11:13 +00:00
freemo commented 2026-04-02 17:03:35 +00:00
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Review claimed by reviewer pool instance reviewer-pool-1. Dispatching independent code review.

Review claimed by reviewer pool instance reviewer-pool-1. Dispatching independent code review.
freemo approved these changes 2026-04-02 17:07:51 +00:00
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Code Review — APPROVED ✅

Summary

This PR implements the historical plan statistics infrastructure for the estimation actor's context assembly, as specified in docs/specification.md lines 19077-19081. The implementation is well-structured, thoroughly tested, and aligns with the specification.

What Was Reviewed

  • 9 files, 1780 lines of new code across domain model, repository, application service, ACMS context provider, Behave tests (27 scenarios), and Robot integration tests (7 tests).

Specification Alignment ✅

  • The spec states the estimation actor analyzes "Historical data from similar plans (if available)" — this PR delivers exactly that capability.
  • HistoricalPlanStats is a proper frozen Pydantic v2 value object in the domain layer.
  • HistoricalPlanStatsService is a stateless application service following the project's service patterns.
  • EstimationHistoricalStatsProvider correctly implements the ContextStrategy protocol from acms_service.py.
  • Empty history (first-run) returns a valid empty stats object, not an error — matching acceptance criteria.

Architecture & Design ✅

  • Clean separation: domain model → repository → application service → ACMS provider.
  • Module boundaries respected: domain model in domain/models/core/, service in application/services/, repository extension in infrastructure/database/.
  • The ACMS provider correctly integrates with the existing pipeline via ContextStrategy protocol (name, capabilities, can_handle, assemble, explain).
  • Budget-aware assembly with proper token estimation and budget constraint checking.

Code Quality ✅

  • All files under 500 lines.
  • Proper module and function docstrings throughout.
  • Imports at top of file.
  • No # type: ignore suppressions in new code.
  • Structured logging with structlog.
  • Fail-fast argument validation in service and repository methods.
  • Exception handling follows project patterns (propagation with meaningful wrapping).

Test Quality ✅

  • 27 Behave scenarios covering:
    • Model validation (creation, immutability, field constraints, serialization)
    • Service logic (empty history, normal aggregation, edge cases with missing data)
    • ACMS provider (confidence, assembly, budget constraints, failure handling)
    • Repository method behavior
  • 7 Robot integration tests with helper script following project patterns.
  • Edge cases well-covered: negative costs, out-of-bounds success rate, missing timestamps, missing cost metadata, tiny budget, failing service.

Minor Observations (non-blocking)

  1. list[Any] return type on get_completed_plans_by_action: The repository method and service use Any for plan objects rather than the concrete domain type. This is pragmatic given the codebase's import complexity but is a minor typing weakness.
  2. Broad exception catch in provider assemble(): except Exception is very broad, but defensible for a context provider that must never crash the ACMS pipeline. The exc_info=True logging ensures failures are observable.
  3. Mutable set_action_name on provider: The provider has a set_action_name configuration method that mutates state. This is a configuration concern, not a thread-safety issue, but worth noting for future refactoring.

Commit Message ✅

  • Follows Conventional Changelog format.
  • Matches issue metadata exactly.
  • ISSUES CLOSED: #652 footer present.

Verdict

All acceptance criteria from issue #652 are met. The implementation is clean, well-tested, and spec-aligned. Approving and merging.

## Code Review — APPROVED ✅ ### Summary This PR implements the historical plan statistics infrastructure for the estimation actor's context assembly, as specified in `docs/specification.md` lines 19077-19081. The implementation is well-structured, thoroughly tested, and aligns with the specification. ### What Was Reviewed - **9 files**, **1780 lines** of new code across domain model, repository, application service, ACMS context provider, Behave tests (27 scenarios), and Robot integration tests (7 tests). ### Specification Alignment ✅ - The spec states the estimation actor analyzes "Historical data from similar plans (if available)" — this PR delivers exactly that capability. - `HistoricalPlanStats` is a proper frozen Pydantic v2 value object in the domain layer. - `HistoricalPlanStatsService` is a stateless application service following the project's service patterns. - `EstimationHistoricalStatsProvider` correctly implements the `ContextStrategy` protocol from `acms_service.py`. - Empty history (first-run) returns a valid empty stats object, not an error — matching acceptance criteria. ### Architecture & Design ✅ - Clean separation: domain model → repository → application service → ACMS provider. - Module boundaries respected: domain model in `domain/models/core/`, service in `application/services/`, repository extension in `infrastructure/database/`. - The ACMS provider correctly integrates with the existing pipeline via `ContextStrategy` protocol (name, capabilities, can_handle, assemble, explain). - Budget-aware assembly with proper token estimation and budget constraint checking. ### Code Quality ✅ - All files under 500 lines. - Proper module and function docstrings throughout. - Imports at top of file. - No `# type: ignore` suppressions in new code. - Structured logging with `structlog`. - Fail-fast argument validation in service and repository methods. - Exception handling follows project patterns (propagation with meaningful wrapping). ### Test Quality ✅ - **27 Behave scenarios** covering: - Model validation (creation, immutability, field constraints, serialization) - Service logic (empty history, normal aggregation, edge cases with missing data) - ACMS provider (confidence, assembly, budget constraints, failure handling) - Repository method behavior - **7 Robot integration tests** with helper script following project patterns. - Edge cases well-covered: negative costs, out-of-bounds success rate, missing timestamps, missing cost metadata, tiny budget, failing service. ### Minor Observations (non-blocking) 1. **`list[Any]` return type on `get_completed_plans_by_action`**: The repository method and service use `Any` for plan objects rather than the concrete domain type. This is pragmatic given the codebase's import complexity but is a minor typing weakness. 2. **Broad exception catch in provider `assemble()`**: `except Exception` is very broad, but defensible for a context provider that must never crash the ACMS pipeline. The `exc_info=True` logging ensures failures are observable. 3. **Mutable `set_action_name` on provider**: The provider has a `set_action_name` configuration method that mutates state. This is a configuration concern, not a thread-safety issue, but worth noting for future refactoring. ### Commit Message ✅ - Follows Conventional Changelog format. - Matches issue metadata exactly. - `ISSUES CLOSED: #652` footer present. ### Verdict All acceptance criteria from issue #652 are met. The implementation is clean, well-tested, and spec-aligned. Approving and merging.
freemo scheduled this pull request to auto merge when all checks succeed 2026-04-02 17:08:00 +00:00
freemo commented 2026-04-02 17:26:09 +00:00
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Review claimed by reviewer pool instance reviewer-pool-1. Dispatching independent code review.

Review claimed by reviewer pool instance reviewer-pool-1. Dispatching independent code review.
freemo commented 2026-04-02 17:28:57 +00:00
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🤖 Backlog Groomer (groomer-1): Closing as duplicate of #652.

Issue #652 (feat(estimation): build historical plan statistics query service for estimation) is the canonical version with full labels (MoSCoW/Should have, Priority/Medium, State/Completed, Type/Feature) and milestone v3.5.0. This issue is an exact title duplicate.

🤖 **Backlog Groomer (groomer-1):** Closing as duplicate of #652. Issue #652 (`feat(estimation): build historical plan statistics query service for estimation`) is the canonical version with full labels (`MoSCoW/Should have`, `Priority/Medium`, `State/Completed`, `Type/Feature`) and milestone `v3.5.0`. This issue is an exact title duplicate.
freemo closed this pull request 2026-04-02 17:29:02 +00:00
freemo approved these changes 2026-04-02 17:30:19 +00:00
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Independent Code Review — APPROVED ✅

Reviewer: Independent PR reviewer (reviewer-pool dispatch)

What Was Reviewed

All new source files in this PR were read and analyzed in full:

  • src/cleveragents/domain/models/core/historical_plan_stats.py (~130 lines)
  • src/cleveragents/application/services/historical_plan_stats_service.py (~230 lines)
  • src/cleveragents/application/services/estimation_context_provider.py (~190 lines)
  • Repository method get_completed_plans_by_action() on LifecyclePlanRepository
  • Commit message and PR metadata

Specification Alignment ✅

  • The spec (lines 19077-19081) states the estimation actor analyzes "Historical data from similar plans (if available)" — this PR delivers exactly that capability.
  • HistoricalPlanStats is a proper frozen Pydantic v2 value object in the domain layer, consistent with the project's domain model patterns.
  • EstimationHistoricalStatsProvider correctly implements the ContextStrategy protocol from acms_service.py, integrating with the ACMS pipeline as specified.
  • Empty history (first-run) returns a valid empty stats object — matching acceptance criteria #5.

Architecture & Design ✅

  • Clean layered separation: domain model → repository → application service → ACMS provider.
  • Module boundaries respected: domain model in domain/models/core/, service in application/services/, repository extension in infrastructure/database/.
  • TYPE_CHECKING import for LifecyclePlanRepository avoids circular imports — good practice.
  • Budget-aware assembly with token estimation and constraint checking.

Code Quality ✅

  • All files under 500 lines.
  • Proper module and function docstrings throughout.
  • Imports at top of file.
  • No # type: ignore suppressions.
  • Structured logging with structlog.
  • Fail-fast argument validation (if not action_name, if limit < 1).
  • ConfigDict(frozen=True, allow_inf_nan=False) — proper immutability and safety.

Correctness ✅

  • _percentile() uses linear interpolation with proper edge cases (empty list, single element).
  • _extract_duration_seconds() correctly prefers applied_at over execute_completed_at for full lifecycle duration.
  • Success rate calculation correctly counts only applied state as success.
  • Step count conversion from 0-indexed to count (+ 1) is correct.
  • as_context_dict() only includes non-zero metrics — compact and useful.

Commit Message ✅

  • Follows Conventional Changelog format: feat(estimation): ...
  • Matches issue #652 metadata exactly.
  • ISSUES CLOSED: #652 footer present.
  • Detailed body explaining all four components.

Minor Observations (non-blocking)

  1. _extract_cost() docstring: Mentions "falls back to the DB column equivalent" but the code returns None when cost_metadata is unavailable. The docstring is slightly misleading but the behavior is correct — plans without cost tracking should be excluded from cost statistics.
  2. Sequence[Any] for plans: Pragmatic given import complexity, but a Protocol with the required attributes would be more type-safe in the future.
  3. Token estimation (~4 chars/token): Rough but acceptable for budget checking — the fragment is small enough that precision doesn't matter much.

Verdict

All 6 acceptance criteria from issue #652 are met. The implementation is clean, well-tested (27 Behave + 7 Robot), and spec-aligned. No blocking issues found. Approving.

## Independent Code Review — APPROVED ✅ ### Reviewer: Independent PR reviewer (reviewer-pool dispatch) ### What Was Reviewed All new source files in this PR were read and analyzed in full: - `src/cleveragents/domain/models/core/historical_plan_stats.py` (~130 lines) - `src/cleveragents/application/services/historical_plan_stats_service.py` (~230 lines) - `src/cleveragents/application/services/estimation_context_provider.py` (~190 lines) - Repository method `get_completed_plans_by_action()` on `LifecyclePlanRepository` - Commit message and PR metadata ### Specification Alignment ✅ - The spec (lines 19077-19081) states the estimation actor analyzes "Historical data from similar plans (if available)" — this PR delivers exactly that capability. - `HistoricalPlanStats` is a proper frozen Pydantic v2 value object in the domain layer, consistent with the project's domain model patterns. - `EstimationHistoricalStatsProvider` correctly implements the `ContextStrategy` protocol from `acms_service.py`, integrating with the ACMS pipeline as specified. - Empty history (first-run) returns a valid empty stats object — matching acceptance criteria #5. ### Architecture & Design ✅ - Clean layered separation: domain model → repository → application service → ACMS provider. - Module boundaries respected: domain model in `domain/models/core/`, service in `application/services/`, repository extension in `infrastructure/database/`. - `TYPE_CHECKING` import for `LifecyclePlanRepository` avoids circular imports — good practice. - Budget-aware assembly with token estimation and constraint checking. ### Code Quality ✅ - All files under 500 lines. - Proper module and function docstrings throughout. - Imports at top of file. - No `# type: ignore` suppressions. - Structured logging with `structlog`. - Fail-fast argument validation (`if not action_name`, `if limit < 1`). - `ConfigDict(frozen=True, allow_inf_nan=False)` — proper immutability and safety. ### Correctness ✅ - `_percentile()` uses linear interpolation with proper edge cases (empty list, single element). - `_extract_duration_seconds()` correctly prefers `applied_at` over `execute_completed_at` for full lifecycle duration. - Success rate calculation correctly counts only `applied` state as success. - Step count conversion from 0-indexed to count (`+ 1`) is correct. - `as_context_dict()` only includes non-zero metrics — compact and useful. ### Commit Message ✅ - Follows Conventional Changelog format: `feat(estimation): ...` - Matches issue #652 metadata exactly. - `ISSUES CLOSED: #652` footer present. - Detailed body explaining all four components. ### Minor Observations (non-blocking) 1. **`_extract_cost()` docstring**: Mentions "falls back to the DB column equivalent" but the code returns `None` when `cost_metadata` is unavailable. The docstring is slightly misleading but the behavior is correct — plans without cost tracking should be excluded from cost statistics. 2. **`Sequence[Any]` for plans**: Pragmatic given import complexity, but a `Protocol` with the required attributes would be more type-safe in the future. 3. **Token estimation (~4 chars/token)**: Rough but acceptable for budget checking — the fragment is small enough that precision doesn't matter much. ### Verdict All 6 acceptance criteria from issue #652 are met. The implementation is clean, well-tested (27 Behave + 7 Robot), and spec-aligned. No blocking issues found. Approving.
HAL9000 commented 2026-04-10 07:18:56 +00:00
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🕵️ Bug Hunter Worker Started

Instance ID: bug-hunt-cycle-2-batch2-worker12
Module Focus: Agent definitions registry, lifecycle, and metadata management
Clone Directory: /tmp/bug-hunt-cycle-2-batch2-worker12
Timestamp: 2026-04-10 07:15

Scanning Plan

This worker instance will perform comprehensive bug detection analysis on the agent definition and management systems, focusing on:

  • Actor registry thread safety and validation
  • Schema validation gaps and edge cases
  • Template engine security and injection vulnerabilities
  • Configuration loading and environment variable handling
  • Concurrency issues in registration and lifecycle operations

Coordination

Other automation agents can track this worker's progress through this tracking issue and related bug reports.

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Worker: Bug Detection | Agent: bug-hunter

# 🕵️ Bug Hunter Worker Started **Instance ID**: bug-hunt-cycle-2-batch2-worker12 **Module Focus**: Agent definitions registry, lifecycle, and metadata management **Clone Directory**: /tmp/bug-hunt-cycle-2-batch2-worker12 **Timestamp**: 2026-04-10 07:15 ## Scanning Plan This worker instance will perform comprehensive bug detection analysis on the agent definition and management systems, focusing on: - Actor registry thread safety and validation - Schema validation gaps and edge cases - Template engine security and injection vulnerabilities - Configuration loading and environment variable handling - Concurrency issues in registration and lifecycle operations ## Coordination Other automation agents can track this worker's progress through this tracking issue and related bug reports. --- **Automated by CleverAgents Bot** Worker: Bug Detection | Agent: bug-hunter
HAL9000 commented 2026-04-10 08:34:09 +00:00
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🕵️ Bug Hunter Worker 12 - Final Report

Instance ID: bug-hunt-cycle-2-batch2-worker12
Module Focus: Agent definitions registry, lifecycle, and metadata management
Status: ✅ COMPLETED
Timestamp: 2026-04-10 07:25

📊 Analysis Summary

Metric Count
Total Findings 7
Critical Issues 2
Medium Issues 4
Duplicate Detected 1
Issues Filed 6
TDD Issues Created 1

🐛 Confirmed Defects Filed

Critical Security Issues

  • #7056 - [security] Environment variable default value injection vulnerability in ActorConfiguration
  • #7172 - [security] Template rendering resource exhaustion vulnerability allows denial of service
  • #7173 - [TDD] Testing issue for template rendering DoS vulnerability

Medium Priority Issues

  • #7095 - [concurrency] Race condition in ActorRegistry.ensure_built_in_actors()
  • #7127 - [consistency] Actor namespace collision between user-defined and built-in actors
  • #7148 - [resource] Memory leak in ActorLoader cache
  • #7157 - [type-safety] RouteDefinition validation assumes unvalidated config structure

Duplicate Detected

  • YAML template post-processing logic error → Already tracked as #6420

🎯 Coverage Areas Analyzed

✅ Security & Validation

  • Environment variable interpolation patterns
  • Template rendering security (Jinja2 sandboxing)
  • Resource exhaustion vulnerabilities
  • Input validation gaps

✅ Concurrency & Thread Safety

  • Actor registry concurrent access patterns
  • Cache synchronization issues
  • Built-in actor generation races

✅ Data Flow & Consistency

  • Namespace collision scenarios
  • Configuration validation logic
  • Type safety in dynamic structures

✅ Resource Management

  • Memory leak patterns in long-running processes
  • Cache lifecycle management
  • Unbounded resource consumption

🛠️ Tools & Methods Used

  • Static Code Analysis - Manual review of core actor management modules
  • Security Pattern Analysis - Searched for injection, exhaustion, and validation bypasses
  • Concurrency Analysis - Examined thread safety patterns and race conditions
  • Type Safety Review - Identified runtime crash scenarios from malformed configs

📍 Key Files Analyzed

  • src/cleveragents/actor/registry.py - Actor registration and lifecycle
  • src/cleveragents/actor/config.py - Configuration parsing and env var handling
  • src/cleveragents/actor/schema.py - Schema validation and graph analysis
  • src/cleveragents/actor/loader.py - YAML discovery and caching
  • src/cleveragents/actor/yaml_template_engine.py - Template processing security

⚠️ Critical Recommendations

  1. Immediate: Fix environment variable injection vulnerability (#7056) - potential security risk
  2. High Priority: Address template DoS vulnerability (#7172) - can crash services
  3. Medium Priority: Fix actor namespace conflicts (#7127) - breaks existing configs

🧹 Cleanup Status

  • ✅ Clone directory /tmp/bug-hunt-cycle-2-batch2-worker12 will be cleaned up on exit
  • ✅ All findings properly documented with evidence and reproduction steps
  • ✅ Issues linked to appropriate parent epics and milestones

Automated by CleverAgents Bot
Worker: Bug Detection | Agent: bug-hunter

# 🕵️ Bug Hunter Worker 12 - Final Report **Instance ID**: bug-hunt-cycle-2-batch2-worker12 **Module Focus**: Agent definitions registry, lifecycle, and metadata management **Status**: ✅ COMPLETED **Timestamp**: 2026-04-10 07:25 ## 📊 Analysis Summary | Metric | Count | |--------|-------| | **Total Findings** | 7 | | **Critical Issues** | 2 | | **Medium Issues** | 4 | | **Duplicate Detected** | 1 | | **Issues Filed** | 6 | | **TDD Issues Created** | 1 | ## 🐛 Confirmed Defects Filed ### Critical Security Issues - **#7056** - [security] Environment variable default value injection vulnerability in ActorConfiguration - **#7172** - [security] Template rendering resource exhaustion vulnerability allows denial of service - **#7173** - [TDD] Testing issue for template rendering DoS vulnerability ### Medium Priority Issues - **#7095** - [concurrency] Race condition in ActorRegistry.ensure_built_in_actors() - **#7127** - [consistency] Actor namespace collision between user-defined and built-in actors - **#7148** - [resource] Memory leak in ActorLoader cache - **#7157** - [type-safety] RouteDefinition validation assumes unvalidated config structure ### Duplicate Detected - YAML template post-processing logic error → Already tracked as #6420 ## 🎯 Coverage Areas Analyzed ### ✅ Security & Validation - Environment variable interpolation patterns - Template rendering security (Jinja2 sandboxing) - Resource exhaustion vulnerabilities - Input validation gaps ### ✅ Concurrency & Thread Safety - Actor registry concurrent access patterns - Cache synchronization issues - Built-in actor generation races ### ✅ Data Flow & Consistency - Namespace collision scenarios - Configuration validation logic - Type safety in dynamic structures ### ✅ Resource Management - Memory leak patterns in long-running processes - Cache lifecycle management - Unbounded resource consumption ## 🛠️ Tools & Methods Used - **Static Code Analysis** - Manual review of core actor management modules - **Security Pattern Analysis** - Searched for injection, exhaustion, and validation bypasses - **Concurrency Analysis** - Examined thread safety patterns and race conditions - **Type Safety Review** - Identified runtime crash scenarios from malformed configs ## 📍 Key Files Analyzed - `src/cleveragents/actor/registry.py` - Actor registration and lifecycle - `src/cleveragents/actor/config.py` - Configuration parsing and env var handling - `src/cleveragents/actor/schema.py` - Schema validation and graph analysis - `src/cleveragents/actor/loader.py` - YAML discovery and caching - `src/cleveragents/actor/yaml_template_engine.py` - Template processing security ## ⚠️ Critical Recommendations 1. **Immediate**: Fix environment variable injection vulnerability (#7056) - potential security risk 2. **High Priority**: Address template DoS vulnerability (#7172) - can crash services 3. **Medium Priority**: Fix actor namespace conflicts (#7127) - breaks existing configs ## 🧹 Cleanup Status - ✅ Clone directory `/tmp/bug-hunt-cycle-2-batch2-worker12` will be cleaned up on exit - ✅ All findings properly documented with evidence and reproduction steps - ✅ Issues linked to appropriate parent epics and milestones --- **Automated by CleverAgents Bot** Worker: Bug Detection | Agent: bug-hunter
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auto/needs-reevaluation
Controller deferred this PR; awaiting Phase 6+ scope-evaluator or operator re-enablement.

  
controller-managed
Auto-agents controller manages this PR/issue (see tools/controller/deploy/RUNBOOK.md). Remove this label to abandon controller management.

  
auto/blocked-by-deps
PR blocked by an open issue dependency. Operator must close the dep (or remove the dependency link) before the merge driver can act. Auto-cleared by merge_drive when no open deps remain.

  
auto/ci-timeout
Most recent merge cycle hit CI timeout. Driver excludes this PR while last merge_cycle row is < 30 min old; label persists thereafter as visible history.

  
auto/claimed-implementer
Currently being processed by an implementer worker.

  
auto/claimed-merge
Currently being processed by the merge driver.

  
auto/claimed-reviewer
Currently being processed by a reviewer worker.

  
auto/driver-down
Merge driver heartbeat stale; pipeline halted. Closed automatically on next clean tick.

  
auto/invariant-violation
Detected master commit violating the strict merge invariant. Tracked as an issue (not a PR label); kept here for label completeness.

  
auto/last-attempt-tier-0
In-cycle escalation: most recent attempt ran at the Tier 0 slot (`tier-0`). Slot's model defined in .opencode/models/tiers.yaml.

  
auto/last-attempt-tier-1
In-cycle escalation: most recent attempt ran at the Tier 1 slot (`tier-1`). Slot's model defined in .opencode/models/tiers.yaml.

  
auto/last-attempt-tier-2
In-cycle escalation: most recent attempt ran at the Tier 2 slot (`tier-2`). Slot's model defined in .opencode/models/tiers.yaml. Gated behind IMPLEMENTER_ESCALATION_TIER2_ENABLED.

  
auto/last-attempt-tier-min
In-cycle escalation: most recent attempt ran at the Tier -1 slot (`tier-min`). Slot's model defined in .opencode/models/tiers.yaml. Suffix is ``-min`` (not ``--1``) so the Forgejo UI reads naturally.

  
Automation Tracking
Tracking issues used by the AI Automation system for agents to communicate and report.

  
auto/needs-conflict-resolution
Rebase conflict needs LLM conflict-resolver.

  
auto/needs-implementer
Failing CI needs implementer attention.

  
auto/postmortem
Documenting a driver incident or rollback.

  
auto/ready-to-merge
Reviewer has APPROVED this PR and no later REQUEST_CHANGES is outstanding. The merge driver requires this label to even consider a PR for merging. Set by the reviewer worker on APPROVE; cleared on REQUEST_CHANGES.

  
auto/restart-throttled
Train repeatedly lost master-tempo races. Driver excludes via merge_cycle until cooldown elapses; label persists as visible history.

  
auto/revert
Revert PR backing out an invariant violation. Fast-tracked through the merge driver.

  
auto/sentinel
Sentinel PR duplicated from upstream into a personal fork by tools/duplicate_prs_to_fork.py for pipeline testing. Lives only in the fork; the canonical pipeline never sees it.

  
auto/stale-inactivity
No implementer activity for N days. Flagged for human review. Auto-cleared on next push to head branch.

  
auto/unstable
Repeatedly fails on current master (>= 3 ci-fail-on-rebased-sha releases in 12 h). Excluded from driver until human triage.

  
Blocked
A ticket in a blocked state and unable to complete until some other task is completed first.

  
Bounty
$100
 A bounty of $100 for any open-source contributor who provides a MR that solves this issue

  
Bounty
$1000
 A bounty of $1000 for any open-source contributor who provides a MR that solves this issue

  
Bounty
$10000
 A bounty of $10000 for any open-source contributor who provides a MR that solves this issue

  
Bounty
$20
 A bounty of $20 for any open-source contributor who provides a MR that solves this issue

  
Bounty
$2000
 A bounty of $2000 for any open-source contributor who provides a MR that solves this issue

  
Bounty
$250
 A bounty of $250 for any open-source contributor who provides a MR that solves this issue

  
Bounty
$50
 A bounty of $50 for any open-source contributor who provides a MR that solves this issue

  
Bounty
$500
 A bounty of $500 for any open-source contributor who provides a MR that solves this issue

  
Bounty
$5000
 A bounty of $5000 for any open-source contributor who provides a MR that solves this issue

  
Bounty
$750
 A bounty of $750 for any open-source contributor who provides a MR that solves this issue

  
MoSCoW
Could have
 Could have feature in order to satisfy the epic/legendary.

  
MoSCoW
Must have
 Must have feature in order to satisfy the epic/legendary.

  
MoSCoW
Should have
 Should have feature in order to satisfy the epic/legendary.

  
Needs Feedback
There are questions in the ticket that can not be completed until the project owner provides clarity.

  
Points
1
 1 man-hours worth of work for an expert with no learning curve.

  
Points
13
 13 man-hours worth of work for an expert with no learning curve.

  
Points
2
 2 man-hours worth of work for an expert with no learning curve.

  
Points
21
 21 man-hours worth of work for an expert with no learning curve.

  
Points
3
 3 man-hours worth of work for an expert with no learning curve.

  
Points
34
 34 man-hours worth of work for an expert with no learning curve.

  
Points
5
 5 man-hours worth of work for an expert with no learning curve.

  
Points
55
 55 man-hours worth of work for an expert with no learning curve.

  
Points
8
 8 man-hours worth of work for an expert with no learning curve.

  
Points
88
 88 man-hours worth of work for an expert with no learning curve.

  
Priority
Backlog
 This ticket has backlogged priority and is not to be worked on yet

  
Priority
CI Blocker
 Critical priority issue that blocks CI/CD pipeline and prevents PR merges

  
Priority
Critical
 The priority is critical

  
Priority
High
 The priority is high

  
Priority
Low
 The priority is low

  
Priority
Medium
 The priority is medium

  
Signed-off: Owner
When an epic or legendary is in review it must be signed off by owner, tech lead, and scrum master before being marked as completed.

  
Signed-off: Scrum Master
When an epic or legendary is in review it must be signed off by owner, tech lead, and scrum master before being marked as completed.

  
Signed-off: Tech Lead
When an epic or legendary is in review it must be signed off by owner, tech lead, and scrum master before being marked as completed.

  
Spike
A ticket for learning a tool or technology that is needed to be able to do future planning and design.

  
State
Completed
 The ticket has been fully implemented, completed, and merged with the source code. This label should only be applied once a ticket is closed.

  
State
Duplicate
 A ticket that represents the same content as an existing ticket.

  
State
In Progress
 A ticket that is actively being developed.

  
State
In Review
 A ticket that has had some code completed to implement but is waiting to pass peer review and is not yet merged in.

  
State
Paused
 This ticket's work started but wasn't finished. It's on hold (likely in a feature branch) and will be resumed later, either due to a blocker or a delay.

  
State
Unverified
 All new tickets start in this state. A developer may set it to show the ticket is unverified. This means we haven't agreed to work on it. It will either move to a verified state or be closed as wontdo.

  
State
Verified
 The issue has been verified by a developer as legitimate. It will be worked on and verified tickets are now considered part of the backlog.

  
State
Wont Do
 This ticket has been decided it wont be done. This may mean the bug has been determined to not be real (cant verify) or the feature is one we have decided we dont want to adopt.

  
Type
Automation
 Any edits or discussion about the AI automated coding system.

  
Type
Bug
 Something that doesnt work as intended.

  
Type
Discussion
 Anytime a ticket represents a discussion about a subject and doesnt fall into one of the other categories.

  
Type
Documentation
 An error or improvement needed in the documentation.

  
Type
Epic
 Any first tier epic. That is, an epic which contains only issues as children and will not have sub-epics.

  
Type
Feature
 Some new functionality not present.

  
Type
Legendary
 A type of Epic which will contain other Epics.

  
Type
Refactor
 A code change that restructures existing code without changing its external behavior.

  
Type
Support
 Someone needs help using the project.

  
Type
Task
 A generic task that doesnt fit into the other type categories.

  
Type
Testing
 Work exclusively focusing on fixing or expanding testing.

No labels
auto/needs-reevaluation
controller-managed
auto/blocked-by-deps
auto/ci-timeout
auto/claimed-implementer
auto/claimed-merge
auto/claimed-reviewer
auto/driver-down
auto/invariant-violation
auto/last-attempt-tier-0
auto/last-attempt-tier-1
auto/last-attempt-tier-2
auto/last-attempt-tier-min
Automation Tracking
auto/needs-conflict-resolution
auto/needs-implementer
auto/postmortem
auto/ready-to-merge
auto/restart-throttled
auto/revert
auto/sentinel
auto/stale-inactivity
auto/unstable
Blocked
Bounty
$100
Bounty
$1000
Bounty
$10000
Bounty
$20
Bounty
$2000
Bounty
$250
Bounty
$50
Bounty
$500
Bounty
$5000
Bounty
$750
MoSCoW
Could have
MoSCoW
Must have
MoSCoW
Should have
Needs Feedback
Points
1
Points
13
Points
2
Points
21
Points
3
Points
34
Points
5
Points
55
Points
8
Points
88
Priority
Backlog
Priority
CI Blocker
Priority
Critical
Priority
High
Priority
Low
Priority
Medium
Signed-off: Owner
Signed-off: Scrum Master
Signed-off: Tech Lead
Spike
State
Completed
State
Duplicate
State
In Progress
State
In Review
State
Paused
State
Unverified
State
Verified
State
Wont Do
Type
Automation
Type
Bug
Type
Discussion
Type
Documentation
Type
Epic
Type
Feature
Type
Legendary
Type
Refactor
Type
Support
Type
Task
Type
Testing
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v3.5.0
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Reference
cleveragents/cleveragents-core!1295
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