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Project Status

Date: 2026-03-25 Scope: agent-skills runtime + agent-skill-registry consistency check

Executive Summary

The project has reached full capability coverage.

  • All 122 capabilities are functional with runtime bindings and passing tests.
  • The 37 formerly stub-only capabilities (identity.*, integration.*, task.*) now use in-memory baseline services.
  • Registry governance: 0 metadata issues, 0 uncovered domains, 0 stubs.
  • Critical paths are covered by smoke checks in CI.
  • High-risk services are hardened and instrumented.
  • DAG-based step scheduler enables parallel execution with backward-compatible defaults.
  • CognitiveState v1 extends ExecutionState with structured cognitive blocks (frame, working, output, trace, extensions).
  • Cognitive hints provide semantic type annotations for auto-wire resolution across capabilities.
  • Safety enforcement protects side-effecting capabilities via trust levels, gates, and confirmation.
  • agent.trace v0.1.0 and research.synthesize v0.2.0 are validated and closed.

Verified Quality Gates

Latest local verification snapshot:

  • Functional smoke suite: 8/8 pass
  • Capability contracts: 122/122 pass (0 violations)
  • Runtime coverage: 122/122 capabilities executable (ratio 1.0)
  • Skill executability: 35/35 executable (ratio 1.0)
  • Runtime inventory: 122 capabilities, 122 official defaults, 23 services, 35 skills
  • Scheduler functional tests: 5/5
  • Scheduler stress tests: 5/5
  • CognitiveState v1 regression tests: 86/86
  • CognitiveState v1 integration tests: 99/99
  • Cognitive hints tests: 27/27
  • Safety enforcement tests: 44/44
  • Registry governance guardrails: metadata issues 0, uncovered domains 0

Catalog context (canonical source of total definitions):

  • Registry catalog: 122 capabilities, 35 skills, 27 domains
  • Runtime inventory matches registry — no drift
  • Canonical metrics reference: ../agent-skill-registry/docs/CANONICAL_METRICS.md

Security and Reliability Status

Implemented and active:

  • code.snippet.execute: sandboxed builtins, input/output size limits, timeout guard
  • web.page.fetch: scheme allow-list and SSRF guard, timeout and response limits
  • pdf.document.read: file/path validation, size and page limits
  • audio.speech.transcribe: format and size validation

Safety Enforcement Status

Implemented and active:

  • Safety block in capability contracts (v2 enforcement: required for side_effects: true)
  • Runtime trust-level enforcement (sandbox < standard < elevated < privileged)
  • Human confirmation gate (requires_confirmation + confirmed_capabilities)
  • Mandatory pre/post gates with per-gate failure policies (block, warn, degrade, require_human)
  • Degraded step status for graceful degrade policy
  • 3 typed safety errors: SafetyTrustLevelError, SafetyGateFailedError, SafetyConfirmationRequiredError
  • Safety vocabulary: vocabulary/safety_vocabulary.yaml (trust_levels, data_classifications, failure_policies, allowed_targets, scope_constraints)
  • Registry validation enforces safety vocabulary and v2 policy
  • 5 capabilities annotated: agent.task.delegate, code.snippet.execute, email.message.send, memory.entry.store, message.notification.send

Observability Status

Implemented and active:

  • Structured JSON logs for runtime lifecycle and high-risk services
  • End-to-end trace correlation with trace_id
  • Sensitive field redaction and payload truncation guards
  • CLI trace support: --trace-id for run and trace commands

See docs/OBSERVABILITY.md for full details.

Skill Governance Status

Implemented baseline:

  • Operational skill quality catalog generator: tooling/build_skill_quality_catalog.py
  • Output artifact separated from registry source: artifacts/skill_quality.json
  • Cold-start support through internal readiness scoring and lab-verified lifecycle path
  • Field maturity path through optional usage and feedback evidence inputs
  • Runtime binding fallback policy with mandatory official default terminal fallback
  • Fallback policy verifier: tooling/verify_binding_fallback_policy.py
  • Binding conformance profiles (strict|standard|experimental) with load-time validation
  • Runtime conformance enforcement via required profile (optional, default-friendly)
  • Explainability surface for capability resolution in CLI (explain-capability)
  • Conformance verifiers: tooling/verify_binding_conformance_layer.py, tooling/verify_conformance_enforcement.py, tooling/verify_binding_conformance_suite.py
  • Explainability exposed on customer adapters:
    • HTTP POST /v1/capabilities/{capability_id}/explain
    • MCP tool capability.explain
  • Governance discovery exposed on customer adapters:
    • HTTP GET /v1/skills/governance
    • MCP tool skill.governance.list
    • CLI skill-governance
  • Governance wiring with usage ingestion from runtime logs:
    • tooling/ingest_skill_usage_from_logs.py
    • quality scoring now includes conformance signals per skill

Optional evidence files:

  • artifacts/skill_lab_validation.json
  • artifacts/skill_usage_30d.json
  • artifacts/skill_feedback_30d.json

Current default behavior without evidence files:

  • Skills receive internal-evidence classification and readiness-based lifecycle state
  • This avoids forcing all skills into low-confidence labels during initial rollout

CI Status

Current workflow gates:

  • pin_drift_guard: enforces maximum drift budget between REGISTRY_REF and registry origin/main
  • smoke: critical capabilities
  • contracts: capability output shape/type/error contracts
  • registry_consistency: registry validation + catalog freshness guard
  • runtime_canary: binding fallback/conformance + customer-facing neutral checks + coverage/executability ratio enforcement
  • full_batch: scheduled/on-demand full suite

Registry Consistency Review

Registry documentation is already complete and remains the source of truth.

Current review result:

  • Registry validation passes
  • Catalog generation completes successfully
  • Stats generation completes successfully
  • No inconsistencies detected in current baseline

Documentation Map

  • docs/RUNNER_GUIDE.md: runtime runner architecture and operations
  • docs/COGNITIVE_STATE_V1.md: CognitiveState v1 cognitive execution model reference
  • docs/SCHEDULER.md: DAG-based step scheduler (parallel/sequential execution)
  • docs/RUNNER_GUIDE.md § 12: Safety enforcement (trust levels, gates, confirmation)
  • docs/OBSERVABILITY.md: logging, trace_id, redaction, tuning, CognitiveState trace
  • docs/AGENT_TRACE_DRY_RUN_GUIDE.md: agent.trace practical usage and dry-run scenarios
  • docs/PRE_MCP_OPENAPI_READINESS.md: readiness checklist and next integrations
  • docs/PROJECT_STATUS.md: current project closure snapshot

Closed Skills

CognitiveState v1

ExecutionState extended with typed cognitive blocks: FrameState (immutable reasoning context), WorkingState (10 cognitive slots), OutputState (result metadata), TraceState (per-step data lineage + aggregate metrics), extensions (plugin namespace). Reference resolver supports 7 namespaces with path traversal. Output mapper supports 5 writable namespaces with 4 merge strategies (overwrite, append, deep_merge, replace). Fully backward-compatible — all legacy vars/outputs/events behavior preserved. Test coverage: 86 regression + 99 integration tests.

agent.trace v0.1.0

3-step pipeline: validate_events → analyze_trace → monitor_trace. Explicit depends_on declarations. Sidecar classification (attach to run/output/transcript). Tested with baseline, mitigated, and real-agent dry-run scenarios.

research.synthesize v0.2.0

Rewritten from 5 steps / 6 LLM calls to 2 steps / 1 LLM call (fast path). Steps: research.source.retrieve (0 LLM, resolves PDF/URL/text) → model.output.generate (1 LLM). Stable output contract: 9 fields (summary, key_points, insights, tensions, uncertainties, source_coverage, next_steps, synthesis_quality, human_readable). Tested with 3-item corpus (23s) and 6-page legal PDF (22s).

model.output.generate Binding Tuning

  • max_tokens: 16384 (prevents output truncation on large contexts)
  • timeout_seconds: 120 (supports large-context LLM calls)
  • Model: gpt-4o-mini via OpenAI Chat Completions

Known Non-Blocking Notes

  • CLI trace command prints no additional output when input mapping fails before step output emission unless explicit trace callback output is enabled by the caller; runtime logs still capture failures.
  • Some console environments may render Unicode symbols with encoding artifacts; this does not affect runtime correctness.

Closure Statement

As of this snapshot, the project is ready to move into adapter work (MCP/OpenAPI) from a quality, consistency, and observability baseline.