chore: public OSS release 2.9.1

CHANGELOG.md

@@ -1,3 +1,13 @@
+### 2.9.1 (Monday, August 10, 2026)
+### Features/Bug Fixes
+* fix(llm): add bounded connection retries
+---
+### 2.9.0 (Monday, August 10, 2026)
+### Features/Bug Fixes
+* fix(e2): allow targeted environment credential reads
+* fix: remediate nSpect High vulnerabilities
+* fix(yara): require local destructive autonomy evidence
+---
 ### 2.8.2 (Friday, August 07, 2026)
 ### Features/Bug Fixes
 * fix(mcp): retry malformed TP4 responses

README.md

@@ -377,7 +377,7 @@ SkillSpector detects **68 vulnerability patterns** across 17 categories:
 | ID | Pattern | Severity | Description |
 |----|---------|----------|-------------|
 | E1 | External Transmission | MEDIUM | Sending data to external URLs |
-| E2 | Env Variable Harvesting | HIGH | Collecting API keys and secrets |
+| E2 | Env Variable Harvesting | HIGH | Enumerating, copying, or searching environment data to collect secrets |
 | E3 | File System Enumeration | MEDIUM | Scanning directories for sensitive files |
 | E4 | Context Leakage | HIGH | Transmitting conversation context externally |
 
@@ -652,8 +652,8 @@ The top-level shape is (this example shows a full LLM-backed scan; with `--no-ll
       {
         "node": "semantic_security_discovery",
         "request_kind": "structured_output",
-        "provider": "anthropic",
-        "model": "claude-opus-4-6",
+        "provider": "nv_inference",
+        "model": "azure/anthropic/claude-opus-4-6",
         "model_source": "provider_response",
         "usage_source": "provider_response",
         "prompt_tokens": 1000,

docs/INFERENCE_USAGE.md

@@ -27,8 +27,8 @@ Each successfully observed provider response contributes one entry to
       {
         "node": "semantic_security_discovery",
         "request_kind": "structured_output",
-        "provider": "anthropic",
-        "model": "claude-opus-4-6",
+        "provider": "nv_inference",
+        "model": "azure/anthropic/claude-opus-4-6",
         "model_source": "provider_response",
         "usage_source": "provider_response",
         "prompt_tokens": 1000,

docs/release/skillspector-2.9.0.md

@@ -0,0 +1,51 @@
+# SkillSpector v2.9.0
+
+Released: 2026-08-10
+
+## Summary
+
+This release includes 3 public-facing change(s) since release/2.8.2.
+
+## Highlights
+
+- fix(e2): allow targeted environment credential reads
+- fix: remediate nSpect High vulnerabilities
+- fix(yara): require local destructive autonomy evidence
+
+## Added
+
+- None.
+
+## Changed
+
+- None.
+
+## Fixed
+
+- fix(e2): allow targeted environment credential reads
+- fix: remediate nSpect High vulnerabilities
+- fix(yara): require local destructive autonomy evidence
+
+## Security
+
+- None.
+
+## Breaking Changes and Migration
+
+- None.
+
+## Deprecations
+
+- None.
+
+## Validation
+
+- Auto-generated from public-safe commit subjects since release/2.8.2; no additional validation commands were recorded by the release driver.
+
+## Known Limitations
+
+- None.
+
+## References
+
+- `CHANGELOG.md`

docs/release/skillspector-2.9.1.md

@@ -0,0 +1,49 @@
+# SkillSpector v2.9.1
+
+Released: 2026-08-10
+
+## Summary
+
+This patch release improves resilience to transient LLM-provider connection failures during analysis. It uses bounded retries and records clearer batch-failure reasons when retries cannot recover.
+
+## Highlights
+
+- Adds bounded retries for transient LLM provider connection failures while preserving per-batch failure reporting.
+
+## Added
+
+- The inspection ledger distinguishes malformed structured LLM responses from exhausted connection retries.
+
+## Changed
+
+- Supported OpenAI and Anthropic clients use a common bounded native retry budget, while other providers receive the same bounded fallback retry schedule when applicable.
+
+## Fixed
+
+- Transient LLM connection failures no longer terminate a batch before the configured retry budget is exhausted.
+
+## Security
+
+- None.
+
+## Breaking Changes and Migration
+
+- None.
+
+## Deprecations
+
+- None.
+
+## Validation
+
+- `uv run --locked --extra dev pytest tests/nodes/test_llm_analyzer_base.py tests/nodes/test_meta_analyzer.py` — passed.
+- `GitLab main pipeline 61992428` — passed.
+- `git diff --check release/2.9.0..68c7a026d4b2d574b63019ceacd8fe8d7caa35db` — passed.
+
+## Known Limitations
+
+- Retries are limited to transient LLM provider connection errors; other provider errors fail immediately.
+
+## References
+
+- `CHANGELOG.md`

package.json

@@ -8,8 +8,8 @@
     "extensions": ["./extensions/skillspector.ts"]
   },
   "peerDependencies": {
-    "@earendil-works/pi-ai": "*",
-    "@earendil-works/pi-coding-agent": "*",
+    "@earendil-works/pi-ai": ">=0.78.1",
+    "@earendil-works/pi-coding-agent": ">=0.78.1",
     "typebox": "*"
   }
 }

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "skillspector"
-version = "2.8.2"
+version = "2.9.1"
 description = "SkillSpector: Security scanner for AI agent skills (Claude Code, Cursor, and similar). Scans skills for vulnerabilities, malicious patterns, and security risks before installation. Supports Git repos, URLs, zips, and local directories; runs static pattern checks and optional LLM semantic analysis; outputs terminal, JSON, and Markdown reports with risk scoring."
 readme = "README.md"
 license = "Apache-2.0"
@@ -51,7 +51,7 @@ dependencies = [
 
 [project.optional-dependencies]
 mcp = [
-    "mcp>=1.2.0",
+    "mcp>=1.29.0,<2.0.0",
 ]
 dev = [
     "skillspector[mcp]",

src/skillspector/inspection_ledger.py

@@ -48,6 +48,8 @@ class LedgerReason(StrEnum):
     EVAL_DATASET = "eval_dataset"
     SYNTAX_ERROR = "syntax_error"
     LLM_BATCH_FAILED = "llm_batch_failed"
+    LLM_STRUCTURED_RESPONSE_INVALID = "llm_structured_response_invalid"
+    LLM_CONNECTION_RETRIES_EXHAUSTED = "llm_connection_retries_exhausted"
     ANALYZER_RUNTIME_ERROR = "analyzer_runtime_error"
     UNACCOUNTED_WORK = "unaccounted_work"
     FINDING_ACCOUNTING_ERROR = "finding_accounting_error"
@@ -75,6 +77,10 @@ class LedgerReason(StrEnum):
     ),
     LedgerReason.SYNTAX_ERROR: "Python source could not be parsed.",
     LedgerReason.LLM_BATCH_FAILED: "LLM analysis failed for this file range.",
+    LedgerReason.LLM_STRUCTURED_RESPONSE_INVALID: (
+        "LLM returned a malformed structured response after retry."
+    ),
+    LedgerReason.LLM_CONNECTION_RETRIES_EXHAUSTED: ("LLM connection failed after bounded retries."),
     LedgerReason.ANALYZER_RUNTIME_ERROR: ("Analyzer failed after beginning applicable work."),
     LedgerReason.UNACCOUNTED_WORK: ("Planned inspection work has no unique terminal outcome."),
     LedgerReason.FINDING_ACCOUNTING_ERROR: (