Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/intense-visions/harness-engineering/harness-codebase-health-analystgit clone --depth 1 https://github.com/Intense-Visions/harness-engineeringWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/intense-visions/harness-engineering/harness-codebase-health-analyst)<a href="https://agentmods.dev/agents/intense-visions/harness-engineering/harness-codebase-health-analyst"><img src="https://agentmods.dev/badge/agents/intense-visions/harness-engineering/harness-codebase-health-analyst.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.14191 |
| Opus 5 | $0.00011 | $0.07096 |
| Sonnet 5 | $0.00004 | $0.02838 |
| Haiku 4.5 | $0.00002 | $0.01419 |
Grade A, and why
harness-codebase-health-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 1,302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
Run health checks, detect hotspots, analyze impact, surface risks before they become incidents
Skills
- harness-hotspot-detector
- harness-dependency-health
- harness-impact-analysis
- cleanup-dead-code
- harness-perf
- harness-security-scan
Steps
- Run
harness graph status(always) - Run
harness check-deps(always) - Run
harness check-perf(always) - Run
harness check-security(always)
Methodology
Harness Hotspot Detector
Identify modules that represent structural risk via co-change and churn analysis.
When to Use
- Weekly scheduled analysis to track codebase risk
- Before major refactoring — find the riskiest areas
- When investigating why changes keep breaking unrelated features
- NOT for finding dead code (use cleanup-dead-code)
- NOT for checking architecture rules (use enforce-architecture)
Prerequisites
A knowledge graph at .harness/graph/ with git history enables full analysis. If no graph exists,
the skill uses static analysis fallbacks (see Graph Availability section).
Run harness scan to enable graph-enhanced analysis.
Graph Availability
Before starting, check if .harness/graph/graph.json exists.
If graph exists: Use graph tools as primary strategy. (Staleness sensitivity: Low — never auto-refresh. Git-based churn data in the graph remains useful even when slightly stale.)
If graph exists and is fresh (or refreshed): Use graph tools as primary strategy.
If no graph exists: Output "Running without graph (run harness scan to
enable full analysis)" and use fallback strategies for all subsequent steps.
Process
Phase 1: CO-CHANGE — Analyze Co-Change Patterns
Query the graph for co_changes_with edges (created by GitIngestor):
query_graph(rootNodeIds=[all file nodes], includeEdges=["co_changes_with"])
Identify file pairs that frequently change together:
- Co-located pairs (same directory): Normal — they share a concern.
- Distant pairs (different modules): Suspicious — may indicate hidden coupling.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 1,302 lines · 22 tokens per session scan A 15472c459948
harness-codebase-health-analyst is an agent published in the GitHub repository Intense-Visions/harness-engineering (20 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 14,191 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
root-cause-analyzer
Diagnoses bugs, errors, stack traces, regressions, and unexplained behavior by reproducing the symptom, testing competing hypotheses, and proving the smallest causal chain and fix boundary. Advisory only — does not modify files, commit, or publish findings.
integration-reviewer
Runtime integration validator — read-only. Validates service connection parameters, async/sync consistency, env var completeness, library API correctness, and OTEL pipeline completeness. Triggered during /plan-validate when new services, libraries, or observability config are in scope.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
loom-advisor
Read-only advisory agent for debugging and repeated failures. Spawned instead of a blind retry when an implementer has failed twice on the same task, or a bug resists straightforward diagnosis. Returns a root-cause diagnosis plus one concrete next step.
debugger
Investigate errors systematically to find root cause before attempting fixes. Gathers evidence, analyzes patterns, and forms testable hypotheses.
SKILL_AUTOMATIC_REMEDIATION
Version: 1.0.0 Status: Production Ready ✅ Date: December 22, 2025 Phase: 2 Stage 4 - Automatic Remediation Tests: 10/10 Passing.