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-graph-maintainergit 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-graph-maintainer)<a href="https://agentmods.dev/agents/intense-visions/harness-engineering/harness-graph-maintainer"><img src="https://agentmods.dev/badge/agents/intense-visions/harness-engineering/harness-graph-maintainer.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 | $0.00023 | $0.06652 |
| Opus 5 | $0.00012 | $0.03326 |
| Sonnet 5 | $0.00005 | $0.01330 |
| Haiku 4.5 | $0.00002 | $0.00665 |
Grade A, and why
harness-graph-maintainer 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 5d 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 — 596 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
Re-scan codebase, sync external connectors, detect graph anomalies, maintain graph freshness
Skills
- harness-dependency-health
- harness-knowledge-mapper
- validate-context-engineering
Steps
- Run
harness scan(always) - Run
harness ingest(always) - Run
harness graph status(always)
Methodology
Harness Dependency Health
Analyze structural health of the codebase and surface problems before they become incidents.
When to Use
- Weekly scheduled health check on the codebase
- Before major refactoring — understand current structural health
- When onboarding to a new project — assess codebase quality
- NOT for checking layer violations (use enforce-architecture)
- NOT for finding dead code (use cleanup-dead-code)
Prerequisites
A knowledge graph at .harness/graph/ 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: Check staleness — compare .harness/graph/metadata.json
scanTimestamp against git log -1 --format=%ct (latest commit timestamp).
If graph is more than 2 commits behind (git log --oneline <scanTimestamp>..HEAD | wc -l),
run harness scan to refresh before proceeding. (Staleness sensitivity: High)
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: METRICS — Compute Graph Structural Metrics
Query the graph for five key structural indicators:
-
Hub detection: Find nodes with high fan-in (>10 inbound
importsedges).query_graph(rootNodeIds=[all file nodes], includeEdges=["imports"])
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.
- 5d ago First seen · 596 lines · 23 tokens per session scan A 8bc8b45f1d09
harness-graph-maintainer is an agent published in the GitHub repository Intense-Visions/harness-engineering (20 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 6,652 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.
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