Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add rjmurillo/ai-agents/plugin install project-toolkitWrote 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/skills/rjmurillo/ai-agents/agent-harness-reference)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/agent-harness-reference"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/agent-harness-reference/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/agent-harness-reference"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/agent-harness-reference.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00122 | $0.04838 |
| Opus 5.5 | $0.00049 | $0.01935 |
| Sonnet 5.5 | $0.00024 | $0.00968 |
| Haiku 4.5 | $0.00012 | $0.00484 |
Grade A, and why
agent-harness-reference 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 — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Harness Reference
Use this reference before changing a hook, dispatcher, generator, hook config, or runtime test. Do not repeat web research unless the refresh rules below say the recorded contract is stale.
Triggers
harness contractcopilot cli hook behaviorhook payload formatcross-harness hook changerefresh hook evidence
Process
- Read
references/official-hook-contracts.mdfor the authoritative row. - Read
references/probe-evidence.mdonly for versioned runtime evidence. - Apply the repository policy in this file and the cited ADRs.
- If a refresh condition applies, update sources, probes, tests, mirrors, and memory in the same change.
- Mark an absent official answer
DOCS SILENT. Do not infer it from Claude.
Authority Order
references/official-hook-contracts.mdrecords current official sources, pinned source commits, exact event sets, output fields, and docs gaps.- This file states the repository decisions derived from those contracts.
references/probe-evidence.mdrecords versioned runtime observations.- ADR-068 and ADR-071 record the dispatcher and runtime verification choices.
- Serena memories are retrieval aids. They do not override the sources above.
If two sources disagree, use the official contract for authored behavior. Keep the conflicting probe as versioned evidence. Do not erase either side.
Repository Loading Surfaces
The repository keeps one canonical skill tree and one generated Copilot tree:
.claude/skills/agent-harness-reference/owns this contract.src/copilot-cli/skills/agent-harness-reference/is generated from it..github/skills/is not a repository shipping surface. Do not create it to mirror this skill.- In the
rjmurillo/ai-agentsrepository,AGENTS.md,src/claude/AGENTS.md,.github/AGENTS.md,.github/copilot-instructions.md,src/AGENTS.md, andtemplates/AGENTS.mdroute authoring work here. An installed plugin ships none of them and does not need to; they are maintainer routing, not a runtime dependency of this skill.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +7 tokens per session 21df514f3709
- 20d ago Changed · +4 lines cb8244315063
- 22d ago Changed · +6 lines 9364289a0630
- 24d ago Changed 3b3ca6390bf8
- 1mo ago First seen · 364 lines · 115 tokens per session scan A e4e7e7c70b07
agent-harness-reference is a skill published in the GitHub repository rjmurillo/ai-agents (48 stars, last pushed today), licensed MIT. It adds 122 tokens to every session and 4,838 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
install-gemini-code-review-action
Installs, configures, or upgrades Dazbo's PR code review & issue triage GitHub Action (gemini-review-action) in a target repository. Use when the user wants to set up, configure, or update automated AI code reviews, issue triaging, or Gemini model workflows for their GitHub repository.
review-deep
Review change vs spec, plan, code quality heuristics, and repo rules; append remediation phase if needed.
review-remote
Review open PR/MR and publish findings.
apply-review-feedback
Classify and apply accepted PR/MR review feedback.
azure-devops-automation
CI/CD pipelines, infrastructure as code, and deployment automation for Azure workloads.
architecture-audit
Comprehensive project consistency review across code, documentation, diagrams, and configuration.