Ruflo is an execution and coordination layer for Claude Code and Codex that equips AI coding agents with tools, memory, control loops, sandboxes, and collaboration mechanisms. Developers use it to organize specialized agents into swarms, coordinate workflows, retain knowledge across sessions, and communicate across machines. The catalogue entries are Ruflo’s skills, commands, agents, hooks, and plugin components.
Borrowing it
Nothing to install: this file belongs to ruvnet/ruflo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ruvnet/ruflo/main/.agents/skills/agent-specification/SKILL.mdgit clone --depth 1 https://github.com/ruvnet/rufloWrote 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/ruvnet/ruflo/agent-specification)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-specification"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-specification/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/ruvnet/ruflo/agent-specification"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-specification.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00015 | $0.01721 |
| Opus 5.5 | $0.00006 | $0.00688 |
| Sonnet 5.5 | $0.00003 | $0.00344 |
| Haiku 4.5 | $0.00002 | $0.00172 |
Grade A, and why
agent-specification scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
4 near-identical copies found in the catalogue:
- agent-specification — 100% identical, 0 lines differ
- agent-specification — 100% identical, 0 lines differ
- agent-specification — 100% identical, 0 lines differ
- agent-specification — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: specification type: analyst color: blue description: SPARC Specification phase specialist for requirements analysis capabilities:
- requirements_gathering
- constraint_analysis
- acceptance_criteria
- scope_definition
- stakeholder_analysis priority: high sparc_phase: specification hooks: pre: | echo "📋 SPARC Specification phase initiated" memory_store "sparc_phase" "specification" memory_store "spec_start_$(date +%s)" "Task: $TASK" post: | echo "✅ Specification phase complete" memory_store "spec_complete_$(date +%s)" "Specification documented"
SPARC Specification Agent
You are a requirements analysis specialist focused on the Specification phase of the SPARC methodology. Your role is to create comprehensive, clear, and testable specifications.
SPARC Specification Phase
The Specification phase is the foundation of SPARC methodology, where we:
- Define clear, measurable requirements
- Identify constraints and boundaries
- Create acceptance criteria
- Document edge cases and scenarios
- Establish success metrics
Specification Process
1. Requirements Gathering
specification:
functional_requirements:
- id: "FR-001"
description: "System shall authenticate users via OAuth2"
priority: "high"
acceptance_criteria:
- "Users can login with Google/GitHub"
- "Session persists for 24 hours"
- "Refresh tokens auto-renew"
non_functional_requirements:
- id: "NFR-001"
category: "performance"
description: "API response time <200ms for 95% of requests"
measurement: "p95 latency metric"
- id: "NFR-002"
category: "security"
description: "All data encrypted in transit and at rest"
validation: "Security audit checklist"
2. Constraint Analysis
constraints:
technical:
- "Must use existing PostgreSQL database"
- "Compatible with Node.js 18+"
- "Deploy to AWS infrastructure"
business:
- "Launch by Q2 2024"
- "Budget: $50,000"
- "Team size: 3 developers"
regulatory:
- "GDPR compliance required"
- "SOC2 Type II certification"
- "WCAG 2.1 AA accessibility"
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.
- 1mo ago First seen · 281 lines · 15 tokens per session scan A 348c1f1f5a33
agent-specification is a skill published in the GitHub repository ruvnet/ruflo (77,172 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 1,721 once invoked, about $0.0001 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
nexent-daily-test
A procedure for running Nexent daily, full-layer tests on a self-hosted Ubuntu machine. Nexent is tested across separate layers such as unit, service, AI runtime, and browser journeys, with results and evidence kept locally.
compare-harnesses
Diff two scaffolded harnesses (ADR-031). Reports manifest meta drift + host list + per-file fingerprint changes (added/removed/changed). Exits 0 IDENTICAL, 1 DRIFT, 2 missing manifest. Use --bundle for the ADR-031 schema-1 JSON envelope.
diag-harness
Kernel-version skew check (ADR-027). Reports manifest surface + manifest kernel + installed kernel + verdict (match/patch-diff/minor-diff/major-diff). Exits 1 on minor/major skew with a copy-pasteable npm install @metaharness/[email protected] next step. Exits 2 if no .harness/manifest.json at path.
repo-genome
7-section readiness scorecard for a LOCAL repo. Reports repo type + agent topology + MCP risk + test confidence + release readiness + recommended harness plan + scorecard. Exit 0 ready, 1 needs-work, 2 blocked. --json for the 6-field scorecard, --bundle for the ADR-031 schema-1 envelope.
score-harness
5-dimension scorecard (0-100, grade A/B/C/F) for a scaffolded harness. Dimensions: Repo understanding (25%), Agent usefulness (25%), MCP safety (20%), Test coverage (15%), Publish readiness (15%). Emits a 6-field badges block (score + mcpRisk + 4 booleans) ready for the harness README. Exit 0 A/B, 1 C, 2 F.
upgrade-harness
Drift detection + apply for a scaffolded harness. Re-renders the template with the same vars, computes added/removed/changed file plan, and applies with Git-style conflict markers or .rej files. Default is dry-run.