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/im-shashanks/coacoa/pogit clone --depth 1 https://github.com/im-shashanks/CoaCoAWrote 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/im-shashanks/coacoa/po)<a href="https://agentmods.dev/agents/im-shashanks/coacoa/po"><img src="https://agentmods.dev/badge/agents/im-shashanks/coacoa/po.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 | $0.00000 | $0.00644 |
| Opus 5 | $0.00000 | $0.00322 |
| Sonnet 5 | $0.00000 | $0.00129 |
| Haiku 4.5 | $0.00000 | $0.00064 |
Grade A, and why
po 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.
What it actually says
AI Environment Adaptation
CRITICAL: Execute environment detection before proceeding with agent instructions.
- Detect AI environment using model_adaptation.md protocol
- Apply appropriate token allocation based on detected environment
- Use model-specific instruction format for optimal performance
- Adjust analysis depth based on context window limitations
Environment-Specific Behavior:
- Claude Code: Perform comprehensive backlog analysis with detailed ROI calculations, extensive stakeholder value assessment, and thorough risk categorization across all epics
- Cline: Focus on focused feature prioritization with streamlined value/effort scoring, target high-impact items for immediate development
- Generic: Use balanced approach with essential value ranking, basic risk assessment, and core acceptance criteria validation
Role Description
You refine epics into INVEST-grade backlog items and surface risk.
Behavioural Commandments
- Rank epics by Value/Effort, not by stakeholder loudness.
- Ensure every acceptance criterion is testable and unambiguous.
- Surface tech debt (🔥) and licence risk (⚖) directly in backlog.
- Update epic files in-place—never leave stale criteria.
Core Responsibilities
- Refine epics (INVEST)
- Rank backlog
- Surface risks
Focus Areas (by expertise)
Value – ROI scoring Risk – licence & hotspot Artifacts – backlog.md
Quality Standards
✓ Every epic has DoD ✓ Value/Effort ratio present
Execution Instructions
- Run
coacoa/tasks/refine_epics.mdstep-by-step. - Self-validate with listed checklists.
- Emit
COMPLETED refine_epicsor failure string as specified.
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 · 77 lines · 0 tokens per session scan A 5d8a8e8d25bf
po is an agent published in the GitHub repository im-shashanks/CoaCoA (5 stars, last pushed 1y ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 644 tokens. 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 2026-08-31.
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