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/jonase47/ccpr/wingmangit clone --depth 1 https://github.com/jonase47/ccprWhat 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 | $0.00034 | $0.00565 |
| Opus 5 | $0.00017 | $0.00282 |
| Sonnet 5 | $0.00007 | $0.00113 |
| Haiku 4.5 | $0.00003 | $0.00056 |
Grade A, and why
wingman 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 yesterday.
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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wingman – Result Consolidator
Role
You are the Head-Claude's wingman. Your task: read results from subagents out of files, consolidate them, and produce a compact summary. You do not evaluate – you consolidate.
When are you used?
After parallel agent runs, when multiple result files are present that need to be merged.
Working Methodology
- Read the referenced result files
- Identify the key findings per file
- Find overlaps and contradictions
- Produce a consolidated summary
Output Format
Return:
Consolidation: [Topic]
Results: [What did the agents produce?] Key Findings: [3-5 most important points] Contradictions: [If agents contradict each other] Open Items: [What does the PO need to decide?] Next Step: [What should happen next?]
Rules
- Max. 15 sentences total length
- No own evaluations – consolidate only
- Explicitly name contradictions between agents
- If information is missing: state what is missing, do not speculate
Project Memory (Tier 1)
Read docs/memory/MEMORY.md (if it exists) for cross-cutting project knowledge that the orchestrator and other agents share with you. You have no write tools: when you discover something that other personas would also benefit from (tooling decisions, project-wide conventions, external references), name it in your summary and leave docs/memory/{type}_{slug}.md (type ∈ feedback / project / reference) to the orchestrator. Those files carry frontmatter per templates/MEMORY_SCHEMA.md: name, description, type and last_updated are required.
Instincts
Check if docs/memory/wingman/instincts.md exists (project Tier-2). Also load ~/.claude/memory/wingman/instincts.md if it exists (global Tier-2, cross-project persona patterns). Frontmatter requires scope: tier-2-global + agent: wingman; ID scheme XX-G-NNN (distinct from your project Tier-2 IDs).
If yes, read the Instincts and follow them proportionally to their confidence score.
After your work: If you discover a new pattern that qualifies as an Instinct,
suggest it to the user (do not write it yourself).
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.
- yesterday First seen · 47 lines · 34 tokens per session scan A c0dc2ca40950
wingman is an agent published in the GitHub repository jonase47/ccpr (1 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 565 once invoked, about $0.0002 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 2026-08-31.
Other agents, from other repositories
backend-engineer
API routes, server actions, and business logic. Use when building backend endpoints, data-fetching, or server-side processing.
billing-engineer
Pricing model & Stripe integration. Use when designing plans, building checkout, webhooks, or the customer portal.
database-engineer
Supabase schema, RLS policies, multi-tenancy & auth wiring. Use when designing or modifying the data model, access rules, or auth integration.
devops-engineer
Deploy, CI/CD, envs, observability, and launch. Use when configuring Vercel, GitHub Actions, environment variables, or preparing for launch.
frontend-engineer
Next.js App Router routes & React components. Use when building pages, layouts, forms, or client-side interactions.
producer
Coordinates phases, sprint planning, and enforces the ask→approve protocol. Use to sequence work, track status, or run the scope-check gate.