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 skills/rand/cc-polymath/discover-agenticnpx skills add rand/cc-polymath --skill discover-agenticgit clone --depth 1 https://github.com/rand/cc-polymathWhat 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 | $0.00044 | $0.01219 |
| Opus 5 | $0.00022 | $0.00609 |
| Sonnet 5 | $0.00009 | $0.00244 |
| Haiku 4.5 | $0.00004 | $0.00122 |
Grade A, and why
discover-agentic 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 3d 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Workflow Skills Discovery
Provides automatic access to skills for building autonomous AI agents, managing tool use, and orchestrating multi-step workflows.
When This Skill Activates
This skill auto-activates when you're working with:
- AI agent development and autonomous workflows
- Task decomposition for LLM agents
- Function calling and tool use patterns
- Context window management and summarization
- Working memory, scratchpads, and state tracking
- Long-term memory and persistence patterns
- Multi-step reasoning and planning
- Failure recovery and re-planning strategies
- Parallel vs sequential execution decisions
Available Skills
Quick Reference
The Agentic category contains 3 specialized skills:
- agentic-task-decomposition - Breaking complex goals into agent-executable steps, dependency graphs, re-planning
- agentic-tool-use - Function calling patterns, parallel tool use, error handling, tool selection heuristics
- agentic-memory - Context window management, working memory, long-term persistence, summarization
Load Full Category Details
For complete descriptions and workflows:
Read ../agentic/INDEX.md
This loads the full Agentic category index with:
- Detailed skill descriptions
- Usage triggers for each skill
- Common workflow combinations
- Cross-references to related skills
Load Specific Skills
Load individual skills as needed:
Task planning and decomposition
Read ../agentic/agentic-task-decomposition.md
Tool calling and execution
Read ../agentic/agentic-tool-use.md
Context and memory management
Read ../agentic/agentic-memory.md
Common Workflows
Building an Agent from Scratch
Sequence: Decomposition -> Tool Use -> Memory
Read ../agentic/agentic-task-decomposition.md # Plan the agent's task handling Read ../agentic/agentic-tool-use.md # Implement tool execution Read ../agentic/agentic-memory.md # Add context management
Adding Tool Use to an Agent
Sequence: Tool Use -> Decomposition
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.
- 3d ago First seen · 181 lines · 44 tokens per session scan A 7461d6f48076
discover-agentic is a skill published in the GitHub repository rand/cc-polymath (136 stars, last pushed 6mo ago), licensed MIT. It adds 44 tokens to every session and 1,219 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 2026-08-30.
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