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 skills add StarryCod/cogitum --skill plangit clone --depth 1 https://github.com/StarryCod/cogitumWrote 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/starrycod/cogitum/plan)<a href="https://agentmods.dev/skills/starrycod/cogitum/plan"><img src="https://agentmods.dev/badge/skills/starrycod/cogitum/plan.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 5.1 | $0.00019 | $0.00457 |
| Opus 5 | $0.00010 | $0.00229 |
| Sonnet 5 | $0.00004 | $0.00091 |
| Haiku 4.5 | $0.00002 | $0.00046 |
Grade A, and why
plan 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.
This is a copy
81% identical to plan — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Plan Mode
Use this skill when the user wants a plan instead of execution.
Core behavior
For this turn, you are planning only.
- Do not implement code.
- Do not edit project files except the plan markdown file.
- Do not run mutating terminal commands, commit, push, or perform external actions.
- You may inspect the repo or other context with read-only commands/tools when needed.
- Your deliverable is a markdown plan saved inside the active workspace under
.cogitum/plans/.
Output requirements
Write a markdown plan that is concrete and actionable.
Include, when relevant:
- Goal
- Current context / assumptions
- Proposed approach
- Step-by-step plan
- Files likely to change
- Tests / validation
- Risks, tradeoffs, and open questions
If the task is code-related, include exact file paths, likely test targets, and verification steps.
Save location
Save the plan with write_file under:
.cogitum/plans/YYYY-MM-DD_HHMMSS-<slug>.md
Treat that as relative to the active working directory / backend workspace. Cogitum file tools are backend-aware, so using this relative path keeps the plan with the workspace on local, docker, ssh, modal, and daytona backends.
If the runtime provides a specific target path, use that exact path.
If not, create a sensible timestamped filename yourself under .cogitum/plans/.
Interaction style
- If the request is clear enough, write the plan directly.
- If no explicit instruction accompanies
/plan, infer the task from the current conversation context. - If it is genuinely underspecified, ask a brief clarifying question instead of guessing.
- After saving the plan, reply briefly with what you planned and the saved path.
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 · 59 lines · 19 tokens per session scan A 6d8663bf23a7
plan is a skill published in the GitHub repository StarryCod/cogitum (11 stars, last pushed 3mo ago), licensed MIT. It adds 19 tokens to every session and 457 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 81% identical to plan, differing in 14 lines, and is treated as a copy.
Other skills, from other repositories
pypi-release
This skill should be used when releasing tunacode-cli to PyPI. It keeps the existing local release checks, then hands the actual PyPI upload to a GitHub Actions workflow that uses the repository's PYPIAPITOKEN secret.
audit-harness
Use when auditing HARNESS.md, pre-commit hooks, pre-push hooks, architecture gates, or CI workflows for tunacode-cli. This skill treats any mismatch, skipped gate, or failing check as a critical failure and requires manual one-by-one execution rather than make targets, batch wrappers, or summary-only audits.
agent-builder
Build production-ready LLM agents with LangGraph, tool use, memory, streaming, and error handling. Use when designing or implementing an AI agent, multi-agent system, or agentic workflow.
html-artifacts
Author the HTML for a plan artifact, dashboard iframe, or Slack attachment — structure, design plan, available runtime, theming, and craft. Read this before writing HTML for saveplan, outputiframe, or slackattachhtml.
baby-sit
Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.