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 strikersam/autonomous-ai-agency --skill session-planninggit clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote 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/strikersam/autonomous-ai-agency/session-planning)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/session-planning"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/session-planning/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/strikersam/autonomous-ai-agency/session-planning"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/session-planning.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00018 | $0.01434 |
| Opus 5 | $0.00009 | $0.00717 |
| Sonnet 5 | $0.00004 | $0.00287 |
| Haiku 4.5 | $0.00002 | $0.00143 |
Grade C, and why
session-planning scanned grade C with 2 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 8d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
cat .claude/state/active-tasks.md # what's already in flight Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
ls .claude/skills/ # see all available skills How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: session-planning — Mandatory Planning Workflow for All AI Agents
When to use: At the start of EVERY non-trivial agentic session, before writing any code. This skill is universal — it works with Claude Code, Codex, Cursor, Aider, and any other AI tool that reads AGENTS.md or CLAUDE.md.
The 10-Step Workflow
Run these steps in order. Do not skip steps 6 (draft PR) or 7 (user confirmation).
Step 1 — Orient (free)
cat AGENTS.md # cross-tool ground truth
cat CLAUDE.md # project-specific rules
cat graphify-out/GRAPH_REPORT.md # codebase map (no token cost)
cat .claude/state/active-tasks.md # what's already in flight
cat docs/changelog.md | head -40 # what recently changed
Step 2 — Understand the Task
- If from a GitHub issue: read the issue body, comments, linked PRs
- If from a user message: restate the task in one sentence to confirm understanding
- If resuming: read
.claude/state/NEXT_ACTION.mdandactive-tasks.md
Step 3 — Load Relevant Skills
ls .claude/skills/ # see all available skills
# Load skills matching the task type:
# Multi-file change → implementation-planner
# Tests needed → test-first-executor
# Auth/key/agent-tools → risky-module-review
# Pre-merge review → council-review
# Adding deps → dependency-audit
# Codebase exploration → graphify
# Cross-harness routing → ecc-harness-patterns
Step 4 — Research (if novel task)
- If the task involves techniques not in the codebase, search OSS reference projects
- Use web search or GitHub MCP to read relevant repos
- Cite sources in the plan
Step 5 — Write the Plan
Use the implementation-planner skill or write directly:
## Plan: <task name>
**Goal:** one sentence
**Files to change:**
- `path/to/file.py` — what changes and why
- `path/to/other.py` — what changes and why
**Steps:**
1. Write tests first (test-first-executor)
2. Implement step A
3. Implement step B
4. Run pytest -x
5. Update changelog
6. Update active-tasks.md
**Risks:** list risky modules requiring risky-module-review
**Acceptance criteria:** how to verify it works
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.
- 8d ago First seen · 172 lines · 18 tokens per session scan C 7f8685756f3c
session-planning is a skill published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 1,434 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 2 findings (reads agent configuration directories, enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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babysit-babysitter-issues
This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.
cog-meeting-processing
Process meeting recordings and transcripts into decisions, action items, and team dynamics.
cog-team-intelligence
Cross-reference GitHub, Linear, Slack, and PostHog with bidirectional sync for team briefs.
spec-driven-development
Specification creation and management for the Pilot Shell methodology. Covers semantic search, clarifying questions, structured spec generation, and iterative refinement.
cog-onboarding
Personalize COG Second Brain workflow through role pack selection and vault initialization.