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 RudraDudhat2509/claude-skills --skill session-docsgit clone --depth 1 https://github.com/RudraDudhat2509/claude-skillsWrote 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/rudradudhat2509/claude-skills/session-docs)<a href="https://agentmods.dev/skills/rudradudhat2509/claude-skills/session-docs"><img src="https://agentmods.dev/badge/skills/rudradudhat2509/claude-skills/session-docs/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/rudradudhat2509/claude-skills/session-docs"><img src="https://agentmods.dev/badge/skills/rudradudhat2509/claude-skills/session-docs.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 | $0.00038 | $0.00949 |
| Opus 5 | $0.00019 | $0.00475 |
| Sonnet 5 | $0.00008 | $0.00190 |
| Haiku 4.5 | $0.00004 | $0.00095 |
Grade A, and why
session-docs 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 9d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Documentation Skill
Run this at the end of every session. The goal is a memory system that is always accurate and never redundant — future sessions should be able to pick up exactly where this one left off without re-reading the conversation.
What this skill does
- Scans the session for what's worth saving
- Identifies which memory files need changes
- Writes immediately — no proposal, no approval gate
Step 1 — Scan the session
Go through the conversation and extract only things that fit one of these three categories:
Outdated status — something was PENDING and is now DONE, or was assumed working and broke, or a decision was reversed. Status that doesn't match reality is the most damaging type of stale memory.
Missing decisions — a non-obvious choice was made and the WHY isn't captured anywhere. If someone reads the code in 3 months, will they understand why this approach was taken? If not, it belongs in memory.
Stale file paths / function names — a file was renamed, moved, deleted, or a key function changed its name or signature. Memory that points to a ghost is worse than no memory at all.
Do NOT capture:
- Things that are obvious from reading the code or running
git log - In-progress work that isn't settled yet
- Error messages and debugging steps (the fix is in the code)
- Anything already in CLAUDE.md
- Temporary state from this session only
Graphify context (check every session)
All three altagic codebases have persistent knowledge graphs in graphify-out/ directories:
| Project | Path | Last known stats |
|---|---|---|
| Color Tool | Color_tool/graphify-out/ |
452 nodes, 710 edges, 43 communities |
| HR Dashboard | hr_dashboard/graphify-out/ |
609 nodes, 861 edges, 42 communities |
| Drapeme Pipeline | drapeme-pipeline/graphify-out/ |
216 nodes, 425 edges, 14 communities |
Each directory contains: graph.html (interactive vis.js), graph.json (GraphRAG-ready), GRAPH_REPORT.md.
If a session built or updated any of these graphs, record the new node/edge counts in memory. If the session made significant structural changes to a codebase (new modules, renamed files, deleted components), note that the graph is stale and should be rebuilt with /graphify <path> before the next deep codebase question.
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.
- 9d ago First seen · 78 lines · 0 tokens per session scan A 9d5a3d7e7b58
session-docs is a skill published in the GitHub repository RudraDudhat2509/claude-skills (2 stars, last pushed 17d ago), licensed MIT. It adds 38 tokens to every session and 949 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 skills, from other repositories
durable-session-state
Persist plans, scope decisions, evidence, and reviewer/critic verdicts to durable files during long or multi-phase tasks so work survives context compaction, session resumes, and handoffs. Use for swarm-mode tasks, before context grows large, when recording approval gates, and when resuming after compaction or a…
obsidian
Use the official Obsidian CLI (v1.12+) to manage vaults, notes, daily notes, search, tasks, tags, properties, links, templates, sync, publish, and workspaces. Use when the user mentions Obsidian, vaults, notes, or wants to automate note-taking and knowledge base workflows.
contextdb-autopilot
Automatic ContextDB session lifecycle — init, session events, checkpoints, and continuity artifacts. Use when you need AIOS context persistence without prompt injection. NOT for general task execution — only for ContextDB session management. TRIGGER: contextdb、session persist、checkpoint save.
memo
AIOS project memory — read/write/search your own memory using the memo capabilities. You (the model) decide what to remember, what to correct, and what was useful; the harness only executes your decision. Use when you want to recall prior work, persist a durable fact, supersede a wrong one, or mark a recalled memory…
memory-state
State variables the Sentinel must keep across ticks (last order sent, freeze flag, FATAL streak, per-state counters, emergency history, cooldowns). Use this skill on every tick to update memory and decide if a new order is required (edge-triggered).
aios-offload-recall
Use when recalling prior AIOS tool/browser outputs from offloaded refs; inspect Mermaid canvas first, then read only matching node-level evidence.