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 vasilyu1983/AI-Agents-public --skill ai-coding-agents-sessionsgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/ai-coding-agents-sessions)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-coding-agents-sessions"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-coding-agents-sessions/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/vasilyu1983/ai-agents-public/ai-coding-agents-sessions"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-coding-agents-sessions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 214 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium Excessive Agency · line 110 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 175 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00039 | $0.05609 |
| Opus 5 | $0.00019 | $0.02805 |
| Sonnet 5 | $0.00008 | $0.01122 |
| Haiku 4.5 | $0.00004 | $0.00561 |
Grade A, and why
ai-coding-agents-sessions 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 11d 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 — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Coding Agents Sessions
Use this skill to design or review the session lifecycle for a coding-agent runtime: session IDs, transcript persistence, resume pickers, stale-cache clearing, cross-worktree recovery, and what state must be restored versus recomputed.
This skill owns runtime session lifecycle. For persistent repo instructions and always-loaded memory, use ../agents-memory/SKILL.md.
ASCII Flow
new or resumed invocation
|
v
session identity
project + worktree + session_id + title + runtime mode
|
v
state restoration
transcript + summaries + tool state + task state + provider cache metadata
|
v
branch decision
continue | rewind | compact | clear | fork subagent | exact resume | claude agents (background resume)
|
v
active turn
append events, persist checkpoints, keep recovery path auditable
Quick Reference
| Question | Read | Outcome |
|---|---|---|
| What belongs in a coding-agent session model? | references/session-lifecycle-and-resume.md |
Session IDs, picker flows, stale-cache reset, and resume semantics |
| How should transcripts recover across worktrees and summaries? | references/transcript-restoration-and-cross-worktree-recovery.md |
Restoration boundaries, search, cross-project safeguards, and replay rules |
| How do users decide between continue / rewind / clear / compact / subagent at each turn? | references/context-lifecycle-and-branching.md |
1M-context branching model, context rot zone, rewind > correction, compact-vs-clear, bad-compact causes, subagent mental test |
| How do forked subagents change session lifecycle (cache, isolation, resume, cost)? | references/context-forking.md |
Blank-vs-forked startup, CLAUDE_CODE_FORK_SUBAGENT=1 and /fork surfaces, cache-prefix economics, fork-as-exception rule |
| Which resume path should I use (session ID / picker / ACP re-attach / recipe re-seed)? | references/resume-path-decision-tree.md |
Decision tree, path comparison table, prompt-cache economics in subagent spawning |
| How does OpenAI Codex split Session / Task / Turn protocol state? | references/openai-codex-session-task-turn-protocol.md |
SQ/EQ protocol, response bookmarks, one-active-task invariant, and interruption rules |
| How does Codex persist, resume, fork, and cloud-resume sessions? | references/openai-codex-session-persistence.md |
SQLite-backed session index, codex resume UUID lookup, codex fork branch semantics, codex cloud task-apply |
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 301 B
- data/sources.json 6.7 KB
- learnings.consolidated.md 601 B
- learnings.md 735 B
- references/context-forking.md 7.4 KB
- references/context-lifecycle-and-branching.md 9.9 KB
- references/openai-codex-session-persistence.md 6.3 KB
- references/openai-codex-session-task-turn-protocol.md 2.8 KB
- references/resume-path-decision-tree.md 8.2 KB
- references/session-lifecycle-and-resume.md 4.5 KB
- references/transcript-restoration-and-cross-worktree-recovery.md 3.1 KB
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.
- 11d ago First seen · 312 lines · 39 tokens per session scan A eaa9817d6b95
ai-coding-agents-sessions is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 8d ago), licensed MIT. It adds 39 tokens to every session and 5,609 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.
Other skills, from other repositories
evolve
Use this skill when extracting session patterns into reusable learnings. Three modes: analyze (extract from session history), review (edit/manage existing learnings), list (display active learnings). Manages .orchestrator/metrics/learnings.jsonl.
memory-cleanup
Use this skill when performing manual memory consolidation (Dream-equivalent). Reviews, consolidates, and prunes memory files under /.claude/projects//memory/. Run after major refactors, every 5+ sessions, or when memory quality degrades (broken links, stale references, contradictions, MEMORY.md > 200 lines). Invoke…
session-handoff
Context transfer between AI sessions. Trigger when the user wants to save the context, resume a task, or hand off the work to another session.
memory-types-team-stores
Use when configuring shared team memory stores and knowledge graphs.
memory-types
Use when managing persistent project memory, facts, and developer context.
fable-handoff
Compact session decisions, durable evidence, open blockers, and exact next actions into structured continuation state for cross-session resumption. Use when pausing a coding session, transferring context to another agent, summarizing long-running work, or creating durable continuation checkpoints — even if the user…