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 itallstartedwithaidea/agent-skills --skill session-archaeologygit clone --depth 1 https://github.com/itallstartedwithaidea/agent-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/itallstartedwithaidea/agent-skills/session-archaeology)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/session-archaeology"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/session-archaeology/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/itallstartedwithaidea/agent-skills/session-archaeology"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/session-archaeology.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 | $0.00037 | $0.02253 |
| Opus 5 | $0.00018 | $0.01126 |
| Sonnet 5 | $0.00007 | $0.00451 |
| Haiku 4.5 | $0.00004 | $0.00225 |
Grade A, and why
session-archaeology 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Archaeology
Part of Agent Skills™ by googleadsagent.ai™
Description
Session Archaeology is the systematic excavation and analysis of past agent sessions to extract reusable patterns, identify recurring failure modes, and derive new skills from operational history. Every agent session produces a rich artifact — a complete transcript of reasoning, tool usage, errors, corrections, and final outcomes. Most teams discard this data. Session Archaeology treats it as the most valuable training signal available: real-world execution traces from your specific domain, your specific codebase, and your specific workflows.
This skill formalizes the methodology used at googleadsagent.ai™ to continuously improve Buddy™ by mining thousands of past Google Ads analysis sessions. The archaeology process identifies which prompt patterns led to accurate recommendations, which tool call sequences completed reliably, and which error recovery strategies succeeded. These findings are then codified into new Agent Skills™ or refinements to existing ones, creating a flywheel of continuous improvement.
The process operates at three levels: individual session review (what went wrong in this specific run), cross-session pattern analysis (what patterns recur across many runs), and trend identification (how is agent behavior evolving over time). Each level yields different insights and different types of improvements.
Use When
- Agent quality has plateaued and you need new optimization signals
- You want to derive new skills or rules from real execution data
- Recurring failures suggest systematic issues rather than random errors
- Onboarding new team members who need to understand agent behavior patterns
- Building regression test suites from real session transcripts
- Preparing for model version migrations (comparing behavior across model versions)
How 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.
- 9d ago First seen · 212 lines · 37 tokens per session scan A 4c0153026653
session-archaeology is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (37 stars, last pushed 4mo ago), licensed MIT. It adds 37 tokens to every session and 2,253 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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