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 ai-analyst-lab/ai-analyst --skill resume-pipelinegit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/ai-analyst-lab/ai-analyst/resume-pipeline)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/resume-pipeline"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/resume-pipeline/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/ai-analyst-lab/ai-analyst/resume-pipeline"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/resume-pipeline.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 | $0.00019 | $0.00303 |
| Opus 5 | $0.00010 | $0.00151 |
| Sonnet 5 | $0.00004 | $0.00061 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
resume-pipeline 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 2d 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.
What it actually says
Resume the requested analysis
- Identify the exact run. Inspect question, inputs, plan and status. If several match, ask which one. A latest link does not select the user's intended work.
- Version 3 state uses
helpers.pipeline.controller.Controller. Never overwrite or reinterpret legacy state as version 3. Offer a new run with explicitly selected, verified legacy artifacts while retaining the originals. - For blocked approval gates, show evidence and request approval. Record it only after receiving it, with checkpoint, actor and reason. Local records are not authenticated identities.
- For failures, inspect the cause before retrying. Confirm an interrupted worker is no longer running. Never remove a lock while another controller is active.
- Run
python -m helpers.pipeline.controller run EXACT_RUN_DIRECTORY, with--retry-failedonly for an intentional retry. Code validates definition and artifact hashes and skips valid completed work. Changed definitions/artifacts require a new reviewed run; do not silently reuse stale work. - Report the actual final status and outputs. Never manually change statuses or create a deck for a plan that did not request one.
Authentication, tool permissions and workflow approvals are separate requirements.
See docs/PIPELINE-CONTROLLER.md for recovery and migration details.
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.
- 2d ago First seen · 27 lines · 19 tokens per session scan A c00c1aaa6a3a
resume-pipeline is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (298 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 303 once invoked, about $0.0001 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 2026-09-12.
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