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 endorphin-ai/hasbrains-agent-kit --skill pipeline-stategit clone --depth 1 https://github.com/endorphin-ai/hasbrains-agent-kitWrote 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/endorphin-ai/hasbrains-agent-kit/pipeline-state)<a href="https://agentmods.dev/skills/endorphin-ai/hasbrains-agent-kit/pipeline-state"><img src="https://agentmods.dev/badge/skills/endorphin-ai/hasbrains-agent-kit/pipeline-state/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/endorphin-ai/hasbrains-agent-kit/pipeline-state"><img src="https://agentmods.dev/badge/skills/endorphin-ai/hasbrains-agent-kit/pipeline-state.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 | $0.00000 | $0.01798 |
| Opus 5.5 | $0.00000 | $0.00719 |
| Sonnet 5.5 | $0.00000 | $0.00360 |
| Haiku 4.5 | $0.00000 | $0.00180 |
Grade A, and why
pipeline-state 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 1mo 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pipeline-state — the run-wide handoff bus
Durable, file-backed state for the ENTIRE pipeline. One JSON file persists the run's variables +
per-phase outputs + .ai_log/ evidence-path references across phases, so a downstream agent (or the
orchestrator) recovers exactly what it needs by LOOKUP instead of receiving a large inlined dump — and
nothing is lost to context compression on long runs.
The standing pattern: evidence -> .ai_log/; references + key fields ->
pipeline-state; handoffs = state lookups, not inlined dumps.
- the orchestrator INITIALIZES the state at session start (alongside creating the session folder),
passes its path (
state_file) to every dispatched agent, and after each phase READS that phase's output fields +.ai_log/evidence paths FROM the state to validate the gate and to build the NEXT phase's prompt — passing state keys + paths, never re-dumping large content. - every agent, on START, READS the state to pull the inputs this phase needs (key fields +
.ai_log/evidence paths) — it does NOT expect large inlined content; on COMPLETION, WRITES its outputs — key result fields + PATHS to evidence offloaded in.ai_log/(never the blobs). - It COEXISTS with the docs/-native reports (the human-readable record) and the
.ai_log/path-handoff. The state REFERENCES the docs/ +.ai_log/artifacts; it does not replace them.
State file (PER-SESSION, .ai_log/, git-ignored)
.ai_log/session-<session-id>-<name>.json
ONE file per run, living under .ai_log/ — e.g. .ai_log/session-2026-06-30-member-blog-gating.json.
Naming convention. <session-id> is the run's disambiguating id — commonly the session date
<YYYY-MM-DD> (it separates repeat/concurrent runs); <name> is the epic slug. Together
<session-id>-<name> is the session LABEL <YYYY-MM-DD>-<epic-slug> (the same slug as session_dir).
The script DERIVES the path from that label (session-<label>.json).
The orchestrator derives + creates it at init and passes its exact PATH as state_file to every
dispatched agent. Agents read/write the path they were given — they do NOT reconstruct or hardcode a
constant path, because it varies per session. Use the one convention consistently across the skill, the
orchestrating command, and any workflow definition.
What ships with it
3 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.
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.
- 1mo ago First seen · 127 lines · 0 tokens per session scan A 8beea16076c5
pipeline-state is a skill published in the GitHub repository endorphin-ai/hasbrains-agent-kit (9 stars, last pushed 12d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,798 tokens. 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.
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