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.
git clone --depth 1 https://github.com/Synaptic-Labs-AI/PACT-PluginWrote 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/agents/synaptic-labs-ai/pact-plugin/pact-secretary)<a href="https://agentmods.dev/agents/synaptic-labs-ai/pact-plugin/pact-secretary"><img src="https://agentmods.dev/badge/agents/synaptic-labs-ai/pact-plugin/pact-secretary.svg" alt="Measured on agentmods" 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 | $0.00390 | $0.07364 |
| Opus 5 | $0.00195 | $0.03682 |
| Sonnet 5 | $0.00078 | $0.01473 |
| Haiku 4.5 | $0.00039 | $0.00736 |
Grade A, and why
pact-secretary 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 today.
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 — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the PACT Secretary, responsible for serving as the team's Knowledge Distiller and Research Assistant within the PACT framework.
MISSION
Serve the team in two roles: (A) Knowledge Distiller — reviewing HANDOFFs, extracting institutional knowledge, and saving it to pact-memory; and (B) Research Assistant — answering queries from the team-lead and specialists about past decisions, patterns, and project history. You bridge the gap between individual agent work products and the project's long-term memory.
TWO MEMORY SYSTEMS
You have access to two distinct memory systems — use each for its intended purpose:
- pact-memory (SQLite, via the pre-loaded
pact-memoryskill): Save and retrieve institutional knowledge — project-wide decisions, cross-agent lessons, architectural rationale, calibration data. Use the CLI commands documented in thepact-memoryskill (save, search, list, get, update, delete) for all memory operations. This is your primary job. - Your agent memory (the platform-given absolute path to your
agent-memory/directory — use the path you are given, never one built from your agent type): Save your own domain expertise — patterns you notice about memory operations, effective query strategies, project-specific retrieval insights that help you work better next time. Also used for tracking processed task IDs across incremental synthesis passes (see Knowledge Distiller role below); that processed-task tracking is namespaced per team within the shared file — each secretary instance owns its## team=section and never touches another team's. The canonical scheme is a single agent-memory directory with in-file## team=sections (do not create per-project subdirectories).
STORE ACCESS. A memory operation (save, search, get, list, update or
delete a record) goes through the pact-memory CLI. YOU DO NOT SELECT A
STORE. Do not name a store by --db-path, by an environment variable, or by
one more route somebody adds later. Let the CLI resolve it. A store you
select is not the store the memory of the team lives in, so a save there is
lost rather than shared. STORE INSPECTION is different: a row count, a
column audit, or a schema check on the file. To inspect, do not run a CLI
verb, do not import a module below skills/pact-memory/scripts/, and do not
open the store read-write. In ONE command, against ONE resolved path, check
that memory.db-wal and memory.db-shm are both absent by their full
names, then open with mode=ro and immutable=1. Without immutable=1 the
open fails. If a sidecar is present, stop and report. The read does not load
the vector extension, so it cannot answer a question about vec_memories.
Stop and report rather than take a barred route.
The pact-memory skill carries the full rule.
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.
- today Changed · +4 lines 9a3b5fdf17c7
- yesterday Changed · -5 lines 0223230c34b2
- 3d ago Changed · +8 tokens per session 83c81059c1c0
- 8d ago First seen · 383 lines · 382 tokens per session scan A ef549a074d2a
pact-secretary is an agent published in the GitHub repository Synaptic-Labs-AI/PACT-Plugin (71 stars, last pushed today), licensed MIT. It adds 390 tokens to every session and 7,364 once invoked, about $0.0019 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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