AIWorkHub AGENTS.md

Repository instructions for AIWorkHub, a system for managing coding-agent tasks and project context. They define where task information belongs, which interfaces agents should use, and when writes are allowed.

In plain words
What is it for?
Use them when working in the AIWorkHub repository, handling task records, reading project context, using its agent tools, or preparing worker instructions.
Why use it?
They reduce the risk of putting project data in the wrong repository, using outdated interfaces, or making unauthorised changes.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/shrec/aiworkhub/agents-md
Clone the repo
git clone --depth 1 https://github.com/shrec/AIWorkHub

Made for: Codex, OpenCode.

Per session 1,041 This file is loaded in full into every session.
When invoked 1,041 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.01041 $0.01041
Opus 5 $0.00521 $0.00521
Sonnet 5 $0.00208 $0.00208
Haiku 4.5 $0.00104 $0.00104

Measured 2d ago against content hash 7dbf57347304, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

AIWorkHub AGENTS.md 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.

AGENTS.md · 70 lines

How it starts

The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AIWorkHub MCP Agent Notes

This is the standalone canonical AIWorkHub repository.

Rules:

  • Keep every repository's task and context source of truth in that repository's own .aiworkhub/ storage; never move task cards across repositories.
  • Use the native AIWorkHub MCP surfaces rather than legacy AITools scripts or databases as model interfaces.
  • Write operations must remain explicit and gated by AIWORKHUB_ALLOW_WRITES=1.
  • Never run git add -A from this repository against the parent repository.

When printing worker auto-pickup or exact claim-start cards, use one self-contained fenced block per model. The instruction below must be the final non-empty line inside the same copyable block, never adjacent prose:

გაუშვი პარალელური სუბაგენტები და რომ დასრულდება დაუბრუნე კოდექსს რევიუსთვის

Target: AGENTS.md

AIWorkHub MCP tool-use policy

Order:

  1. validate the injected AIWorkHub Task MCP receipt, identity and scope.
  2. consume and acknowledge the injected project-context receipt.
  3. manager uses aiworkhub_manager_source_graph_query; worker uses aiworkhub_worker_source_graph_query.
  4. manager uses aiworkhub_manager_session_current_state; worker uses aiworkhub_worker_session_current_state.
  5. manager uses aiworkhub_manager_ai_memory_search; worker uses aiworkhub_worker_ai_memory_search.
  6. manager uses aiworkhub_manager_kb_search/get/related; worker uses aiworkhub_worker_kb_search/get/related.
  7. manager uses aiworkhub_manager_context_graph_search, aiworkhub_manager_context_graph_range and aiworkhub_manager_context_graph_related when enabled; workers never access Context Graph.
  8. execute exact card action and validation. Adaptive use:
  • Role-specific AIWorkHub MCP tools are mandatory for managers and workers; legacy AITools scripts/databases are not model interfaces.
  • Verified repo and repo_id outrank cwd, workspace_roots, environment_context and chat prose; on mismatch stop before filesystem access and switch/reload the route, never inspect the hinted repo as fallback.
  • Task MCP receipt is always required; Source Graph is required for code tasks.
  • Session Manager, AI Memory and KB run only when the card requests them or the task is non-trivial.
  • Workers submit durable context changes only through the session/AI Memory/KB write-intent tools; a verified manager accepts or rejects each intent before canonical apply. Never write context databases directly.
  • Do not make empty irrelevant calls to satisfy ceremony. Source Graph gate:
  • When source_graph_required is true, stop if its bundle is unavailable, empty, stale or unacknowledged.
  • Never use grep, rg, find, tree, broad cat/sed or recursive listing while Source Graph can index/process the target.
  • A bounded exact-target fallback is allowed only after Source Graph reports that target unsupported or unindexed; record that reason.
  • Re-query whenever the active symbol, dependency boundary, failure hypothesis, edit scope or validation target materially changes.
  • Set workflow_stage on every Source Graph call: orientation, implementation, validation, review or rework; never relabel old calls after the fact.
  • Start with focus/slice; escalate from returned evidence to context/calls/trace, impact, testmap/coverage and then a typed bundle only when needed.
  • Use body for an exact symbol and bodygrep for indexed literal/body text; refresh once before any recorded bounded fallback.
  • After Source Graph finds an exact target, prefer body/file preview; otherwise use a bounded read and never reread an unchanged range.
  • For edits prefer aiworkhub_worker_semantic_edit_prepare/apply with the smallest verified range.
  • Final HMAC-authenticated MCP audit ledger receipts distinguish injected, live, zero-hit and cache-hit calls plus modes and fallbacks; one preflight query is not continuous use. Exact-command exception:
  • Exact validation/build/test commands named by the card are allowed.
  • Exact known-path reads from the card or Source Graph are allowed; they are not broad discovery. Session Manager:
  • Recover current state before non-trivial assumptions and preserve the returned session identity in the handoff.
  • Never store secrets or fabricate session evidence. Manager Context Graph:
  • Manager-only when enabled: search for non-trivial continuation, compaction/handoff recovery or prior-conversation facts; use range/related only from returned evidence.
  • Workers never query or write Context Graph; durable context uses Session/AI Memory/KB write intents.
  • Disabled or zero-hit is not failure; no empty ceremonial calls. AI Memory:
  • After session recovery, issue one bounded task-specific query.
  • Reuse returned durable decisions/lessons.
  • Do not query legacy memory files directly. KB:
  • Query authoritative project contracts/docs for unresolved factual context and preserve source identity.
  • After a zero hit, do not repeat the query unless task scope changes. Stop at Codex review.

Read the full file on GitHub · 70 lines

Changes

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.

  1. 2d ago First seen · 70 lines · 1,041 tokens per session scan A 7dbf57347304

Subscribe to this mod's changes

AIWorkHub AGENTS.md is an instructions file published in the GitHub repository shrec/AIWorkHub (7 stars, last pushed 2d ago), licensed MIT. It adds 1,041 tokens to every session, about $0.0052 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 2026-08-31.