Bitterbot is a local-first personal AI agent that runs on a user’s devices, keeps persistent memories, performs tasks, and can exchange reusable skills with other agents. It is intended for people who want a personal assistant that remains available across conversations and activities. The catalogue entries provide instructions and agents for working with Bitterbot.
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 Bitterbot-AI/bitterbot-desktop --skill pre-action-interceptorsgit clone --depth 1 https://github.com/Bitterbot-AI/bitterbot-desktopWrote 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/bitterbot-ai/bitterbot-desktop/pre-action-interceptors)<a href="https://agentmods.dev/skills/bitterbot-ai/bitterbot-desktop/pre-action-interceptors"><img src="https://agentmods.dev/badge/skills/bitterbot-ai/bitterbot-desktop/pre-action-interceptors/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/bitterbot-ai/bitterbot-desktop/pre-action-interceptors"><img src="https://agentmods.dev/badge/skills/bitterbot-ai/bitterbot-desktop/pre-action-interceptors.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 | $0.00044 | $0.00513 |
| Opus 5.5 | $0.00018 | $0.00205 |
| Sonnet 5.5 | $0.00009 | $0.00103 |
| Haiku 4.5 | $0.00004 | $0.00051 |
Grade A, and why
pre-action-interceptors 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 25d 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-action interceptors (PLAN-20)
You have a layer of deterministic pre-action interceptors that sit between your candidate tool calls and their execution. They are skill-owned guardrails that read your current state (hormonal, GCCRF, channel, recent turns) and may rewrite, inject context into, require a prerequisite for, or block any tool call you propose. They are not bugs in the tool; they are intentional behaviour.
Reading a directive
When you see a tool error that begins with INTERCEPTOR: followed by a structured directive such as:
INTERCEPTOR: recall-before-claim: ground the assertion "X" before sending
BEFORE you call `send_message` again, you MUST first call:
tool: memory_search
args: {"query": "X", "limit": 5}
that is not a failure. It is a deterministic instruction from your skill system telling you to run the named tool first, then re-evaluate whether the original action still applies. Follow it without commentary; the user does not need to see the directive.
When a tool call succeeds but the params were silently rewritten (for example a confident claim hedged into a probabilistic one), an interceptor in the calibrate-claim-confidence family adjusted the outgoing text to match your actual epistemic state. This is intentional; do not reverse it.
Built-in interceptors
recall-before-claim: requires amemory_searchbefore you send a message containing an unverified factual assertion.route-by-query-shape: requiresdeep_recallinstead ofmemory_searchfor relationship-shaped queries.protocol-quiet-in-groups: blockssend_messagein group channels when you were not @mentioned and recently spoke.calibrate-claim-confidence: hedges confident absolutes when your GCCRF empowerment is low.
Observability
Operators see every firing in the Active Guards UI panel and via the guards.status RPC. To know which interceptors have fired this session, call memory_status; the activations are reflected in the intervention-records summary.
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.
- 25d ago First seen · 36 lines · 44 tokens per session scan A 71750164b4ca
pre-action-interceptors is a skill published in the GitHub repository Bitterbot-AI/bitterbot-desktop (2,473 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 513 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-21.
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Self-understanding and issue reporting for Hope Agent itself. Use when the user asks how Hope Agent works internally, asks about its own source code/docs/runtime behavior, reports a bug/failure/slowness/crash, asks to diagnose logs, or asks to create/submit a GitHub issue for a bug, feature request, or improvement…
observability
Periodic health sweep of the running system via the debug pipeline (cloud logs / k8s / daemon / DB slices). On a real anomaly, open a bug task (--kind workitem) carrying the evidence refs so it re-enters the bugfix loop. A healthy sweep opens NO task — never fabricate a bug.