pre-action-interceptors

pre-action-interceptors is a skill for Claude Code, Codex from Bitterbot-AI/bitterbot-desktop. It costs 44 tokens per session (513 once invoked), scanned A, original, MIT.

A set of automatic checks placed between an agent's proposed tool action and its execution. These checks can add context, require another step, rewrite an action, or block it.

In plain words
What is it for?
It is for handling blocked or rewritten tool calls, following required prerequisites, and investigating active safeguards.
Why use it?
It explains tool errors marked as interceptor messages and helps prevent actions that lack required checks or context.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for handling blocked or rewritten tool calls, following required prerequisites, and investigating active safeguards.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/bitterbot-ai/bitterbot-desktop/pre-action-interceptors
About the project

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.

Bitterbot-AI/bitterbot-desktop · 2,473 stars · on GitHub · bitterbot.ai

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.

Any agent
npx skills add Bitterbot-AI/bitterbot-desktop --skill pre-action-interceptors
Clone the repo
git clone --depth 1 https://github.com/Bitterbot-AI/bitterbot-desktop

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for pre-action-interceptors

README.md
[![agentmods](https://agentmods.dev/badge/skills/bitterbot-ai/bitterbot-desktop/pre-action-interceptors/github.svg)](https://agentmods.dev/skills/bitterbot-ai/bitterbot-desktop/pre-action-interceptors)
Your own site
<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.

agentmods 80×15 button for pre-action-interceptors

Your own site · 80×15
<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>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 513 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $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

Measured 25d ago against content hash 71750164b4ca, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

skills/pre-action-interceptors/SKILL.md · 36 lines

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 a memory_search before you send a message containing an unverified factual assertion.
  • route-by-query-shape: requires deep_recall instead of memory_search for relationship-shaped queries.
  • protocol-quiet-in-groups: blocks send_message in 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.

Read the full file on GitHub · 36 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. 25d ago First seen · 36 lines · 44 tokens per session scan A 71750164b4ca

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

aimh-recall-diagnose

A three-level diagnostic guide for failed memory retrieval, checking stored fields, the search index database, and then the code path.

Traceless-zero/AI-MemoryHub · 138 tokens

debug

Enable debug logging for this session and help diagnose issues.

asgeirtj/system_prompts_leaks · 12 tokens

ai-core/debug-logging

Pluggable, category-toggleable debug logging for TanStack AI activities. Toggle with debug: true | false | DebugConfig on chat(), summarize(), generateImage(), generateSpeech(), generateTranscription(), generateVideo(). Categories: request, provider, output, middleware, tools, agentLoop, config, errors. Pipe into…

TanStack/ai · 104 tokens

ha-logs

A read-only troubleshooting skill for querying Hope Agent’s local SQLite databases, which store logs, conversations, and background-job status.

shiwenwen/hope-agent · 183 tokens

ha-self-diagnosis

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…

shiwenwen/hope-agent · 114 tokens

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.

Prismer-AI/PrismerCloud · 67 tokens