tool-calling-principles

tool-calling-principles is a skill for Claude Code, Codex from j4flmao/agent-skills. It costs 24 tokens per session (692 once invoked), scanned A, original, MIT.

A guide to designing agents that use tools and return structured results. It covers checking the environment, following an exact output format, and recovering from tool errors.

In plain words
What is it for?
Planning tool calls, validating JSON outputs, checking available environment details, and responding safely when a tool times out or returns unexpected data.
Why use it?
It reduces made-up answers and fragile tool use by requiring agents to verify assumptions and handle bad or incomplete responses carefully.

Skill for Claude CodeCodex

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

Good fit Planning tool calls, validating JSON outputs, checking available environment details, and responding safely when a tool times out or returns unexpected data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/j4flmao/agent-skills/tool-calling-principles
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 j4flmao/agent-skills --skill tool-calling-principles
Clone the repo
git clone --depth 1 https://github.com/j4flmao/agent-skills

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 tool-calling-principles

README.md
[![agentmods](https://agentmods.dev/badge/skills/j4flmao/agent-skills/tool-calling-principles/github.svg)](https://agentmods.dev/skills/j4flmao/agent-skills/tool-calling-principles)
Your own site
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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 tool-calling-principles

Your own site · 80×15
<a href="https://agentmods.dev/skills/j4flmao/agent-skills/tool-calling-principles"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/tool-calling-principles.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 692 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00024 $0.00692
Opus 5 $0.00012 $0.00346
Sonnet 5 $0.00005 $0.00138
Haiku 4.5 $0.00002 $0.00069

Measured 9d ago against content hash 1e0165433fdc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

tool-calling-principles 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 9d 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/ai/ai-agents/tool-calling-principles/SKILL.md · 51 lines

How it starts

The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Tool/Environment Grounding: The Ontology of Action

An agent without tools is a brain in a vat—capable of hallucinating universes but powerless to perturb reality. Tools are the sensory organs and actuator limbs of synthetic intelligence. Grounding is the rigorous discipline of tethering probabilistic reasoning to deterministic environments.

To call a tool is not merely to execute a function; it is to collapse a wave of potential text into a localized impact on the external world.

I. First Principles of Actuation

  1. Strict Structured Outputs (The Schema Contract) Language models speak in infinite semantic permutations; the environment demands rigid syntactic conformity. The interface between thought and action is the JSON Schema. Axiom of Structure: Never rely on emergent formatting. Enforce rigorous type constraints, required fields, and semantic descriptions. The schema is the absolute law governing the interface.

  2. Defensive Calling (The Principle of Skepticism) The environment is hostile, stochastic, and latent. A tool call must be defensive—assuming latency timeouts, malformed responses, or state changes. Axiom of Defense: Validate assumptions prior to actuation. If reading a file, assume it may be locked or absent. Never commit destructive actions without explicit verification of state.

  3. Error Recovery & Self-Correction (The Resilience Loop) Failure is the default state of complex environments. When a limb fails to grasp an object, the brain does not halt; it recalculates the trajectory. When a tool throws an error, the agent must parse the stack trace, hypothesize the cause, and iterate the call. Axiom of Resilience: An error is not a termination condition; it is high-fidelity sensory feedback. Catch the exception, reflect on the delta between expectation and reality, and adjust the schema parameters.

II. The Actuation Cycle

%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
flowchart TD
    Thought((Cognitive Intent)) -->|Schema Mapping| Validate{Pre-call Validation}
    Validate -- Valid --> Action[Tool Execution]
    Validate -- Invalid --> Correct1(Internal Re-mapping)
    Correct1 --> Validate
    
    Action --> Response{Environment Feedback}
    Response -- Success --> Observe(State Grounding Update)
    Response -- Exception/Error --> Reflect[Analyze Stack Trace / Error Msg]
    
    Reflect --> Hypothesize(Hypothesize Failure Mode)
    Hypothesize --> Adjust(Adjust Parameters/Logic)
    Adjust --> Validate
    
    Observe --> NextThought((Subsequent Intent))

Read the full file on GitHub · 51 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. 9d ago First seen · 51 lines · 24 tokens per session scan A 1e0165433fdc

Subscribe to this mod's changes

tool-calling-principles is a skill published in the GitHub repository j4flmao/agent-skills (22 stars, last pushed 2d ago), licensed MIT. It adds 24 tokens to every session and 692 once invoked, about $0.0001 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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