agent-first-data 0.27.0

A naming convention that lets AI agents understand your data without being told what it means, plus a CLI and library for reading and safely editing structured JSON, TOML, YAML, dotenv, and INI documents.
Documentation

Agent-First Data

A naming convention that lets AI agents understand your data without being told what it means, plus a CLI and library for reading and safely editing structured JSON, TOML, YAML, dotenv, and INI documents.

Ask your agent: "Apply the Agent-First Data convention across my project's fields, config, and logs."

The problem: data doesn't say what it means

An agent reads {"timeout": 5000} from a tool. Seconds or milliseconds? It guesses — and a 5-second timeout silently becomes 83 minutes. The same trap is everywhere: {"price": 1200} gets charged as $1,200 instead of $12.00; {"created": 1738886400} is treated as an ID instead of a date.

It reads {"api_key": "sk-live-abc123"} and writes that line straight into a log file, because nothing marked the value as a secret.

Then it moves to the next tool, which calls the same value elapsed instead of duration — so what the agent learned about one tool tells it nothing about the next.

None of this is carelessness. The data never says what it means, so the meaning has to live somewhere else — documentation, a schema, a prompt. That copy goes stale, gets lost, or was never written.

What it does: put the meaning into the field name

Agent-First Data puts the meaning into the field name itself. Call the field timeout_ms and there is nothing left to guess — the name says milliseconds. Call it api_key_secret and any AFDATA output boundary that follows the convention hides it automatically.

It is a convention, not a framework — a small set of name endings, plus a tiny library in four languages that reads and formats them.

  • Names carry meaning. Endings like _ms, _bytes, _secret, _usd_cents, and _percent put units and intent directly into the field name.
  • One set of data, three ways to show it. The same fields render as JSON or YAML — both keep original keys and types, for machines — or as a single human log line with units formatted for scanning. Secrets are removed in every form.
  • Structured secrets are redacted. Anything ending in _secret is hidden when a structured value passes through an AFDATA redactor or renderer. A _url field keeps its address but scrubs the userinfo password and secret-named query parameters. Legacy names like api_key can be protected by passing an explicit secret-name list.
  • Logging agents can read. Structured logs that follow the same rules, with request-scoped fields.
  • One contract in four languages. Rust, Go, Python, and TypeScript share wire behavior and fixtures while keeping idiomatic native API shapes.

A quick look

One record — a log event with a timeout, an API key, and a database URL — rendered three ways. Nothing is configured; the field names carry everything.

{"kind":"log","log":{"event":"startup","args":{"timeout_s":30,"api_key_secret":"sk-123"},"db_url":"postgres://user:p@ss@db/app?token_secret=abc"},"trace":{"duration_ms":1280}}

JSON and YAML keep original keys and types (structure-preserving) and only redact secrets:

---
kind: "log"
log:
  args:
    api_key_secret: "***"
    timeout_s: 30
  db_url: "postgres://user:***@db/app?token_secret=***"
  event: "startup"
trace:
  duration_ms: 1280

Plain is the one human renderer — it strips unit suffixes and formats values for scanning:

kind=log log.args.api_key=*** log.args.timeout=30s log.db_url="postgres://user:***@db/app?token_secret=***" log.event=startup trace.duration=1.28s

Supported suffixes

Category Suffixes
Duration _ns, _us, _ms, _s, _minutes, _hours, _days
Timestamps _epoch_ns, _epoch_ms, _epoch_s, _rfc3339
Size _bytes (integer everywhere — config and output alike)
Currency _msats, _sats, _usd_cents, _eur_cents, _jpy, _{code}_cents, _{code}_micro (code is 3–4 ASCII letters)
Strict strings _bcp47, _utc_offset, _rfc3339_date, _rfc3339_time
Other _percent, _secret, _url

JSON and YAML keep suffixes and raw values; Plain strips duration/size/currency/timestamp suffixes after formatting the value, and never strips _url/_bcp47/_utc_offset/_rfc3339_date/_rfc3339_time.

Redaction boundary

AFDATA redaction is intentionally field-name based:

  • _secret / _SECRET redacts the whole value or subtree to ***.
  • Legacy names such as api_key are redacted only when the caller passes an explicit secret_names list; matching is exact field-name equality.
  • _url fields scrub the userinfo password and query parameters whose names end in _secret or appear in secret_names; broad names such as api_key, token, or password are not hidden by default.
  • Free-form strings are not scanned for arbitrary secrets. If a secret URL is embedded in prose, redact the URL first with redact_url_secrets.

The suffix protects structured fields only after they pass through an AFDATA redactor or renderer. It cannot remove a live secret from process argv, shell history, /proc, a parent process, or third-party logs. Avoid putting secrets in argv; if a tool records its invocation, pass argv through redact_argv before logging it.

There are no named redaction profiles. Use the default policy (All), an explicit secret_names list, or the documented scoped policies (TraceOnly, Off) for deliberate exceptions.

Reading and editing config documents

Beyond emitting AFDATA, the library and afdata CLI read and safely edit structured documents — JSON, TOML, YAML, dotenv, and INI — by dot-path:

afdata get config.toml server.port                    # one value as an AFDATA record (secrets redacted)
host=$(afdata value config.toml server.host)          # raw scalar, for shell substitution
afdata set config.toml server.port 8080 --value-type number

Use get when the next step must preserve JSON types and structure; value deliberately turns one scalar into raw shell bytes. set creates missing object parents along the requested dot-path, but refuses to traverse an existing scalar or incompatible container.

Edits are source-preserving and atomic: comments, key order, and formatting survive; a write is read back before it lands, so an edit that would leave a file its own parser rejects fails instead of succeeding; and a failed write leaves the original untouched. Values are never guessed — a bare value is always a string, and an exact type is asked for, not inferred. A name marks its whole subtree, so a _secret node stays redacted however you address it, and revealing it is an auditable opt-in. Errors carry stable codes and never quote the document, because an error event is the thing an agent logs.

Flags, error codes, and exit codes are in docs/cli.md; the Rust library is agent_first_data::document (Document / DocumentFile).

Token-efficient CLI discovery

Help is a normal result, not an exception to the output contract, and it answers in one round trip. afdata --help returns a protocol-v1 result whose help-v2 payload carries every legal shape of the command, each complete. afdata set --help, for example, shows the distinct set-value, set-null, and set-secret shapes instead of an argument catalog that leaves an agent to solve conflicts. There is no second level to ask for: what it could omit — the optional arguments — would be registered but undiscoverable to a caller that stopped at the first. --output plain renders the same catalog for humans, and --docs renders the whole registry as Markdown. One cli-spec-v1 registry generates argv parsing, typed invocations, combination validation, output plans, help, and that reference. Contracts: cli-spec-v1 and cli-help-v2.

Writing AFDATA-style Bash scripts

The CLI embeds a sourceable Bash 3.2+ authoring kit:

#!/usr/bin/env bash
set -euo pipefail
_AFDATA_BASH_SOURCE="$("${AFDATA_BIN:-afdata}" shell bash)"
source /dev/stdin <<<"$_AFDATA_BASH_SOURCE"
unset _AFDATA_BASH_SOURCE

afdata_args_begin "build.sh [OPTIONS] PACKAGE [-- CARGO_ARG ...]"
afdata_args_flag release --release "Build release artifacts"
afdata_args_positional package PACKAGE "Package to build"
afdata_args_rest CARGO_ARG "Arguments forwarded to cargo"
afdata_args_parse "$@"

afdata_log info "Building ${package}"
if [ "$release" = true ]; then
  afdata_run cargo build --release ${AFDATA_ARGS_REST[@]+"${AFDATA_ARGS_REST[@]}"}
else
  afdata_run cargo build ${AFDATA_ARGS_REST[@]+"${AFDATA_ARGS_REST[@]}"}
fi
afdata_result "Build complete"

The helpers handle long-form argument parsing, --help, AFDATA output flags, raw config reads, and structured log/result/error events. afdata_run keeps child stdin/stdout/stderr and TTY interaction untouched; only the Bash script's own lifecycle is structured, with a terminal error on child failure. Use afdata_call when an AFDATA Bash parent invokes an AFDATA Bash child and must remain the sole owner of the final result. See the complete Bash authoring kit guide.

Where to use it: CLI flags, config files, logs, and API responses

  • Building a CLI tool an agent will call — your output is understood correctly the first time, with no extra schema to ship. Results land on stdout and errors on stderr by default, so a shell capture or pipe never mistakes a failure for data; a single --output-to stdout collapses everything onto one stream when a consumer would rather branch on kind.
  • Writing a config file — keys like timeout_s or db_password_secret make settings self-explanatory to whoever edits them, and secrets stay hidden when the config is printed back.
  • Adding logs to a service — the same lines stay readable for a person and parseable for an agent.
  • Designing an API response or event payload — units and sensitivity travel with the data, across every boundary it crosses.
  • Auditing for leaked secrets — one naming rule (_secret) makes redaction automatic instead of case-by-case.

One shared contract, four languages

The shared core surface ships in Rust, Go, Python, and TypeScript (each in its own casing):

  • Protocol builders json_result / json_error / json_progress / json_log → .build() → an event; reader decode_protocol_event(text) → a typed decoded event.
  • Output render(value, format, options) — the single value × format × options → string entry point.
  • Redaction redacted_value / redact_url_secrets for paths that bypass render.
  • CLI primitives cli_parse_output, cli_parse_log_filters, build_cli_error, build_cli_version, and the CliEmitter.

The Rust crate is the reference implementation of CliSpec, closed-world combination resolution, version, and help-v2; the whole CLI surface sits behind the default-on cli feature. Argv is only ever parsed through a registry — there is no raw pre-parser. Skill admin, stream redirection, and tracing initialization are also Rust-only. The shared surface is enumerated in spec/api-surface.json; other SDK compilers consume the same serialized CLI spec and fixtures as they migrate.

Adopt it: hand the convention to your coding agent

Agent-First Data is a convention, not a dependency you wire in by hand — and adopting a convention is exactly the kind of work you now hand to an agent. There's even an Agent Skill for exactly that — the convention in a form an agent reads and applies directly. Paste this to your coding agent:

Learn the Agent-First Data convention: read https://agentfirstkit.com/agent-first-data/docs/specification and https://agentfirstkit.com/agent-first-data/docs/agent-skill. Then look at the codebase we're working in and tell me whether adopting the convention would help it — and if so, how: which fields and config keys to rename, and where the output and logging helpers fit.

Install the Libraries

cargo add agent-first-data --no-default-features   # Rust library
pip install agent-first-data     # Python
npm install agent-first-data     # TypeScript
go get github.com/agentfirstkit/agent-first-data/go   # Go

Install the CLI

The afdata CLI provides the same formatting, redaction, and protocol-event helpers from any shell, with no toolchain required:

# prebuilt binary
brew install agentfirstkit/tap/afdata   # macOS / Linux
scoop bucket add agentfirstkit https://github.com/agentfirstkit/scoop-bucket && scoop install afdata   # Windows

# or from crates.io
cargo install agent-first-data

Prebuilt archives are also available from GitHub Releases.

Validate an Agent Skill

afdata skill validate checks a SKILL.md against the official metadata constraints with a strict YAML parser. Passing a directory also verifies that its name matches the front-matter name. Use afdata skill install, status, and uninstall to manage the bundled skill.

afdata skill validate skills/agent-first-data

Docs

License

MIT