agent-first-data 0.25.0

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 tool 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.
  • Secrets stay secret. Anything ending in _secret is hidden automatically, in output and in logs. 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.
  • The same in four languages. One identical API across Rust, Go, Python, and TypeScript.

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.

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

Edits are source-preserving and atomic — comments, key order, and formatting survive; a failed write leaves the original untouched, and the CLI refuses to write through a symlink. A bare value is always a string (007 never becomes 7); --value-type string|number|bool|null|json writes an exact type. _secret fields stay redacted even on a directly targeted get — value --reveal-secret is the auditable opt-in. Every command's first positional is the FILE (- reads stdin for reads only); errors carry stable error.codes (document_path_not_found, document_type_mismatch, …). The Rust library is agent_first_data::document (Document / DocumentFile); TOML/YAML/dotenv/INI are feature-gated, JSON is core.

Token-efficient CLI discovery

Help is a normal result, not an exception to the output contract. afdata --help answers in the same format as every other command — a protocol-v1 result envelope — so an agent discovering an unfamiliar CLI parses the surface instead of scraping 80-column prose. --output plain is the conventional terminal text; --recursive is a compact whole-surface index; --output markdown is the documentation export. The model is deliberately small: no long-form prose, no repeated inherited globals, no eagerly embedded version values. The exact contract is spec/cli-help-v1.schema.json.

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 helpers cli_parse_output, cli_parse_log_filters, build_cli_error, build_cli_version, cli_handle_version_or_continue, and the CliEmitter.

Four capabilities are Rust-only and deliberately outside the cross-language contract: output-aware Clap help (cli_handle_version_or_help_or_continue and the help renderers), skill admin (SKILL.md validation plus install/uninstall/status), stream redirection (--stdout-file / --stderr-file), and tracing/logging init (afdata_tracing::try_init). The exact shared surface is enumerated in spec/api-surface.json.

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