v0.1.8

Changelog

features

• 30b483d feat(scan): detect Nakama profile workspaces (#8)

fixes

• 2035fb9 fix(website): override minimatch under typescript-eslint (#5)

---

Use it as a step

- uses: qansaid/agentdiet@v1
  with:
    max-tokens: 4000   # fail the job above this always-on budget; 0 disables the gate
    window: 200000     # context window for the share column

input	default	meaning
path	.	repository path to audit
max-tokens	4000	fail when always-on tokens exceed this; 0 disables the gate
window	200000	context window used for the share column
version	latest	agentdiet version to install

output	meaning
always-on-tokens	tokens injected into every request
grade	A-F against the budget

Deterministic and offline: no API key, no network at audit time. The
estimator (prose chars/4.5 + fenced-code chars/3.4, rounded up per segment)
is calibrated against cl100k_base at a measured MAE of 8%.

Why this exists

Every agent runtime has a folder of files it loads unconditionally. They
grow by accretion — a rule per incident, a table per architecture decision —
until the agent spends 12k tokens of attention on stale instructions before
it has read the question. agentdiet turns that into a number in the CI log
and a budget that fails the pull request that would have made it worse.

MIT licensed. Source, docs, and real sample output:
https://github.com/qansaid/agentdiet

v0.1.7

Changelog

features

• a2a0eb9 feat(website): add the AgentDiet product site (#1)

other

• 3a615df chore(deploy): script and document the Cloudflare Tunnel deployment (#2)
• 83fd1d2 docs(readme): add a theme-aware banner, pipeline diagram and collapsible reference (#3)
• 65794ca docs(site): replace prose em dashes with ordinary punctuation (#4)

---

Use it as a step

- uses: qansaid/agentdiet@v1
  with:
    max-tokens: 4000   # fail the job above this always-on budget; 0 disables the gate
    window: 200000     # context window for the share column

input	default	meaning
path	.	repository path to audit
max-tokens	4000	fail when always-on tokens exceed this; 0 disables the gate
window	200000	context window used for the share column
version	latest	agentdiet version to install

output	meaning
always-on-tokens	tokens injected into every request
grade	A-F against the budget

Deterministic and offline: no API key, no network at audit time. The
estimator (prose chars/4.5 + fenced-code chars/3.4, rounded up per segment)
is calibrated against cl100k_base at a measured MAE of 8%.

Why this exists

Every agent runtime has a folder of files it loads unconditionally. They
grow by accretion — a rule per incident, a table per architecture decision —
until the agent spends 12k tokens of attention on stale instructions before
it has read the question. agentdiet turns that into a number in the CI log
and a budget that fails the pull request that would have made it worse.

MIT licensed. Source, docs, and real sample output:
https://github.com/qansaid/agentdiet

v0.1.6

Changelog

other

• 637e4a1 docs(release): carry Marketplace listing copy in release footer

---

Use it as a step

- uses: qansaid/agentdiet@v1
  with:
    max-tokens: 4000   # fail the job above this always-on budget; 0 disables the gate
    window: 200000     # context window for the share column

input	default	meaning
path	.	repository path to audit
max-tokens	4000	fail when always-on tokens exceed this; 0 disables the gate
window	200000	context window used for the share column
version	latest	agentdiet version to install

output	meaning
always-on-tokens	tokens injected into every request
grade	A-F against the budget

Deterministic and offline: no API key, no network at audit time. The
estimator (prose chars/4.5 + fenced-code chars/3.4, rounded up per segment)
is calibrated against cl100k_base at a measured MAE of 8%.

Why this exists

Every agent runtime has a folder of files it loads unconditionally. They
grow by accretion — a rule per incident, a table per architecture decision —
until the agent spends 12k tokens of attention on stale instructions before
it has read the question. agentdiet turns that into a number in the CI log
and a budget that fails the pull request that would have made it worse.

MIT licensed. Source, docs, and real sample output:
https://github.com/qansaid/agentdiet

v0.1.5

Measure the context your AI coding agent loads before you type a single word and fail the build when it grows.

agentdiet is a single static binary that walks a repository, finds every file your agent runtime injects into the prompt AGENTS.md, CLAUDE.md, .cursorrules, copilot-instructions.md, skills, MCP declarations and reports what that costs: always-on tokens paid on every request, the on-demand surface, cost per 1,000 sessions, and an A–F grade against a budget you can enforce in CI.

It does not lint your code. It lints the instructions: the one part of the repository that every agent pays for on every turn, and the part nobody measures.

ALWAYS-ON   paid on every request, before you type
     442   0.22%    41 L  .claude/CLAUDE.md

TOTAL      442 always-on tokens  (0.22% of a 200,000-token window)
GRADE      A  (budget 1,500)   on-demand surface: 76,533 tokens
COST       $1.33 per 1,000 sessions at $3.00 per 1M input tokens

Use it as a step

- uses: indraoverflow/agentdiet@v0.1.5
  with:
    max-tokens: 4000   # fail the job above this always-on budget; 0 disables the gate
    window: 200000     # context window for the share column

input	default	meaning
path	.	repository path to audit
max-tokens	4000	fail when always-on tokens exceed this; 0 disables the gate
window	200000	context window used for the share column
version	latest	agentdiet version to install

output	meaning
always-on-tokens	tokens injected into every request
grade	A–F against the budget

Deterministic and offline: no API key, no network at audit time. The estimator (prose chars/4.5 + fenced-code chars/3.4, rounded up per segment) is calibrated against cl100k_base at a measured MAE of 8%.

What's new in v0.1.5

• agentdiet --version now reports the version it was built from instead of dev, including builds that receive no linker flags — so go install github.com/indraoverflow/agentdiet@latest prints agentdiet 0.1.5, and CI logs name the version they actually ran.
• The v1 major tag is moved onto each released commit by the release workflow, so uses: indraoverflow/agentdiet@v1 resolves to the same commit as v0.1.5.

Why this exists

Every agent runtime has a folder of files it loads unconditionally. They grow by accretion — a rule per incident, a table per architecture decision — until the agent spends 12k tokens of attention on stale instructions before it has read the question. agentdiet turns that into a number in the CI log and a budget that fails the pull request that would have made it worse.

MIT licensed. Source, docs, and real sample output: https://github.com/indraoverflow/agentdiet

v0.1.4

Measure the context your AI coding agent loads before you type a single word — and fail the build when it grows.

agentdiet is a single static binary that walks a repository, finds every file your agent runtime injects into the prompt — AGENTS.md, CLAUDE.md, .cursorrules, copilot-instructions.md, skills, MCP declarations — and reports what that costs: always-on tokens paid on every request, the on-demand surface, cost per 1,000 sessions, and an A–F grade against a budget you can enforce in CI.

It does not lint your code. It lints the instructions: the one part of the repository that every agent pays for on every turn, and the part nobody measures.

ALWAYS-ON   paid on every request, before you type
     442   0.22%    41 L  .claude/CLAUDE.md

TOTAL      442 always-on tokens  (0.22% of a 200,000-token window)
GRADE      A  (budget 1,500)   on-demand surface: 76,533 tokens
COST       $1.33 per 1,000 sessions at $3.00 per 1M input tokens

Use it as a step

- uses: indraoverflow/agentdiet@v0.1.4
  with:
    max-tokens: 4000   # fail the job above this always-on budget; 0 disables the gate
    window: 200000     # context window for the share column

input	default	meaning
path	.	repository path to audit
max-tokens	4000	fail when always-on tokens exceed this; 0 disables the gate
window	200000	context window used for the share column
version	latest	agentdiet version to install

output	meaning
always-on-tokens	tokens injected into every request
grade	A–F against the budget

Deterministic and offline: no API key, no network at audit time. The estimator (prose chars/4.5 + fenced-code chars/3.4, rounded up per segment) is calibrated against cl100k_base at a measured MAE of 8%.

Why this exists

Every agent runtime has a folder of files it loads unconditionally. They grow by accretion — a rule per incident, a table per architecture decision — until the agent spends 12k tokens of attention on stale instructions before it has read the question. agentdiet turns that into a number in the CI log and a budget that fails the pull request that would have made it worse.

MIT licensed. Source, docs, and real sample output: https://github.com/indraoverflow/agentdiet

v0.1.3

Changelog

other

• 330e3cb ci(release): pin release tag to the pushed semver tag and stop moving v1 from publishing

v0.1.0

Changelog

features

• 36346ec feat: measure the always-on context tax of AI coding agents