Pair with the worker-side simplification on the librefang PR — the
worker is now a pure transport (no key material, no signing) and this
repo's CI takes over signature production.
scripts/sign-plugins-index.mjs reads REGISTRY_PRIVATE_KEY from a GitHub
Actions secret, signs plugins-index.json with Ed25519, and writes
plugins-index.json.sig alongside it. Aborts loudly when the secret is
missing so a misconfigured CI can't silently ship an unsigned payload.
The workflow now runs build → sign → commit (.json + .sig) → push →
poke worker /refresh. The worker fetches the committed .json + .sig
verbatim and stores both — the daemon then verifies against the
embedded pubkey it ships with.
Closes PR review CRITICAL #1: the worker is no longer a sign-anything
oracle reachable via REGISTRY_REFRESH_TOKEN. Trust root is now this
repo's branch protection + Actions secret scope, not a token any CI
job that can talk to stats.librefang.ai can use to mint signatures.
Note: the keypair was rotated as part of this change (PR not yet
merged so no daemon TOFU pins exist). New pubkey:
ClGa0Ucap8NdrKAy1rw9Tt6A9I8eg4zJ53+xIuKMuq0=
The plugins-index.json.sig committed here is signed with the matching
new private key, in lockstep with the daemon EMBEDDED_REGISTRY_PUBKEY
constant and all three worker [vars] entries.
Pair with the existing plugins-index.json path (which feeds the
daemon's signed install lane). registry-index.json mirrors the
dict-shaped payload the registry-worker's cron currently builds via
40+ GitHub Contents API calls — but built locally from the checked-out
tree by scripts/build-registry-index.mjs, so the worker only fetches
ONE file per category type (2 total: plugins + registry) on refresh.
Workflow now picks up content changes across all 8 category dirs
(was: plugins/ only) so dashboard updates land within seconds of a
push instead of waiting for the 02:00 UTC cron tick.
The dashboard's /api/registry endpoint reads kv_store('registry_data');
the worker's forced-refresh now writes that key with these bytes and
purges the Cache-API entry, so the next dashboard hit sees fresh
data instead of the 1h-cached previous payload.
Generated counts on first build: 11p 17h 32a 60s 44c 57pr 22w 33mcp.
Walking ~40+ plugin TOMLs via the GitHub Contents API from the worker
exceeded the Workers Free 50-subrequest-per-invocation limit, leaving
the daemon's signed plugins index either empty or partial after every
forced refresh.
Move the walk into the repo: scripts/build-plugins-index.mjs reads each
plugins/<name>/plugin.toml directly from the checked-out tree and emits
a sorted flat array (name, version?, description?, needs?) at
plugins-index.json. The CI workflow regenerates and commits this file
on every push under plugins/, then pokes the worker's
/api/registry/refresh — which now fetches the single committed
plugins-index.json (1 subrequest), validates the JSON shape, and
re-signs it with Ed25519. Refresh cost is now constant in registry
size, not linear.
The dashboard's dict-shaped /api/registry payload is unchanged — that
still rebuilds via the daily 02:00 UTC cron.
Without this, dashboard / daemon see content changes only after the
next 02:00 UTC cron tick (up to ~24h delay). The action POSTs to
stats.librefang.ai/api/registry/refresh with a bearer token shared
with the worker secret of the same name; until both secrets exist on
their respective sides, the endpoint returns 503 and the action fails
loud — no silent half-deploy.
Filters on directories the worker actually reads (plugins/, agents/,
skills/, hands/, channels/, providers/, workflows/, mcp/) so README
edits don't burn worker invocations.
Known limitation: the worker rebuild walks ~40+ GitHub Contents API
subrequests, which on Workers Free truncates partway through and
leaves some categories empty. This action fires the right path; the
underlying budget fix (Workers Paid, or pre-building the index in
this repo) is tracked separately.
* chore: prune deprecated models across providers
Remove old-generation models that are strictly superseded by current versions
on the same provider/family. Affected providers: anthropic, bedrock, vertex-ai,
xai, moonshot, zhipu, baichuan, stepfun, volcengine, minimax, cohere, together,
fireworks, deepinfra, openrouter. Also clean up orphan aliases (grok3, grok-mini,
minimax-m2.1) and remap moonshot alias to kimi-k2.5.
Net: -42 model entries across 18 files. Provider model counts and README rows
updated accordingly.
* chore: remove redundant and orphan aliases from aliases.toml
Provider TOML files auto-register their model.aliases at load time, so
re-declaring them globally is duplication. Also drop entries pointing to
models that no longer exist after the prune.
- 45 redundant entries duplicating provider-defined aliases
- 11 orphan targets (gpt-4o, gpt-4o-mini, grok-2-mini, grok-3,
mixtral-8x7b-32768, copilot/gpt-4, open-mistral-nemo,
pixtral-large-latest, jamba-1.5-large, palmyra-x5, venice-uncensored)
Net: 100 lines down to 21. The file is now what the header comment
always claimed it was: 'additional global aliases not tied to a specific
model entry.'
* chore: second pass — prune more deprecated models
Apply the same 'strictly superseded by same-provider/family successor'
rule to providers missed in the first pass:
- openai: gpt-4.1 / -mini / -nano, o3, o4-mini (5)
- meta-llama: llama-3.3-70b-instruct (1)
- zhipu: glm-4v-plus (1)
- together: Llama-3.3-70B-Instruct-Turbo (1)
- xiaomi: mimo-v2-flash / -omni / -pro (3)
- aion-labs: aion-1.0 / -mini (2)
- qianfan: ernie-speed-128k, ernie-4.0-turbo-8k (2)
- cerebras: cerebras/llama3.1-8b (1)
- qwen-code: qwen-code/qwq-32b (1)
- nvidia-nim: 12 models (llama-3.1/3.2 series, mixtral-8x22b,
mistral-small-3.1, phi-4-mini, qwq-32b, r1-distill-32b,
qwen2.5-coder, nemotron-mini-4b, nemotron-70b-instruct)
- openrouter: meta-llama/llama-3.3-70b (paid), rekaai/reka-edge (2)
- alibaba-coding-plan: qwen3.5-plus, qwen3-max-2026-01-23,
MiniMax-M2.5, kimi-k2.5 (4)
Net: -35 model entries. providers/README.md model counts updated.
220 models remain.
* fix: add shell execution rules to collector hand system prompt
* fix: use latest taplo instead of hardcoded version
* fix: use uncenter/setup-taplo action with latest version
- Replace tier "free" with "fast" (valid tiers: frontier/smart/balanced/fast/local)
- Remove version suffix from teams-mcp integration id field
- Update sync-pricing.py to not generate invalid tier values
* fix: pin npm package versions in MCP integration templates
Prevent supply chain attacks by pinning exact versions instead of
using unpinned `npx -y @package` which pulls latest on every run.
23 of 25 integrations pinned. sqlite-mcp and aws skipped (packages
not found on npm registry).
* fix: use stable azure/mcp version instead of beta
* feat: add pricing sync script and update model prices from OpenRouter API
- scripts/sync-pricing.py fetches real-time pricing from OpenRouter
- Updated 64 price fields across 13 provider files
- Run periodically or in CI to keep prices current
* feat: add 4 context engine plugins
- topic-memory: keyword clustering for topic-aware memory recall
- episodic-memory: conversation segmentation and cross-session recall
- user-profile: persistent user profiling from conversation patterns
- context-decay: time-based memory decay with reinforcement dynamics
All plugins use the ingest/after_turn hook protocol with stdin/stdout JSON.
* chore: add plugin scaffolding, update docs and templates
- Add plugin.toml template with {{NAME}} placeholder
- Add new-plugin Makefile target with hooks/ scaffolding
- Update plugins/README.md with all 10 plugins
- Update README.md stats (10 plugins, 220+ models)
- Add Plugin checkbox and checklist to PR template
- Add Plugin to issue template content type dropdown
- Fix CONTRIBUTING.md: last_verified is recommended, not required
* fix: correct model pricing and remove deprecated entries
- openrouter/gemma-2-9b-it: fix pricing from 0.0 to 0.03/0.09 per M tokens
(free variant correctly stays at 0.0)
- github-copilot: remove deprecated copilot/gpt-4 model entry
(GPT-4 retired in favor of GPT-4o for Copilot)
* docs: annotate kimi-coding as membership-gated
Kimi Code CLI uses quota-based membership model (not per-token billing).
Free tier has limited weekly requests; underlying model is K2.5.
Pricing kept at 0.0 consistent with other subscription providers
(chatgpt, github-copilot) but with explanatory comments.
* style: fix trailing newline in github-copilot.toml
* fix: correct Moonshot/Kimi model pricing from official sources
All 5 models had incorrect pricing:
- moonshot-v1-8k: 0.10/0.10 → 0.20/2.00
- moonshot-v1-32k: 0.30/0.30 → 1.00/3.00
- moonshot-v1-128k: 0.80/0.80 → 2.00/5.00
- kimi-k2: 2.00/8.00 → 0.60/2.50
- kimi-k2.5: 2.00/8.00 → 0.45/2.20
Sources: platform.moonshot.ai/docs/pricing/chat, costgoat.com, getmaxim.ai
* feat: add MiniMax M2.7 and M2.7-highspeed models
Released 2026-03-18, MiniMax's latest flagship text model.
10B activated params, 200K context, 128K output, tool use, streaming.
Pricing: $0.30/$1.20 per M tokens (input/output).
Added to both international (minimax.io) and China (minimaxi.com) providers.
Community-maintained TOML catalog for LibreFang. New models can be added
via PR without requiring a LibreFang binary release.
Includes validation script, bilingual docs, and GitHub templates.
Evan Hu