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.
npx agentmods add skills/baokhang83/fluencyloop/plannpx skills add baokhang83/fluencyloop --skill plangit clone --depth 1 https://github.com/baokhang83/fluencyloopWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00127 | $0.04474 |
| Opus 5 | $0.00063 | $0.02237 |
| Sonnet 5 | $0.00025 | $0.00895 |
| Haiku 4.5 | $0.00013 | $0.00447 |
Grade A, and why
plan scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Strips warnings and disclaimerslowAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
Planning is also teaching: the same "teach the why, ask whether it is understood, don't lecture" Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/fluencyloop:plan — map a big chunk before you build it
Sits upstream of /fluencyloop:feature. A feature is one branch; a plan is an
initiative that will spawn several features. You will: (1) frame the chunk, (2) analyse
requirements and surface material gaps, (3) design and show the overall architecture, (4) break
it into task items, (5) sequence them into a roadmap with a critical path, (6) offer to open
GitHub tickets under a milestone, (7) hand each task off to /fluencyloop:feature. The plan is a
map you build against, not a spec to ratify —
do not over-invest. Keep the developer the architect.
Bundled CLI (Claude Code)
Before invoking a deterministic command, use this plugin's bundled launcher:
"${CLAUDE_PLUGIN_ROOT}/bin/fluencyloop" <arguments>. Every fluencyloop … command below
means that exact Bash-tool command; it is never a chat instruction or a globally installed
command.
Do not hand-scaffold .fluencyloop/, .claude/skills/, designs, sessions, state, or helper
scripts. The bundled CLI creates the deterministic files and returns their paths.
Local site — open once
Before the first user-visible response, invoke
"${CLAUDE_PLUGIN_ROOT}/bin/fluencyloop" site --ensure --open-once --json. This ensures the
reader for every workflow entry, but opens a browser tab only once while that managed reader is
alive. If it reports running: true and no earlier assistant message in this session starts with
FluencyLoop site:, say FluencyLoop site: <url> (opened in browser). once, using its returned
URL. Do not mention an unavailable site or repeat the announcement.
Generated prose — ASD-STE100
Write generated user-facing technical prose in ASD-STE100 style: use short, direct sentences, active voice, one main action per sentence, and stable, unambiguous terms. Preserve product names, code identifiers, CLI commands, field names, and exact recorded values. Do not claim formal ASD-STE100 compliance: that requires checking the official controlled dictionary and rules.
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.
- 2d ago First seen · 321 lines · 127 tokens per session scan A 0997a53970f6
plan is a skill published in the GitHub repository baokhang83/fluencyloop (44 stars, last pushed 16d ago), licensed Apache-2.0. It adds 127 tokens to every session and 4,474 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
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openspec-verify-change
Verify implementation matches change artifacts. Use when the user wants to validate that implementation is complete, correct, and coherent before archiving.
openspec-new-change
Start a new OpenSpec change using the experimental artifact workflow. Use when the user wants to create a new feature, fix, or modification with a structured step-by-step approach.
agent-harness-fault-injection
Use when an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.
ce-optimize
Run metric-driven optimization loops. Use when improving a measurable outcome through experiments.
ce-pov
Give a decisive, project-grounded point of view: a graded verdict on an external-adoption question, a holistic take on a document, or a position on a supplied approach set. Use for a solo POV. Use when asked to consult other models, reconcile their opinions, or oracle. Not for findings review (use ce-doc-review)…