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 skills add ashfulcra/fulcra-tools --skill fulcra-content-reviewgit clone --depth 1 https://github.com/ashfulcra/fulcra-toolsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ashfulcra/fulcra-tools/fulcra-content-review)<a href="https://agentmods.dev/skills/ashfulcra/fulcra-tools/fulcra-content-review"><img src="https://agentmods.dev/badge/skills/ashfulcra/fulcra-tools/fulcra-content-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ashfulcra/fulcra-tools/fulcra-content-review"><img src="https://agentmods.dev/badge/skills/ashfulcra/fulcra-tools/fulcra-content-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00046 | $0.02409 |
| Opus 5 | $0.00023 | $0.01205 |
| Sonnet 5 | $0.00009 | $0.00482 |
| Haiku 4.5 | $0.00005 | $0.00241 |
Grade A, and why
fulcra-content-review scanned grade A with 0 findings 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 3d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fulcra Content Review
For content a human will put their name on. Code review checks that a thing works; content review checks that a thing persuades, in the owner's voice, without overclaiming. Every rule here was paid for in one real editing session — the provenance notes say where.
0. Decide point of view FIRST
Whose voice is this? Decide before any line edit — POV drives everything else. A document that says "we" (the agents), describes its owner in third person, and credits itself in the colophon is incoherent the moment the owner shares it as theirs. If the owner's voice: first person, owner's diction, agents described as tools they used. (Provenance: the Modest Proposal draft failed exactly this way.)
Pronoun-referent audit. Once POV is chosen, extract every "we/us/our" sentence programmatically and check each referent against the choice. One document, one "we" — a draft that uses "we" for the authors in one sentence and for the company being addressed in the next reads as sloppy to exactly the reader it's trying to persuade. Same failure class as everything else on this list: two names for one thing, and the text trusts the wrong one. (Provenance: v3 of the same draft shipped 8 referent slips; the owner caught them in the first few sentences.)
1. Claims discipline — before style, always
- A single measurement is not a rate. Label it: "first live wake: 55 s", never a bare "55 s" in a stat block. One data point presented as steady-state is the most common quiet overclaim.
- Necessary vs sufficient. When a test proves half a capability, say which half. "A scheduled run reached its tools" does not show it will act on instructions it finds elsewhere — design the second test instead of rounding up. (Provenance: the ChatGPT scheduled-task tests — the owner caught the overclaim, and the honest second test then passed cleanly.)
- Unforgeable evidence beats self-report. Verify from the system of record (a change feed, a timestamped upload), not the actor's account of itself. Put a token in the test that exists only on the path under test — and call it read-evidence, not "unguessable"; a short hex nonce proves the file was read, it is not cryptography.
- Preserve every leg of the evidence. Result files prove the actor did the work; only the preserved instructions prove the work wasn't smuggled in with them. Archive the prompt/charter verbatim next to the result, and state preconditions a skeptic would ask about (e.g. a one-time human pre-approval that let the run act unattended). (Provenance: second-round review caught the missing prompt artifact after the test itself had already passed.)
- Numbers are measured or labeled estimates. If the colophon claims "every number was measured," make that true.
- Don't infer intent from your own experience of the surface. "We didn't have to change it" does not mean "it wasn't built for this" — ease of use is usually evidence of deliberate design, not of accident. Claims about why a product is the way it is belong to its owner; check before printing one. (Provenance: a draft claimed the platform "wasn't built for agents" because the fleet rode it unmodified; the owner's correction was that the agent-facing surfaces are heavily worked precisely so models find them easy.)
- Enumerate, don't name-match, when auditing claims. Checking a document for "the claims I remember making" has unbounded false negatives — walk every number, every stat block, every "we proved" systematically. (Same failure class as the credential sweep: four corrections because the search was scoped by a name the searcher chose.)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago Changed · -5 lines · -27 tokens per session de9f936d2391
- 12d ago First seen · 187 lines · 73 tokens per session scan A 03418c673acb
fulcra-content-review is a skill published in the GitHub repository ashfulcra/fulcra-tools (10 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 2,409 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
magic-compact
Compact this Claude Code session.
citation-intelligence
Use when the user wants to know which URLs AI engines cite for a query, whether their domain is being cited by ChatGPT/Claude/Perplexity/Gemini/Google AI Overviews/Bing, what queries their site is cited for, how citation rate changes over time, or how their citation coverage compares to competitors. Self-hosted, BYO…
proof-engine
Create formal, verifiable proofs of claims with machine-checkable reasoning. Use when asked to prove, verify, fact-check, or rigorously establish whether a claim is true or false — mathematical, empirical, or mixed. Trigger phrases: "is it really true", "can you prove", "verify this", "fact-check this", "prove it"…
join
Join the Cortex agent team — reads agent config from team directory, generates .cortex.md protocol, updates CLAUDE.local.md. Idempotent — safe to re-run as a sync.
setup
First-time Cortex setup — creates team directory, config, and chief of staff agent.
leave
Leave the Cortex agent team — removes .cortex.md, cleans CLAUDE.local.md, sets agent status to inactive in team directory.