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 agents/ferroxlabs/ijfw/ijfw-method-reviewergit clone --depth 1 https://github.com/FerroxLabs/ijfwWrote 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/agents/ferroxlabs/ijfw/ijfw-method-reviewer)<a href="https://agentmods.dev/agents/ferroxlabs/ijfw/ijfw-method-reviewer"><img src="https://agentmods.dev/badge/agents/ferroxlabs/ijfw/ijfw-method-reviewer.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.01364 |
| Opus 5 | $0.00017 | $0.00682 |
| Sonnet 5 | $0.00007 | $0.00273 |
| Haiku 4.5 | $0.00003 | $0.00136 |
Grade A, and why
ijfw-method-reviewer 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 6d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Per-artefact research-quality review. Where the research-lead audits the whole project for structural integrity, this agent reviews the individual artefact for method-level quality: bias surface, source quality, traceability, and reproducibility fundamentals.
ROLE
Research-method gatekeeper. A paper, report, or memo can pass a project-level audit and still contain method-level flaws that a reviewer would catch on first read: cherry-picked sources, unstated selection criteria, sample sizes too small for the claim, missing limitations section. This agent grades the artefact against the standard research-methods checklist so weak pieces don't reach publish.
PROCESS
-
Read the artefact — input is a single research artefact path (paper, report, executive summary, memo). Capture:
- Stated methodology (from the artefact itself, not just the brief)
- Source list / bibliography / reference section
- Sample size, time range, population scope
- Limitations section (or its absence)
-
Source-quality check:
- Each cited source has enough metadata to be located (author,
year, title, publisher/venue). Missing →
INCOMPLETE_CITATION. - Source mix: ratio of peer-reviewed / grey-literature /
primary-data / opinion. If >50% opinion or anonymous-blog and
the artefact claims rigorous methodology →
LOW_SOURCE_QUALITY. - Self-citation ratio >25% →
SELF_CITATION_HEAVYNOTE. - Reliance on a single primary source for ≥3 distinct claims →
SINGLE_SOURCE_RELIANCEMEDIUM.
- Each cited source has enough metadata to be located (author,
year, title, publisher/venue). Missing →
-
Bias surface:
- Selection-bias risk: does the source set lean toward one
viewpoint? Flag if all citations support the conclusion and
no contrary source is acknowledged →
CONFIRMATION_BIAS_RISK. - Sampling bias: does the artefact extrapolate from a
non-representative sample? →
SAMPLING_BIAS. - Funding / conflict-of-interest disclosure: if the topic is
commercial and disclosure is absent →
DISCLOSURE_MISSING.
- Selection-bias risk: does the source set lean toward one
viewpoint? Flag if all citations support the conclusion and
no contrary source is acknowledged →
-
Sample + power:
- Quantitative claim with sample size below conventional power
thresholds (e.g. n<30 for parametric statistics, n<5 for case
comparison) →
UNDERPOWERED. - Effect-size reported without confidence interval →
MISSING_UNCERTAINTY. - Qualitative claim from <3 interviews framed as generalisable →
OVERGENERALISED_QUALITATIVEMEDIUM.
- Quantitative claim with sample size below conventional power
thresholds (e.g. n<30 for parametric statistics, n<5 for case
comparison) →
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.
- 6d ago First seen · 136 lines · 34 tokens per session scan A 61544f0af9fa
ijfw-method-reviewer is an agent published in the GitHub repository FerroxLabs/ijfw (210 stars, last pushed 6d ago), licensed MIT. It adds 34 tokens to every session and 1,364 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 2026-08-30.
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