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/deepklarity/harness-kit/fable-auditnpx skills add deepklarity/harness-kit --skill fable-auditgit clone --depth 1 https://github.com/deepklarity/harness-kitWrote 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/deepklarity/harness-kit/fable-audit)<a href="https://agentmods.dev/skills/deepklarity/harness-kit/fable-audit"><img src="https://agentmods.dev/badge/skills/deepklarity/harness-kit/fable-audit.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 | $0.00086 | $0.00593 |
| Opus 5 | $0.00043 | $0.00296 |
| Sonnet 5 | $0.00017 | $0.00119 |
| Haiku 4.5 | $0.00009 | $0.00059 |
Grade A, and why
fable-audit 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 5d 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.
What it actually says
Fable roadmap auditor
Trust nothing; verify everything. Lean by user directive: an audit is one short report plus two file updates — not a documentation pass.
- Verify claims with proof. For each task DONE since the last audit, run its
verify step or inspect the merged artifact. Delegate command runs and file
checks to parallel haiku subagents; keep judgment in the main conversation.
The board is truth (
board_overview.py/spec_trace.py/task_inspect.py --brieffromtaskit/taskit-backend/); doc-vs-board divergence is a finding. Downgrade anything unprovable. - Recompute what has data: run the suites; count autonomy/unsticks from board history (hand-counted until the metrics script lands — say so). Report gaps as gaps.
- Grade against
SCORECARD.md— every score move names the anchor (0/5/10) its evidence supports; recompute the overall (plain average). Scores live only in thefable_roadmap.mdtable — a score anywhere else is a finding. Then judge the ladder againstfable_roadmap.mdexit criteria; check the ratchet rules there. Violations are findings. - Write
audits/YYYY-MM-DD*.md: verdict line (on track / drifting / blocked + why), verified-done table with proof, demotions, metrics snapshot, top-3 risks with mitigations, ladder call. Readable in 30 seconds from the top. - Update the living docs: RESUME "Current state" + BACKLOG re-rank (the top IS the next wave draft) + the ladder Status column if a level changed.
- Artifact only if the user asks — redeploy to the URL in RESUME's durable facts; never mint a new one.
--dispatchonly: convert the backlog top into a wave spec underbootstrap/and load it (pre-dispatch checklist in the loader templates).
End with: verdict line, ladder level, top 3 next actions — plain prose.
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.
- 5d ago First seen · 38 lines · 86 tokens per session scan A 40f89ddb6532
fable-audit is a skill published in the GitHub repository deepklarity/harness-kit (95 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 593 once invoked, about $0.0004 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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