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/ssbun/csl-agent-kit/task-target-alignment-evalnpx skills add SSBun/csl-agent-kit --skill task-target-alignment-evalgit clone --depth 1 https://github.com/SSBun/csl-agent-kitWhat 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 | $0.00040 | $0.00830 |
| Opus 5 | $0.00020 | $0.00415 |
| Sonnet 5 | $0.00008 | $0.00166 |
| Haiku 4.5 | $0.00004 | $0.00083 |
Grade A, and why
task-target-alignment-eval 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Target Alignment Eval
Operate this project-local suite without shared Skill distribution.
Workspace Boundary
- Resolve the workspace with
git rev-parse --show-toplevel. - Read
<workspace>/evals/README.md,<workspace>/evals/task-target-alignment/README.md, and the authoritative protocol at<workspace>/skills/meta/csl-tasks/shared/protocols/task-target-alignment.md. - Treat
<workspace>/evals/as canonical. The.agents/skills/task-target-alignment-evalentry is discovery-only and must remain a relative symlink to this package. - Never copy this Skill into
<workspace>/skills/, a global Skill directory, package manifests, or installer enumeration.
Workflow
- Run
node <workspace>/evals/scripts/check-project-evals.js, then run the evaluator'svalidatecommand before relying on the suite. - For fixture work, keep stable ASCII case IDs, versioned schemas, provisional oracle labels, two-variant contrast scenarios,
allowedDecisions, risk, commitment-difference dimensions, reason requirements, and Safety Overlay. KeepgateModereport-only until human adjudication. - Keep deterministic validation and scoring separate from model execution. Use
prepareto create oracle-free request JSONL andscoreorcompareonly with observed prediction/report files; never fabricate eval results. - For Pi model runs, use one parent-owned async
workflowScriptwith the exact model under test and fresh-context evaluator children. For the 64-case baseline, split oracle-free packets into 16 unrelated batches of four and run three fresh repeats (48 children), using waves when the effective spawn or concurrency cap is lower. Randomize batch order; never let one child process the entire corpus or expose gold labels. - Save generated predictions and reports only under the suite's ignored
results/directory unless the user explicitly approves a durable artifact. - Report under-guard, over-guard, L2 checkpoint, L3/L4 mismatch, Safety Overlay, reason completeness, transition, family, and stability metrics separately; never hide opposing regressions behind one aggregate score or present a provisional report as release approval.
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 First seen · 56 lines · 40 tokens per session scan A 1a3d1acd75a9
task-target-alignment-eval is a skill published in the GitHub repository SSBun/csl-agent-kit (10 stars, last pushed 4d ago), licensed MIT. It adds 40 tokens to every session and 830 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.
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