historical-precedent-finder — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited historical-precedent-finder (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
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Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Identifies historical precedents relevant to a current news event or policy debate, provides a structured comparison showing why each precedent is or is not analogous, and lists the sources a journalist should consult to verify and deepen each comparison.
Required: A clear description of the current event, policy, or situation you need precedents for — including the specific aspect you want to compare (e.g., "the economic impact of a rapid technology transition," not just "AI").
Optional: Any historical precedents already being cited in media coverage (helps the assistant assess their accuracy and offer alternatives); the time period or geography you want precedents from; the audience (general reader, specialist, editorial team); whether you want analogies that support or challenge the dominant narrative.
700–1,000 words. Two main sections: Precedents (2–4 precedents, each with description, shared features, differences, analogy strength, and sources to consult) and Misleading Analogies (1–2 commonly cited comparisons that do not hold up, with explanation). Tone: analytical, measured, written for a journalist who wants to use historical context responsibly. No sensationalism or forced dramatic parallels.
Current situation: A national government is considering a large-scale programme to retrain workers displaced by automation in the logistics and warehousing sector. The programme would be funded by a levy on companies deploying automated systems. Aspect to compare: The design and effectiveness of government-funded retraining programmes for workers displaced by technology at scale Precedents already in media: "This is like the coal miners in the 1980s" (frequently cited); "This is like the Luddites" (occasionally cited) Audience: General news readership
Structural Features of the Current Situation
Before identifying precedents, the key structural characteristics are:
Precedents
1. Trade Adjustment Assistance (TAA) programmes — United States, 1962–present
Description: The US government created TAA to retrain and support workers who lost jobs due to increased foreign trade, particularly after trade liberalisation agreements. The programme has been expanded and reformed multiple times over six decades.
Shared features: Government-funded retraining for workers displaced by an economic structural shift; targeted at specific sectors and communities; intended to ease the political and social costs of a policy the government was simultaneously pursuing.
Differences: TAA was reactive (workers had already lost jobs), not proactive. Funding came from general revenue, not from an industry levy. The displacement was caused by trade, not technology — the retraining challenge is different because trade-displaced workers often had transferable manufacturing skills, while automation-displaced warehouse workers may face a more fundamental skills gap.
Analogy strength: Partial. The programme design lessons are highly relevant — decades of evaluation data show what works and what does not in large-scale retraining. But the proactive framing and industry-levy funding mechanism have no direct parallel in TAA.
Sources to consult: Congressional Research Service reports on TAA effectiveness; academic evaluations by Hyman (2018) and Autor, Dorn, and Hanson (2013) on trade-displaced workers; Government Accountability Office audits of TAA outcomes.
2. Regional development programmes in response to coal industry decline — United Kingdom, 1980s–2000s
Description: After rapid coal mine closures in the 1980s, the UK government and EU structural funds invested in retraining and regional economic development for affected communities over two decades.
Shared features: A concentrated workforce in a specific sector facing elimination of their primary employment; government-funded retraining and economic diversification; affected communities were geographically concentrated and economically dependent on the industry.
Differences: Coal closures were driven by political decisions and market forces, not automation. The workforce was highly geographically concentrated (mining towns), whereas logistics workers are more geographically dispersed. The government that closed the mines had limited political incentive to fund generous retraining — the current scenario, where the government is designing the programme before displacement, suggests different political dynamics.
Analogy strength: Partial. The long-term outcomes are instructive — many former mining communities experienced decades of economic decline despite retraining investment, suggesting that retraining alone is insufficient without broader economic development. But the political context and geographic concentration make this analogy less direct than it appears.
Sources to consult: Beatty and Fothergill (2020) on long-term outcomes in former coalfield communities; National Audit Office reports on coalfield regeneration programmes; local authority data on employment and deprivation indices in former mining areas.
3. Singapore's SkillsFuture programme — 2015–present
Description: A national programme providing all adult citizens with credits for skills development, combined with sector-specific training partnerships between government and industry. Designed as a proactive response to anticipated automation and economic restructuring.
Shared features: Proactive government intervention before mass displacement; industry co-funding and co-design; focused on preparing a workforce for a changing economy rather than rescuing workers already displaced.
Differences: SkillsFuture is economy-wide, not sector-specific. Singapore's small geographic size, strong state capacity, and different labour market structure (heavy reliance on foreign workers in some sectors) make direct comparison difficult. The political and institutional context is fundamentally different from a large democratic country.
Analogy strength: Strong for programme design, weak for political context. The design principles — proactive timing, industry co-funding, individual training accounts — are directly relevant. But the implementation challenges will be vastly different in a larger, more politically fragmented country.
Sources to consult: Singapore Ministry of Education SkillsFuture evaluations; OECD (2020) reviews of Singapore's adult learning system; academic assessments of SkillsFuture uptake and outcomes by sector.
Misleading Analogies
"This is like the coal miners in the 1980s" The surface similarity is compelling — workers in a specific sector losing jobs — but the analogy obscures more than it reveals. Coal closures were the result of political confrontation and market forces, not a gradual automation process. The government of the time had adversarial rather than supportive intentions toward the affected workforce. Most importantly, the current proposal is proactive (before displacement), while the coal response was reactive (after closures). Using the coal analogy risks implying that failure is inevitable, which is not supported by the structural comparison.
"This is like the Luddites" This analogy is essentially useless for serious analysis. The Luddite movement was a labour protest against specific machinery in the early 19th-century textile industry. It tells us nothing about the design of government retraining programmes, the effectiveness of industry levies, or the labour market conditions facing modern warehouse workers. Citing the Luddites is a way of signalling familiarity with the concept of technology displacement, not a way of understanding it.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.